Targeting epigenetic methylation: emerging diagnosis and therapeutic strategies in cancer.

Introduction

Cancer poses a severe global public health challenge, ranking as the second leading cause of death and accounting for approximately one in six deaths worldwide [ 1 ]. The difficulty of early detection, coupled with the inherent proliferative and drug-resistant properties of tumor cells, contributes to persistently high cancer incidence and mortality rates. Despite significant advances in traditional treatments (surgery, radiotherapy, chemotherapy, and hormone therapy) [ 2 , 3 ] and the transformative impact of immunotherapy and targeted therapies [ 1 , 3 ], mortality rates remain unchanged for many patient populations. Consequently, there is an urgent need to develop innovative therapies or combination strategies to achieve long-term survival benefits for patients.

Epigenetic modifications refer to stable nucleic acid modifications that do not alter the underlying DNA sequence [ 4 ]. As a key chemical modification, methylation directly shapes cellular characteristics and guides differentiation by regulating gene expression and maintaining genomic stability [ 5 ]. Human understanding of methylation began with the discovery of 5-methylcytosine (5mC) in 1925 [ 6 ]. Subsequent decades of research identified major forms of RNA methylation [ 7 ] and histone methyltransferases [ 8 ]. In 1983, scientists first demonstrated the close association between DNA methylation and cancer, revealing that cancer genomes exhibit hypomethylation compared to normal cells [ 9 , 10 ]. Subsequently, abnormal histone modifications [ 11 ] and RNA methylation [ 12 ] were also confirmed to contribute to tumorigenesis. Abnormal epigenetic alterations permeate the entire course of cancer development. Reports indicate that over 300 genes undergo epigenetic modifications across various cancers [ 13 , 14 ], with these alterations closely linked to cellular proliferation, developmental abnormalities, invasion and metastasis, and treatment response [ 15 ]. Methylation-associated proteins serve as key regulators in tumor pathology, exhibiting both oncogenic and tumor-suppressing functions depending on the specific tumor context.

Given that methylation profoundly influences the entire process of cancer progression, investigating its biological functions and identifying key target genes are crucial for elucidating cancer pathogenesis. Screening and therapeutic strategies targeting methylation hold promise for improving patient outcomes [ 5 ]. This review focuses on the relevance of methylation to cancer detection, treatment, drug resistance, and prognostic assessment, outlining its current clinical applications and future prospects to provide scientific evidence for clinical decision-making.

Methylation and cancer

Methylation is a biochemical process wherein methyl groups are catalytically transferred from an active methyl compound to other macromolecules or chemicals, thereby forming methylated proteins and nucleic acids [ 16 ]. In biological systems, proteins involved in the methylation process include methyltransferases (‘writers’), demethylases (‘erasers’), and proteins with methylation-dependent binding activity (‘readers’). These proteins collaborate to shape various biological processes, including the regulation of gene expression, the control of protein function, and RNA processing [ 16 ]. As an essential nucleic acid and protein modification, methylation can suppress gene expression and is closely linked to the occurrence and development of diseases such as aging, Alzheimer’s disease, cancer, and necroptosis making it one of the most extensively studied epigenetic modifications currently [ 17 – 21 ]. DNA methylation, RNA methylation, and histone methylation are the most common types of methylation in biological systems [ 20 ]. Fig. 1 Main types and mechanisms of methylation. A DNA methylation. The main methylation sites are continuous extensive CpG islands via DNA methyltransferases (DNMTs) and DNA demethylation (Ten-eleven translocation [TET] dioxygenases and thymine DNA glycosylase [TDG] are responsible for); Unmethylated and hemimethylated sites are also affected. In addition, 6-methyladenosine (m 6 A) has also been found to be methylated. B RNA methylation. Major sites contain more targetable nucleotides (A, G, C, or U) and methylated products (Cm, Gm, and Um), which stand for 2′-O methylations of indicated ribose. C Histone methylation. The main methylation sites are in the amino-terminal tails of histones H3 and H4, as well as the associated methyltransferase (blue), demethylase (orange), and reader (green)

DNA methylation

DNA methylation refers to the process catalyzed by methyltransferases such as DNMT1, DNMT3A, DNMT3B, and DNMT3L, wherein a methyl group (-CH3) is added to the 5′ position of cytosine within CpG islands (primarily located in transcriptional start sites or promoter regions). This leads to the formation of 5mC residues and significantly impacts tumor onset and development. Specifically, DNA hypomethylation can promote the transcriptional activation of oncogenes, whereas DNA hypermethylation can inhibit the expression of tumor suppressor genes [ 17 , 22 , 23 ]. These epigenetic changes can alter genomic stability, thereby serving as a cancer-related risk factor. TET dioxygenase and thymine DNA glycosylase mediate DNA demethylation and may also contribute to oncogenic progression [ 24 , 25 ]. Although the mechanistic processes surrounding m6A methylation are still poorly understood, decreases in genomic m6A levels have been shown to promote tumor development in humans [ 26 ]. To date, three classes of DNA methylation readers have been established, including members of the methyl-CpG-binding domain (MBD), Kaiso, and set ring finger-associated (SRA) families [ 5 ] (Fig. 1 ). For further details, see reference [ 27 ].

Many tumor-related genes are characterized by hypermethylation or hypomethylation of CpG islands in their promoter regions, which respectively lead to the inhibition or enhancement of gene expression. DNA hypermethylation is a common feature of tumor suppressor genes such as CDKH2A, CDH1, CDH13, and APC in colorectal cancer (CRC), prostate cancer, lung cancer, and breast cancer [ 28 ]; CDKN2A in leukemia [ 29 ]; RASSF1 in breast, prostate, lung, hematological, and CRC [ 28 ]; SET9 and PAX1 in cervical cancer [ 30 , 31 ]; and BRCA1 in ovarian and breast cancer [ 32 , 33 ]. Conversely, hypomethylation of normally inactive regions of the DNA can support tumor development. For instance, in breast and ovarian cancers, for example, Sat2 and Satα DNA hypomethylation are associated with disease progression and poorer prognosis [ 34 – 36 ]. DNA hypomethylation can also lead to the re-expression of human papillomavirus (HPV) and Epstein-Barr virus (EBV), thereby triggering the onset or progression of lymphoma and cervical cancer [ 37 , 38 ]. Similar effects have also been observed for MAGE and ALKBH1 in CRC [ 39 , 40 ], MAGE in melanoma [ 41 ], MUC1 in ovarian cancer [ 42 ], and SNCG in gastric, ovarian, and breast cancers [ 43 , 44 ]. Tumorigenesis may also stem from changes in the regulation of DNA methylation or the hypermethylation of one copy of a particular tumor suppressor gene while the other copy harbors a mutation or genomic loss [ 45 , 46 ].

RNA methylation

RNA methylation refers to the chemical modification of RNA species (mainly tRNA and rRNA, the rest including mRNA, microRNA [miRNA], and lncRNA, etc.) at particular bases (m6A, N 1 -methyladenosine [m 1 A], 5-methylcytidine (m 5 C), N 3 -methylcytosine [m 3 C], N 7 -methylguanosine [m 7 G], 2′-O-methylation [2′-O-Me], pseudouridine [Ψ], and adenosine-to-inosine [A-to-I]) by methyltransferases such as METTL3, METTL14, and METTL16. Specific demethylases, including ALKBH5, FTO, IGF2BP1, and YTHDF, can demethylate these RNA species, reversing the effects of such modification [ 47 – 51 ]. RNA modifications can shape the expression of particular genes and the differentiation of specific cell populations by altering target genes and inducing oncogenic signaling pathway activity, functioning in a manner that supports or impairs the tumor cell development, migration, metabolic activity, invasion, stem cell-like activity, and therapeutic resistance [ 48 , 49 , 52 – 54 ] (Fig. 1 ). RNA-related species play a key role in the development of cancer, and an in-depth investigation of their mechanisms is of great significance for understanding RNA methylation, cancer diagnosis, and cancer treatment. miRNAs play fundamental regulatory roles in shaping cell activity, including development, differentiation, proliferation, apoptosis, and genome stability, through specific base pairing with target transcripts and/or protein molecules, as well as transcriptional and post-transcriptional regulation of their expression [ 55 , 56 ].

miRNAs can contribute directly to tumorigenesis in different tissues by acting as tumor suppressors (such as miR-15a and miR-16-1) or oncogenes (such as clusters miR-17-92 and miR-155) [ 57 , 58 ]. Additionally, miRNAs are considered markers of genetic susceptibility to cancer [ 59 , 60 ]. In addition to their effects on cancer cell function, miRNAs can also modulate the function of other cells in the tumor microenvironment. Extracellular miRNAs can influence intercellular communication and cooperate with stromal cells and extracellular matrix components to create a suitable microenvironment for cancer cell growth and escape immune response [ 61 ]. LncRNAs have proven to be involved in every hallmark of cancer cells, from their intrinsic ability to proliferate and survive, to increased metabolism, to their relationship with the tumor microenvironment. Early evidence that lncRNAs in cancer comes from their transcriptional regulation by key carcinogenic or tumor-suppressing transcription factors (such as p53, MYC, estrogen receptor) or signaling cascades (such as the Notch pathway) [ 62 – 67 ]. These lncRNAs contribute to the functional output of carcinogenic or tumor suppressor responses. Carcinogenic lncRNAs include BOK-AS, CDKN2B-AS1, GAS5, HIF1A, HOTAIR, lincRNA-p21, LSINCT5, P21-AS, PTCSC3, TUG1, and UCA1 [ 68 ]. Tumor suppressor lncRNAs include BGL3, DILC, DLEU1, DLEU2, GAS5, MEG3, NBAT-1, and TERRA, etc. New lncRNAs are still being discovered [ 69 ]. Abnormal expression, mutations, and single-nucleotide polymorphisms (SNPs) of lncRNAs are associated with tumorigenesis and metastasis [ 70 ]. There are relatively few studies on the relevant mechanisms. For example, SNP rs11672691 interacts with HNRNPAB through PCAT19-long to activate, thereby promoting the growth and metastasis of prostate cancer [ 71 ]; SNP rs12517403 increases the risk of cervical cancer by reducing SP1 binding and lowering CARMN expression [ 72 ]; The occurrence of SNP rs7463708 mutation can more easily regulate the upregulation of PCAT1 expression and promote the proliferation of prostate cancer cells [ 73 ]; SNP rs6695584 mutation activation activates the expression of lncSLCC1, thereby driving the glycolysis pathway and accelerating the growth of rectal cancer tumor cells [ 74 ]; The SNP rs11655237 mutation leads to the reduction of LINC00673 (G), which inhibits the proliferation process of pancreatic cancer cells [ 75 ]. Identifying shared genetic mechanisms has brought new opportunities for the development of therapeutic targets and diagnostic tools. Future research should focus on functional validation and the investigation of environmental interactions to fully leverage these findings to advance cancer management. A more comprehensive overview of RNA methylation has been published previously [ 76 ].

The precise functions of the downstream targets of RNA methylation ultimately determine whether it plays a protective or counterproductive role in the context of tumor development and progression. Among them, the m6A modification mediated by METTL and the m5C modification mediated by NSUN have received more widespread attention. For instance, overexpression of the m6A methyltransferase METTL3 is evident in breast, colon, liver, and stomach cancers, wherein it can suppress SOCS2 or let-7 g expression to support proliferative tumor growth [ 77 – 80 ]. METTL3-mediated m6A modification upregulates NRXN3, promoting peritoneal metastasis of CRC [ 81 ]; Upregulation of NFE2L3 regulates the WNT signaling pathway, promoting the dryness and progression of lung adenocarcinoma cells [ 82 ]; Up-regulating IGF2BP2 and activating the PIK3CA/AKT pathway promote the malignant progression of esophageal cancer [ 83 ]; Upregulation of LINC00857 enhances the stemness and metastasis of ovarian cancer cells by activating the YAP-TEAD pathway [ 84 ]; Up-regulating TMOD4 promotes the growth and metastasis of liver cancer [ 85 ]. NSUN2 is an m 5 C methyltransferase with overexpression in breast and colorectal cancers that may function by suppressing miR-125b to inhibit GAB2 and other factors to support migratory and proliferative growth [ 86 – 88 ]. NSUN2-mediated m 5 C modification upregulates TRIM28 and promotes the progression of prostate cancer [ 89 ]; Up-regulating PKM2 promotes glycolysis and progression of hepatocellular carcinoma (HCC) [ 90 ]; Upregulation of KDM6B promotes bone metastasis of breast cancer [ 91 ]; Upregulation of SREBP2 promotes the progression of esophageal squamous cell carcinoma [ 92 ]; Up-regulating PGK1 activates the PI3K/AKT pathway, promoting the growth and invasion of gastric cancer [ 93 ]. Furthermore, the tRNA m 1 A methyltransferase TRMT6/TRMT61A/TRMT10C can promote HCC development through its ability to induce the PPARδ and c-Myc proto-oncogenes, similarly enhancing ovarian and cervical tumor growth [ 94 , 95 ]. A-to-I RNA editing in glioblastoma can promote ADAR1 and ADAR3 upregulation, in turn leading to higher levels of GM2A expression and a reduction of the expression of GRIA2, which ultimately promotes migratory, invasive, and proliferative growth, while ADAR2 downregulation yields the opposite effect [ 96 – 98 ].

Histone methylation

The methylation of histones is essential for the control of transcriptional activity and DNA replication. Methylation is a fundamental regulatory mechanism that controls transcription and replication. Histone methyltransferases (HMTs) catalyze this process, including histone lysine methyltransferases and protein/histone arginine methyltransferases (PRMTs). They transfer the methyl group from S-adenosine methionine (SAM) to histones, yielding methylated histones and S-adenosine homocysteine (SAH). The removal of these methyl residues is mediated by HDMs, including members of the LSD (LSD1/2) and JmjC (JHDM1/2/3, JARID1, JMJD, PHF8, UTX/UTY, PHF8) families [ 99 ].

These epigenetic modifications predominantly occur on lysine and arginine residues of histone 3 and histone 4 (H3/H4), although other modifications are observed at lower levels. Arginine residues can be either monomethylated or demethylated. PRMTs can simultaneously transfer two methyl groups to the same nitrogen atom at the end of an arginine polypeptide to produce asymmetric methylarginine, or add one methyl group to each end of the nitrogen atom to generate symmetric dimethylarginine. In contrast, lysine residues can be methylated once, twice, or three times [ 20 , 100 ]. Lysine and arginine methylation reader proteins include members of the ‘royal’ domain superfamily (Tudor, chromo, MBT, and PWWP) with plant homeodomain (PHD) zinc finger domains or Tudor and PHD zinc finger domains, respectively [ 5 ]. Abnormally regulated HMT and HDM activity or expression can lead to genome-wide shifts in histone methylation, which may impact tumor suppressor or oncogene expression, potentially contributing to tumor progression [ 20 , 100 ] (Fig. 1 ). For a more in-depth discussion of histone methylation, see references [ 101 , 102 ].

The ability of histone methylation to suppress or enhance gene expression depends on both the location and magnitude of such methylation. Specific functional studies have been performed on many different histone residues (including H3K4/H3K9/H3K17/H3K27/H3K36/H3K56/H3K79/H4K5/H4K20/H3R2/H3R8/H3R17/H3R26/H4R3). For instance, reduced levels of EZH2 can reduce H3K27me3 levels to induce the expression of miR-139-5p, ultimately protecting against pancreatic cancer onset. KRAS can reverse the effects of EZH2 and increase NFATc1 expression through reductions in H3K27me3 levels and concomitant increases in H3K4me3 levels, favoring pancreatic oncogenic progression [ 103 , 104 ]. The absence of KMT3A can trigger significantly reduced H3K36me3 levels, thereby upregulating disheveled segment polarity protein 22, in turn enhancing Wnt/β-catenin signaling activity to support CRC development [ 105 ]. PRMT1 can catalyze the asymmetric demethylation of H4K3 to upregulate genes associated with the Notch pathway, thereby promoting esophageal cancer onset and progression [ 106 ]. The upregulation of G9a (also referred to as EHMT2) can trigger enhanced H3K9 and H3K27 methylation, leading to reduced expression of E-cadherin and epithelial-mesenchymal transition activity in PANC-1 pancreatic cancer cells [ 107 , 108 ].

Methylation in the detection and diagnosis of cancer

Many cancers only present with symptoms following primary tumor metastasis, emphasizing the need to develop early screening strategies that can accurately detect and diagnose specific malignancies before they progress. Research focused on the mechanisms governing tumorigenesis has largely focused on measurable biomarkers detectable in biopsy samples (tissues or shed cells), systemic circulation (plasma or whole blood), secreted/excreted samples (urine, sputum, or stool), and associated lavage fluids [ 109 ]. Liquid biopsy strategies are increasingly becoming popular noninvasive approaches to cancer screening. One promising liquid biopsy strategy focuses on the analysis of cell-free DNA (cfDNA) in the systemic circulation, which includes circulating tumor-derived DNA (ctDNA), which may aid in the early detection and diagnosis of cancer [ 110 ]. Necrotic or apoptotic tumor cells can release ctDNA, which is also actively secreted in some instances, providing detailed information regarding the genomic makeup of both primary and secondary tumors [ 111 ]. Currently, unrestricted methylation-based approaches are the most widely utilized screening and early diagnostic strategies, as the abnormal methylation patterns that arise during early stages of tumorigenesis are generally tissue- and cancer-type-specific [ 112 , 113 ]. Patterns of methylation are often highly prevalent across tumor tissue and found over large regions of the genome, in contrast with somatic mutations that are only present in particular tumor clones [ 113 , 114 ]. Increasingly robust standardized approaches to capturing cfDNA in blood and other biofluids have enhanced the feasibility of analyzing DNA methylation levels for important locations in the genome (Fig. 2 ). Analyzing methylation status can provide an effective means of detecting and diagnosing tumors early during their development. Methylation-based research efforts can be broadly classified into genome-wide methylomics and site-specific methylation analyses [ 100 ]. In addition, exosomes are also promising detection modes. Exosomes are extracellular vesicles secreted into body fluids by living cells. Exosomes from tumor tissues contain DNA, RNA, proteins, lipids, and other substances that can reflect tumor information and provide basic analysis for tumor diagnosis, genetic analysis, and epigenetic analysis [ 115 ]. The advantage of exosome liquid biopsy is that it provides more comprehensive tumor information than ctDNA, and the tumor information contained in exosomes is more stable and not easily degraded [ 116 ]. However, the early application of exosomes in liquid biopsy for cancer diagnosis is a preliminary exploration stage, such as exosome DNA and RNA mutations, transcriptome analysis, and exosome protein analysis [ 115 ]. Current studies have found that double-stranded DNA in tumor-derived exosomes represents the entire genome and reflects the mutated state of tumors in melanoma [ 117 ], and exosome miRNA-10b-5p and exosome miRNA-21 have high diagnostic sensitivity and are independent risk factors for predicting poor prognosis of liver cancer [ 118 , 119 ]. More experiments are needed to verify their effectiveness and determine the target of a comprehensive analysis. Fig. 2 The relationship between ctDNA and methylation in patients with cancer and methylation detection techniques. ctDNA are obtained from the patient’s tissue/shed cells), blood (whole blood or plasma), secretions/excreta (stool, urine, or sputum), and corresponding lavage fluid and analyzed to identify various DNA changes in the tumor (including gene mutations, gene fusions, copy number variations, and methylation). Further, modern research techniques are used to analyze methylation to screen and identify cancer, among which the cutting-edge research techniques for studying methylation can be divided into two categories (genome-wide methylomics and site-specific methylation detection)

DNA methylation is the most widely used epigenetic marker for the detection and diagnosis of cancer, in part because it persists well under typical experimental conditions and under routine storage, in addition to being common throughout the human genome (Fig. 2 ). The stability of DNA methylation, cancer-specific patterns of methylation, and the accessibility of cfDNA make it a particularly promising target for screening and diagnostic applications. In a screening capacity, while false negative or false positive results can occur, analyses of DNA methylation can afford greater specificity and sensitivity through the simultaneous analysis of multiple targets. Certain DNA methylation-related epigenetic biomarkers have been clinically implemented to date, including PAX1 in oral and cervical cancer, GSTP1 in prostate cancer, Twist1 in bladder cancer, ONECUT2 in urothelial carcinoma, SHOX2 in lung cancer, RNF180 in gastric cancer, and Septin9 in CRC. Over 30 diagnostic kits on the market have received approval from the Food and Drug Administration (FDA), Communate Europpene (CE), and National Medical Products Administration (NMPA) [ 120 – 146 ] (Table 1 ). Table 1 Approved methylation kits/biomarkers for cancer diagnosis/detection with performances Cancer Gene target Sample type Sensitivity, % Specificity, % References Bladder cancer Twist1 Urine 88.20 86.80 [ 112 ] Bladder cancer ZNF154 Urine 84.00 96.00 [ 113 ] Bladder cancer OTX1/ONECUT2/TWIST1/FGFR3 mut /TERT mut /HRAS Urine 93.00 86.00 [ 114 ] Bladder cancer 15 proprietary markers (Bladder EpiCheck) Urine 68.20 88.00 [ 115 ] Cervical cancer PAX1 Exfoliated cell 100 84.70 [ 116 ] Cervical cancer PCDHGB7 Exfoliated cell 96.00 94.30 [ 117 ] Cervical cancer PAX1/ZNF582 Exfoliated cell 88.24 91.89 [ 118 ] Cervical cancer PAX1/JAM3 Exfoliated cell 74.10 95.90 [ 119 ] Cervical cancer FAM19A4/hsa-mir124-2 Brush self 73.30 66.20 [ 120 ] Cervical cancer POU4F3/positive hrHPV test Exfoliated cell 67.50 82.30 [ 121 ] Cervical cancer ASTN1/DLX1/ITGA4/RXFP3/SOX17/ZNF671 Exfoliated cell 85.00 85.40 [ 122 ] Colorectal cancer SDC2 Faeces 90.20 90.20 [ 123 ] Colorectal cancer SDC2/TFPI2 Faeces 96.40 96.60 [ 124 ] Colorectal cancer SDC2/SFRP2 Faeces 83.81 93.94 [ 125 ] Colorectal cancer SDC2/NPY/FGF5/PDX1 Faeces 91.17 91.92 [ 113 ] Colorectal cancer SDC2/ADHFE1/PPP2R5C Faeces 84.80 98.00 [ 126 ] Colorectal cancer BMP3/NDRG4/FIT/KRAS mut Faeces 91.94 87.08 [ 113 ] Colorectal cancer Septin9 Blood 48.20 91.50 [ 127 ] Colorectal cancer Septin9/SDC2/BCAT1 Blood 83.70 93.90 [ 128 ] Colorectal cancer Septin9/BCAT1/IKZF1/BCAN/VAV3 Blood 87.3 91.10 [ 113 ] Lung cancer SHOX2/RASSF1A BALF 88.24 81.25 [ 129 ] Lung cancer SHOX2/PTGER4 BALF 67.00 90.00 [ 130 ] Lung cancer SHOX2/RASSF1A/PTGER4 Blood 87.00 98.00 [ 131 ] Lung cancer 6 proprietary markers (Lung EpiCheck) Blood 84.30 77.70 [ 132 ] Gastric cancer RNF180/Septin9 Blood 62.20 84.80 [ 133 ] Gastric cancer Reprimo/SDC2/TCF4 Blood 93.39 80.33 [ 134 ] Liver cancer mSEPT9 Blood 91.00 81.00 [ 135 ] Liver cancer BMPR1A/PLAC8 Blood 95.42 96.02 [ 113 ] Prostate cancer GSTP1/RASSF1/APC Tissue 78.00 60.00 [ 136 ] Prostate cancer GSTP1/SFRP2/IGFBP3/IGFBP7/APC/PTGS2 Urine 73.00 76.00 [ 137 ] Oral cancer PAX1/ZNF582 Exfoliated cell 87.00 86.00 [ 138 ] Urothelial carcinoma ONECUT2/VIM Urine 89.74 92.46 [ 112 ] BALF bronchoalveolar lavage fluid

DNA methylation is a key determinant of transcriptional activity and is closely tied to the process of oncogenesis. Therefore, studies of the methylation status of particular genes are quite common, revealing that changes in DNA methylation can be leveraged to detect tumor development independent of age, sex, lesion location, or other factors. These diagnostic approaches offer the advantages of being sensitive, specific, noninvasive, easy to implement, and compatible with early detection, such that they are more convenient than MRI- or CT-based screening protocols.

Methylation and the treatment of cancer

Aberrant epigenetic modifications and regulatory activity can profoundly shape the behaviors of tumor cells and the surrounding tumor microenvironment (TME), highlighting the promising potential of targeting these factors in the context of cancer therapy. Cellular reprogramming involves irregular epigenomic changes that modulate cellular signaling pathways and tumor-associated gene expression, thereby creating conditions conducive to tumor growth and metastasis [ 147 ]. Given that writer, reader, and eraser proteins are the key mediators of epigenetic changes, the development of novel drugs targeting these proteins represents a promising avenue for cancer treatment, which is currently being actively investigated in numerous preclinical and clinical trials. Below, we provide a discussion of epigenetic inhibitory compounds that have already undergone clinical testing for their ability to treat cancer.

DNA methylation inhibitors

DNMT inhibitors represent the most extensively developed class of epigenetic inhibitory drugs to date, having attracted substantial interest and undergone testing in several clinical trials. The nucleoside analogs 5-azacytidine (ZCyt, Azacytidine), 5-aza-2′-deoxycytidines (ZdCyd), and 5-fluoro-2′-deoxycytidine (FdC, Decitabine) can be incorporated into RNA and DNA, thereby interfering with DNA, RNA, and protein biosynthesis [ 148 ]. When cells are treated with these compounds at higher doses, toxic compounds are generated that disrupt de novo thymidylate synthesis and lead to increased cytotoxicity [ 149 ]. These compounds are also capable of irreversibly binding to and inactivating DNMT (5mC), yielding a mutagenic or toxic complex [ 149 ]. These drugs have received FDA approval to treat myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML), achieving respective complete response rates as high as 54% and 100%. Furthermore, favorable effects have also been reported when these drugs are used at low doses in patients with chronic myeloid leukemia (CML) and hemoglobinopathy [ 150 ].

The high susceptibility of these inhibitors to deaminase-mediated degradation led to the development of guadecitabine (SG-110), which consists of decitabine linked to deoxyguanosine via a phosphodiester bond [ 151 ]. The resultant compound possesses a distinct dinucleotide structure that protects against drug removal through deamination of decitabine, thereby affording superior biostability. When used for treatment, the gradual release of the active metabolite of guadecitabine, decitabine, can initiate associated inhibitory processes [ 152 ]. This drug has received approval as a first-line salvage therapy for AML and MDS, although randomized controlled trials have reported that it affords similar survival benefits to those associated with azacytidine and decitabine in randomized controlled trials [ 153 ]. While the single-agent efficacy of DNMT inhibitors in hematological malignancies can be quite high, their efficacy in solid tumors remains very limited. This discrepancy is attributable to the distinct features of solid tumors, including metabolic instability, poor cellular nutritional status, and limited binding to cellular DNA. The development of modified cytidine analogs with enhanced stability or the combination of drugs with other therapeutic agents may provide an opportunity to achieve improved efficacy while avoiding the toxic side effects associated with high-dose cytidine analog administration (common adverse events include bone marrow suppression, gastrointestinal reactions, fatigue, weakness, and allergic reactions) [ 154 , 155 ]. Consequently, numerous studies have explored innovative DNMT inhibitors or combination treatment strategies (Table 2 ). Table 2 Single targeting methylations used in cancer Inhibitor target Sensitizer Cancer Phase, Years Clinical trial number DNA methyltransferases DNMT inhibitor fluoro-2-deoxycytidine (FdC)/Tetrahydrouridine Advanced solid tumours II, 2006 NCT00359606 DNMT inhibitor Azacitidine (ZCyt) Acute myeloid leukemia and myelodysplastic syndrome II, 2008 NCT00666497 DNMT inhibitor 5-fluoro-2-deoxycytidine/Tetrahydrouridine Lung, breast, bladder, and head and neck cancer II, 2009 NCT00978250 DNMT inhibitor 5-fluoro-2-deoxycytidine/Tetrahydrouridine Advanced solid tumours II, 2012 NCT01534598 DNMT inhibitor 4′-thio-2′-deoxycytidine Advanced solid tumours I, 2015 NCT02423057 DNMT inhibitor Guadecitabine (SG-110)/DLI Acute myeloid leukemia II, 2016 NCT02684162 DNMT inhibitor 5-aza-4′-thio-2′-deoxycytidine Advanced solid tumours I, 2017 NCT03366116 DNMT inhibitor Decitabine (5-aza-2′-deoxycytidine [ZdCyd]) Acute myeloid leukemia with complex or monosomal karyotype II, 2017 NCT03080766 DNMT inhibitor Guadecitabine Paraganglioma, gastrointestinal stromal tumours, phaeochromocytoma, hereditary leiomyomatosis, and renal cell carcinoma II, 2017 NCT03165721 DNMT inhibitor Guadecitabine Acute myeloid leukemia III, 2017 NCT02920008 DNMT inhibitor Decitabine BAP1-associated malignancies II, 2023 NCT05960773 RNA methyltransferases FTO inhibitor Meclofenamate Advanced solid tumours with brain metastasis NA, 2015 NCT02429570 FTO inhibitor Bisantrene Acute myeloid leukemia II, 2019 NCT03820908 METTL3 inhibitor STC-15 Advanced solid tumours I, 2022 NCT05584111 Histone methyltransferases (lysine demethylasear/ginine methyltransferase/H3K27 methyltransferase/H3K79 methyltransferase/H3K36 methyltransferase) LSD1 inhibitor Seclidemstat (SP-2577) Advanced solid tumours I, 2019 NCT03895684 LSD1 inhibitor INCB059872 Ewing sarcoma I, 2018 NCT03514407 LSD1 inhibitor INCB059872 Acute myeloid leukemia, small cell lung cancer, myelofibrosis, Ewing sarcoma and poorly differentiated neuroendocrine tumours I/II, 2016 NCT02712905 LSD1 inhibitor Bomedemstat (IMG-7289/MK-3543)/ATRA Acute myeloid leukemia I, 2016 NCT02842827 LSD1 inhibitor Pulrodemstat Celgene (CC-90011) Non-Hodgkin lymphoma and advanced solid tumours I, 2016 NCT02875223 LSD1 inhibitor Phenelzine Prostate cancer II, 2014 NCT02217709 LSD1 inhibitor GSK2879552/ATRA Acute myeloid leukemia I, 2014 NCT02177812 LSD1 inhibitor GSK2879552 Small cell lung cancer 1, 2013 NCT02034123 LSD1-HDAC6 inhibitor JBI-802 Advanced solid tumours I/II, 2022 NCT05268666 PRMT5 inhibitor GSK3326595 Early stage breast cancer II, 2020 NCT04676516 PRMT5 inhibitor AZD3470 MTAP deletion advanced solid tumours I/IIa, 2023 NCT06130553 PRMT5 inhibitor AZD3470 Haematologic malignancies I/II, 2023 NCT06137144 PRMT5 inhibitor SCR-6920 Advanced solid tumours I, 2022 NCT05528055 PRMT5 inhibitor PF-06939999 (PRMT5-IN-3) Non-small cell lung cancer, head and neck squamous cell cancer, esophageal cancer, endometrial cancer, cervical cancer, and bladder cancer I, 2019 NCT03854227 PRMT5 inhibitor TNG462 MTAP deletion advanced solid tumours I/II, 2023 NCT05732831 PRMT5 inhibitor TNG908 MTAP deletion advanced solid tumours I/II, 2022 NCT05275478 PRMT5 inhibitor PRT543 B-cell non-Hodgkin lymphoma and advanced solid tumours I, 2019 NCT03886831 PRMT5 inhibitor PRT811 Advanced solid tumors, central nervous system lymphoma, and recurrent high-grade gliomas I, 2019 NCT04089449 PRMT5 inhibitor JNJ-64619178 B-cell non-Hodgkin lymphoma and advanced solid tumours I, 2018 NCT03573310 PRMT5 inhibitor AMG 193 MTAP deletion advanced thoracic tumors Ib, 2024 NCT06333951 PRMT5-MTA inhibitor MRTX1719 MTAP deletion advanced solid tumours I/II, 2022 NCT05245500 EZH2 inhibitor Tazemetostat (EPZ-6438) B-cell lymphoma with EZH2 mutation II, 2018 NCT03456726 EZH2 inhibitor Tazemetostat Malignant mesothelioma with BAP1 mutant II, 2016 NCT02860286 EZH2 inhibitor Tazemetostat Follicular lymphoma with EZH2 mutation II, 2022 NCT05467943 EZH2 inhibitor Tazemetostat Follicular lymphoma with EZH2 mutation Observational, 2022 NCT05228158 EZH2 inhibitor Tazemetostat Follicular lymphoma II, 2023 NCT06068881 EZH2 inhibitor Tazemetostat Follicular lymphoma Ib/III, 2019 NCT04224493 EZH2 inhibitor Tazemetostat Metastatic malignant peripheral nerve sheath tumors II, 2021 NCT04917042 EZH2 inhibitor Tazemetostat INI1-negative tumors and synovial sarcoma II, 2015 NCT02601950 EZH2 inhibitor Tazemetostat INI1-negative tumors and synovial sarcoma I, 2015 NCT02601937 EZH2 inhibitor Tazemetostat Diffuse large B-cell lymphoma and advanced solid tumours I/II, 2013 NCT01897571 EZH2 inhibitor Tazemetostat Advanced solid tumours Expanded Access, 2019 NCT03874455 EZH2 inhibitor Tazemetostat Epithelioid Sarcoma Expanded Access, 2020 NCT04225429 EZH2 inhibitor Tazemetostat Advanced solid tumors, non-hodgkin lymphoma, and histiocytic disorders With EZH2, SMARCB1, or SMARCA4 mutations II, 2017 NCT03213665 EZH2 inhibitor Tazemetostat B-cell lymphoma and advanced solid tumours I, 2016 NCT03010982 EZH2 inhibitor CPI-1205 B-cell lymphoma II, 2015 NCT02395601 EZH2 inhibitor CPI-0209 Advanced solid tumors and lymphomas I/II, 2019 NCT04104776 EZH2 inhibitor AXT-1003 Non-hodgkin lymphomas I, 2023 NCT05965505 EZH2 inhibitor SHR2554 Mature lymphoid neoplasms I, 2018 NCT03603951 EZH2 inhibitor PF-06821497 Small cell lung cancer,castration-resistant prostate cancer, and follicular lymphoma I, 2018 NCT03460977 EZH2 inhibitor GSK2816126 Diffuse large B-Cell lymphoma, transformed follicular lymphoma, non-Hodgkin’s lymphomas, solid tumors, and multiple myeloma I, 2014 NCT02082977 EZH2-1 inhibitor Valemetostat Tosylate (DS-3201b) Peripheral T-cell lymphoma II, 2021 NCT04703192 EZH2-1 inhibitor Valemetostat Tosylate T-cell leukemia/lymphoma II, 2019 NCT04102150 EZH2-1 inhibitor Valemetostat Tosylate B-cell lymphoma II, 2021 NCT04842877 EZH2-1 inhibitor HH2853 Non-hodgkin’s lymphomas and advanced solid tumors I/II, 2020 NCT04390737 EED inhibitor MAK683 Diffuse large B-cell lymphoma, nasopharyngeal carcinoma, and other advanced solid tumors I/II, 2016 NCT02900651 EED inhibitor ORIC-944 Metastatic prostate cancer I/Ib, 2022 NCT05413421 EED inhibitor APG-5918 Nasopharyngeal carcinoma, castrate-resistant prostate cancer, gastric cancer, ovarian clear cell carcinoma, non-hodgkin lymphomama, B-cell lymphoma, mesothelioma sarcoma, and epithelioid sarcoma I, 2022 NCT05415098 DOT1L inhibitor Pinometostat (EPZ-5676) Paediatric MLL-rearranged leukemias Ib, 2014 NCT02141828 DOT1L inhibitor Pinometostat Advanced hematologic malignancies with 11q23-rearrangement I, 2012 NCT01684150 NSD2 inhibitor KTX-1001 Multiple myeloma I, 2022 NCT05651932 SETD2 inhibitor EZM0414 Multiple myeloma and diffuse large B-Cell lymphoma I/Ib, 2021 NCT05121103 Trial information is taken from ClinicalTrials. gov DNMT DNA methyltransferase, BAP1 BRCA1-associated protein-1, DLI donor lymphocyte infusion, FTO fat mass and obesity, METTL3 methyltransferase like 3, LSD1 Lysine-specific demethylase 1, ATRT all-trans retinoic acid, PRMT5 protein arginine methyltransferase 5, MTA methylthioadenosine, MTAP methylthioadenosine phosphorylase, EZH1/2 enhancer of zeste homolog 1/2, EED embryonic ectoderm development, DOT1L histone 3 lysine 79 methyltransferase, NSD 2 nuclear receptor-binding SET domain-containing 2, SETD 2 SET domain containing 2

Histone methylation inhibitors

Similar to DNA, histones are subject to extensive epigenetic modification at many residues. Methylation and acetylation are the predominant classes of histone modification [ 156 ], and histone regulatory proteins are among the most commonly dysregulated proteins in many cancers, providing a wealth of therapeutic targets, many of which can impact histone methylation. These targets include specific histone lysine methyltransferases (EZH1/2 and H3K79 methyltransferases), PRMT5, LSD1, and less common targets such as SETD2, NSD2, and EED (Table 2 ). Among these targets, inhibitors of EZH proteins are the most extensively studied [ 157 , 158 ].

EZH is a subunit of polycomb repressor complex 2 (PRC2) that inhibits Hox gene expression [ 159 , 160 ]. Proteins in the polycomb group (PcG) serve as suppressors of transcriptional activity through their ability to regulate chromatin remodeling [ 161 ]. PRC2 can promote chromatin compression and inhibit transcription through the catalysis of H3K27 methylation [ 162 ]. H3K27 can undergo acetylation (H3K27ac) or mono-, di-, or trimethylation (H3K27me1/2/3), with PcG activity being characterized primarily by the H3K27me2 and H3K27me3 modification of gene promoters. H3K27me1 and H3K27ac are generally believed to facilitate transcription [ 163 ]. PRC2 is comprised of four subunits, including EZH1/2, EED, SUZ12, and RbAp46/48, with the first three of these proteins functioning to mediate the inhibition of H2K27 methylation-inducing genes, while also shaping the onset of a range of diseases through the effects of particular cofactors (AEBP2, C10or12, C17orf96, JARID2, PCL1-3, and RBBP4/7) [ 164 ]. In animal model studies, the loss of EZH2 has been demonstrated to impair development and lead to death, with deceased mice exhibiting normal EZH1 expression, thus supporting a vital role for EZH2 in the epigenetic functions of PRC2 [ 165 , 166 ]. However, some reports have demonstrated that EZH1 may also function as a weak epigenetic regulator when low levels of EZH2 expression are evident [ 167 ]. EZH2 is also an important regulator of the DNA damage repair and cell cycle progression [ 168 , 169 ].

The EZH2 protein, comprising over 700 amino acids, is encoded by a gene on chromosome 7q35-36 [ 170 ]. The final protein consists of multiple domains, including the HMT-activating SET domain [ 170 ]. In the context of oncogenesis, the effects of EZH proteins can be both PRC-dependent and PRC-independent. For instance, H2AK-119ub1, H3K27me3, and PRC1 can together form a complex that disrupts chromatin remodeling and inhibits the transcription of downstream genes, leading to their inactivation of silencing. The acceleration of ribosomal activity and translational regulation, as well as the phosphorylation of particular residues (such as Y705 of STAT3), can also promote the development of tumors, including prostate cancer, gastric cancer, and glioblastoma [ 170 , 171 ]. During the chromatin remodeling process, EZH2 is involved in a regulatory relationship with the SWI/SNF polyprotein complex, with high EZH2 activity levels inhibiting PRC2-regulated target gene expression in stem/progenitor cells [ 172 , 173 ]. This finding has been reported in many tumor types, including endometrial, ovarian, and malignant rhabdomyoid tumors [ 174 , 175 ]. Conversely, low levels of EZH2 activity can lead to SWI/SNF activation and PRC2 target gene upregulation, contributing to cellular differentiation and dormancy [ 172 , 173 ]. EZH1/2 can stably interact with p85β and USP7 or ERβ components within the nucleus to support H3K27me3 modification and global transcriptional expression in a manner conducive to CRC and breast cancer development [ 176 , 177 ]. Major EZH inhibitors currently under investigation include 3-deazaneplanocin A (DZNep), which is an SAH hydrolase inhibitor that functions by upregulating SAH and suppressing SAM-dependent HMT activity, ultimately suppressing the growth and progression of tumors [ 178 ]. In 2020, the FDA approved tazemetostat (EPZ-6438) for the treatment of patients with epithelioid sarcoma and follicular lymphoma, and it has reportedly achieved objective response rates of 15–69% and a progression-free survival interval of 5.5–13 months [ 179 , 180 ].

Low molecular weight ligands capable of binding to H3K27me3 pockets of EED have also been reported to influence cancer-related outcomes. As a WD40-containing protein, EED can bind to H3K27me3 residues via an aromatic cage known as an H3K27me3 pocket, leading to an allosteric increase in PRC2 activity that can propagate H3K27 methylation [ 181 , 182 ]. Inhibitors of EED can suppress the methyltransferase activity of PRC2 methyltransferase containing EZH1/EZH2, thereby suppressing the proliferation of tumor cell lines harboring mutations in EZH2, including lines resistant to SAM-competitive EZH2 inhibitors [ 183 ]. EED inhibitors can also trigger rapid proteasomal degradation of EED, EZH2, and SUZ12, thereby selectively inhibiting the proliferation of PRC2-dependent tumor cells [ 184 , 185 ]. Thus, EED inhibitors such as MAK683 may be promising anticancer agents.

To date, over 9 human PRMTs have been identified that are capable of catalyzing methyl group transfer from SAM to the arginine residues of target substrate proteins [ 186 ]. These PRMTs are broadly classified into the type I (asymmetric dimethylarginine, ADMA), type II (symmetric dimethylarginine, SDMA), and type III (monomethylarginine) categories [ 186 ]. As a type II arginine methyltransferase, PRMT5 can bind to MEP50, forming a stable functional complex that supports cancer cell proliferation [ 187 , 188 ]. The loss of the 2-methylthioadenosine metabolism regulator methylthioadenosine phosphorylase (MTAP) in somatic cells can disrupt SDMA functionality, reversing the proliferation of cancer cells [ 187 ]. The highly conserved HMT DOT1L catalyzes H3K79 methylation to regulate the activation of transcription and elongation [ 189 ]. In patients harboring MLL-rearrangement (MLL-r), fusion proteins lead to the aberrant chromatin localization of DOT1L-containing multi-subunit complexes, resulting in ectopic H3K79 methylation and enhanced expression of leukemia-related genes [ 190 , 191 ]. Pinometostat is a DOT1L inhibitor that can selectively suppress H3K79 methylation, suppressing the transcriptional activation of MLL-r target genes, including MEIS1 and HOXA9, to alter tumor cell development [ 192 ].

Lysine demethylases are classified based on their sequences and mechanistic functions into the LSD and JmjC-containing lysine demethylase subfamilies [ 193 ]. LSD1 and LSD2 are members of the larger flavin adenine dinucleotide (FAD)-dependent amine oxidase superfamily, whereas JmjC lysine demethylases are FeII/2-oxoglutarate (2-OG)-dependent enzymes responsible for catalyzing mono-, di-, and trimethyllysine demethylation [ 193 ]. LSD1 utilizes FAD as a cofactor and binds to CoREST, HDAC, C-terminal binding, and nucleosome remodeling proteins to facilitate the demethylation of particular histone H3 residues, including H3K4me1/2 and H3K9me1/2, thereby activating or inhibiting particular cancer-related genes [ 194 ]. Molecularly distinct inhibitors of LSD1 can simultaneously interact with multiple regions of this complex, thereby synergistically inhibiting the transcriptional activation of tumor suppressor genes through combination treatment [ 195 ]. A limited number of NSD-2 and SETD-2 inhibitors, which act on H3K36, have also been discovered and have made their way into clinical trials.

Inhibitors of RNA methylation

To date, there has only been relatively limited success in the development and clinical application of RNA methylation inhibitors. FTO and METTL3 inhibitors are the only small molecules that have been widely utilized in research settings. FTO is a non-heme FeII/α-ketoglutaric acid (α-KG)-dependent dioxygenase AlkB protein family member responsible for the in vitro oxidation and demethylation of m3T and m3U in single-stranded DNA and RNA sequences [ 196 , 197 ]. The AlkB family proteins ABH1, ABH2, and ABH3 can reportedly oxidize demethylated N-methylated DNA/RNA clips [ 198 , 199 ]. FTO is also capable of removing m6A residues from nuclear RNA and mRNA transcripts via α-KG and Fe II-dependent mechanisms [ 200 ]. Consequently, inhibitors of these processes have shown promise as antitumor agents in preclinical settings. Bisantrene is an FTO inhibitor that has recently been evaluated in clinical trials for the treatment of hematologic malignancies, potentially due to suboptimal efficacy outcomes in solid tumors [ 201 – 203 ]. Meclofenamate, another FTO inhibitor, exhibited significant clinical efficacy when used to treat MGMT-methylated glioblastoma or brain metastases [ 204 ]. This may be attributed to the fact that this compound is a nonsteroidal anti-inflammatory drug capable of interfering with microtubule growth, which is crucial for glioblastoma cell communication, thereby compromising the morphological and functional integrity of the cellular network in glioblastoma [ 204 ].

The 580-amino-acid METTL3 protein consists of a methyltransferase domain and a zinc finger domain, both of which are essential for its enzymatic activity [ 205 ]. MT-A70 is a key methyltransferase subunit capable of forming a methyltransferase complex (MTC) with METTL14 or other binding partners, producing a heterodimeric core component of this multi-protein complex that can guide the transcription of m6A-modified RNAs [ 206 , 207 ]. This can enable selective m6A deposition at m6A sites enriched in mRNA coding sequences and 3′-untranslated regions to influence cancer outcomes [ 208 ]. In preclinical studies, the small molecule inhibitor STM2457 of METTL3 has demonstrated effectiveness in anti-tumor treatment and synergy with other therapeutic approaches. It can suppress the growth and progression of non-small cell lung cancer (NSCLC) [ 209 ]; Inhibit the DNA repair mechanism and thereby increase cisplatin sensitivity in ovarian cancer [ 210 ]; Inhibit the CXCL5/CCL5 pathway and increase immune infiltration, thereby synergizing with PD-1 to enhance the anti-tumor effect [ 211 ]; Down-regulating MCL1 and MYC thereby increased the venetoclax-induced apoptosis of leukemia cells [ 212 ]; And inhibit the proliferation of esophageal squamous cell carcinoma by activating the DNA damage response through the ATM-Chk2 axis [ 213 ]. Another METTL3 inhibitor, STC-15, has also been advanced into clinical trials for the management of solid tumors (Table 2 ). Given the early promise of these RNA methylation-related inhibitory drugs, further efforts to develop innovative drugs and combination regimens are crucial to achieving better therapeutic efficacy in patients with cancer.

Epigenetic diet

Furthermore, the new term “epigenetic diet” refers to a class of bioactive dietary compounds, such as isothiocyanates in broccoli, genistein in soybeans, and resveratrol in red grapes, which are common components in foods [ 214 ]. These components can alter the epigenome and bring about beneficial health effects. Tumor progression can be inhibited by modulating epigenetic modification enzymes such as DNA methyltransferases and histone deacetylases, as well as certain non-coding RNAs [ 214 – 216 ]. Current research focuses mainly on preclinical studies. For example, green tea polyphenols (dietary DNMTi) plus sulforaphane (dietary histone deacetylase inhibitor) or soybean isoflavone (genistein, dietary HDACi) enable ERα reactivation to upregulate p21/KLOTHO or enhance the efficacy of the estrogen antagonist tamoxifen, thereby exhibiting potential for the treatment and prevention of ERα-negative human breast cancer [ 217 , 218 ]; Large amounts of short-chain fatty acids in dietary fiber (mainly propionic acid and butyric acid, dietary HDACi) can inhibit the expression of tumor suppressor genes such as MYC, FOS, and JUN, thereby inhibiting the proliferation of colorectal cancer cells [ 219 ]; The anthocyanin delphinidin (dietary DNMTi) activates the Nrf2-ARE pathway and can be used as a potential chemoprophylaxis agent for skin cancer [ 220 ]; Anthocyanin-rich bilberry extract induces acute lymphocyte apoptosis through redox-sensitive epigenetic modifications (DNA and histone methylation-related factors) [ 221 ]; A diet deficient in methyl groups and folic acid can significantly reduce the level of S-adenosylmethionine in the livers of male rats and mice, leading to the formation of liver cancer [ 222 ]; Curcumin alters the expression profile of miRNAs both in vivo and in vitro, and the formation of m6A is regulated by the binding of miRNAs to mRNA substrates through METTL3, thereby influencing cancer activity [ 223 ]; Cyanidin 3-glucoside upregulates the expression of lncRNA MALAT1 and nRNA tubulin γ 1, increases the level of miR-125b, and thereby regulates the cell cycle of liver cancer cells to inhibit the occurrence of liver cancer [ 224 ]; Delphinidin may inhibit the occurrence of breast cancer through the HOTAIR/miR-34a axis and exert anti-cancer effects [ 225 ]; Pomegranate polyphenolics (ellagitannins and anthocyanins) prevent colon tumor occurrence by inhibiting miR-126/VCAM-1 and miR-126/PI3K/AKT/mTOR signaling [ 226 ]. Only one clinical trial has reported that black raspberries can demethylate tumor suppressor genes and regulate other biomarkers of human colon and rectal tumor development [ 227 ]. Although epigenetic diets hold broad prospects in cancer prevention and treatment, it is still necessary to address individual differences, verify effects in long-term large-sample human clinical trials, and optimize dosage and combination regimens. In conclusion, epigenetic diets offer new insights and strategies for cancer prevention and treatment. In the future, they are expected to become an important component of precision medicine, working in synergy with traditional treatment methods to improve the survival rate and quality of life of cancer patients.

Combination therapies

Despite the development of novel therapies predicated on a refined understanding of the epigenetic mechanisms governing tumorigenesis, the heterogeneous and dynamic nature of tumors often precipitates therapeutic resistance. This resistance typically emerges from alterations in signaling pathways, changes in drug target expression, mutations, or other adaptive mechanisms [ 228 , 229 ]. Consequently, overcoming such resistance to achieve superior long-term survival thus requires a comprehensive characterization of the functional interactions between different drug combinations, thereby enabling the rational design and selection of small-molecule agents. In many prior studies, epigenetic therapies have demonstrated reciprocal sensitizing effects when combined with other forms of treatment. To date, several clinical trials have explored various combinations of epigenetic drugs with radiotherapy, chemotherapy, targeted therapy, and immunotherapy approaches (Table 3 ). Data from ClinicalTrials.gov showed that combination therapy was significantly associated with improved overall response rate in cancer patients, increasing it by more than 10%. In particular, the combination with immunotherapy has demonstrated superior survival benefits. However, many clinical trials fail, withdraw, or terminate. The primary factors contributing to these setbacks in combinatorial strategies include severe toxicity, premature progression, and early occurrence of treatment-related deaths. Table 3 Combinations targeting methylations used in cancer Inhibitor target Sensitizer Cancer Phase, Years Clinical trial number DNMT/PD1 inhibitors Guadecitabine/Nivolumab Metastatic colorectal cancer Ib/II, 2018 NCT03576963 DNMT/PD1 inhibitors Guadecitabine/Pembrolizumab Recurrent ovarian, primary peritoneal, and fallopian tube cancer II, 2016 NCT02901899 DNMT/PD1 inhibitors Guadecitabine/Pembrolizumab Non-small cell lung cancer and castrate-resistant prostate cancer I, 2017 NCT02998567 DNMT/PD1 inhibitors Decitabine/Pembrolizumab Non-small cell lung cancers, esophageal carcinomas, and pleural mesotheliomas I/II, 2017 NCT03233724 DNMT/PD1 inhibitors Decitabine/Nivolumab Non-small cell lung cancer II, 2016 NCT02664181 DNMT/PD1 inhibitors Decitabine/Nivolumab Mucosal melanoma I/II, 2016 NCT05089370 DNMT/PD1 inhibitors Decitabine/Camrelizumab Classical hodgkin lymphoma II/III, 2020 NCT04510610 DNMT/PD1 inhibitors Decitabine/Camrelizumab Hodgkin lymphoma II, 2017 NCT03250962 DNMT/PD1 inhibitors Decitabine/Camrelizumab Acute myeloid leukemia II, 2020 NCT04353479 DNMT/PD1 inhibitors Decitabin/Anti-PD1 Relapsed/refractory malignancies I/II, 2016 NCT02961101 DNMT/PD1 inhibitors Azacitidine/Pembrolizumab Non-small cell lung cancer II, 2015 NCT02546986 DNMT/PD1 inhibitors Azacitidine/Pembrolizumab Metastatic melanoma II, 2017 NCT02816021 DNMT/PDL1-TGFβ inhibitors Azacitidine/Bintrafusp Alfa Epithelial malignancies (excluding lung and renal cell carcinomas) and pulmonary metastases I/II, 2020 NCT04648826 LSD1/PD1 inhibitors Seclidemstat/Pembrolizumab Ovarian and endometrial cancer I, 2020 NCT04611139 LSD1/PD1 inhibitors INCB059872/Nivolumab Small cell lung cancer I/II, 2016 NCT02712905 LSD1/PD1 inhibitors CC-90011/Nivolumab Advanced solid tumours II, 2020 NCT04350463 PRMT5/PD1 inhibitors AMG 193/Pembrolizumab MTAP deletion advanced thoracic tumors Ib, 2024 NCT06333951 EZH2/PD1 inhibitors Tazemetostat/Pembrolizumab Advanced non-small cell lung cancer Ib/II, 2022 NCT05467748 EZH2/PD1 inhibitors Tazemetostat/Pembrolizumab Urothelial carcinoma (stage III/IV bladder Cancer AJCC v8) I/II, 2019 NCT03854474 EZH2/PD1 inhibitors XNW5004/Pembrolizumab Head and neck squamous cell carcinoma, urothelial carcinoma, prostate cancer, small-cell lung cancer, non-smallcell lung cancer, cervical cancer, and other solid tumors Ib/II, 2023 NCT06022757 EZH2-1/PD1 inhibitors Valemetostat/Pembrolizumab HPV-negative recurrent/metastatic head and neck squamous cell carcinoma Ib/II, 2023 NCT05879484 DNMT/PDL1 inhibitors Guadecitabine/Atezolizumab Acute myeloid leukemia with myelodysplasia-related changes Ib/II, 2016 NCT02935361 DNMT/PDL1 inhibitors Guadecitabine/Atezolizumab Advanced urothelial carcinoma I/II, 2017 NCT03179943 DNMT/PDL1 inhibitors Guadecitabine/Durvalumab Advanced renal cancer Ib/II, 2017 NCT03308396 DNMT/PDL1 inhibitors Guadecitabine/Durvalumab Hepatocellular carcinoma, pancreatic adenocarcinoma and cholangiocarcinoma Ib, 2018 NCT03257761 LSD1/PDL1 inhibitors Bomedemstat/Atezolizumab Extensive-stage small cell lung cancer I/II, 2021 NCT05191797 LSD1/PDL1 inhibitors Iadademstat/Atezolizumab or Durvalumab Extensive-stage small cell lung cancer II, 2024 NCT06287775 EZH2/PDL1 inhibitors Tazemetostat/Atezolizumab Diffuse large B-cell lymphoma I, 2014 NCT02220842 EZH2/PDL1 inhibitors Tazemetostat/Durvalumab Advanced solid tumors, adult solid tumor, colorectal carcinoma, soft-tissue sarcoma, and pancreatic adenocarcinoma II, 2021 NCT04705818 DNMT/PDL1 or CTLA4 Azacitidine/Durvalumab or Tremelimumab Head and neck cancer I/II, 2017 NCT03019003 DNMT/CTLA4 inhibitors Guadecitabine/Ipilimumab Metastatic melanoma I, 2015 NCT02608437 DNMT/CTLA4 inhibitors Decitabine/Ipilimumab Acute myeloid leukemia I, 2017 NCT02890329 EZH2/CTLA4 inhibitors CPI-1205/Ipilimumab Advanced solid tumours I/II, 2017 NCT03525795 EZH2-1/PD1 inhibitors Valemetostat/Ipilimumab Metastatic aggressive variant prostate, urothelial, and renal cell carcinomas I, 2020 NCT04388852 EZH2/PDL1-TGFβ inhibitors SHR2554/SHR1701 Advanced solid tumors and B-cell lymphomas I/II, 2020 NCT04407741 DNMT inhibitor/Chemotherapy Guadecitabine/Cladribine/Idarubicin Untreated adult acute myeloid leukemia II, 2014 NCT02096055 DNMT inhibitor/Chemotherapy Guadecitabine/Azacitidine/Cytarabine Acute myeloid leukemia II, 2017 NCT03164057 DNMT inhibitor/Chemotherapy Azacitidine/Lenalidomide Acute myeloid leukemia and myelodysplastic syndromes II, 2011 NCT01442714 FTO inhibitor/Chemotherapy Bisantrene/Fludarabine/Clofarabine Acute myeloid leukemia II, 2021 NCT04989335 LSD1 inhibitor/Chemotherapy Seclidemstat/Cyclophosphamide/Topotecan Ewing sarcomas I, 2018 NCT03600649 LSD1 inhibitor/Chemotherapy Bomedemstat/Venetoclax Acute myeloid leukemia I, 2022 NCT05597306 LSD1 inhibitor/Chemotherapy Tranylcypromine/cytarabine/ATRA Non-M3 Acute myeloid leukemia I/II, 2016 NCT02717884 LSD1 inhibitor/Chemotherapy Phenelzine Sulfate/Abraxane Breast cancer Ib, 2018 NCT03505528 LSD1 inhibitor/Chemotherapy Phenelzine Sulfate/Docetaxel Prostate cancer II, 2010 NCT01253642 LSD1 inhibitor/Chemotherapy Iadademstat/Paclitaxel Small cell lung cancer and extrapulmonary high grade neuroendocrine carcinomas II, 2022 NCT05420636 PRMT5 inhibitor/Chemotherapy AMG 193/Docetaxel MTAP deletion advanced solid tumours I/II, 2021 NCT05094336 PRMT5 inhibitor/Chemotherapy AMG 193/Carboplatin/Paclitaxel or Pemetrexed MTAP deletion advanced thoracic tumors Ib, 2024 NCT06333951 EZH2 inhibitor/Chemotherapy CPI-0209/Carboplatin Platinum sensitive recurrent ovarian cancer I, 2023 NCT05942300 EZH2-EZH1 inhibitor/Chemotherapy Valemetostat/Irinotecan Small cell lung cancer I/II, 2019 NCT03879798 DOT1L inhibitor/Chemotherapy Pinometostat/Daunorubicin/Cytarabine Acute myeloid leukemia with MLL rearrangement Ib/II, 2019 NCT03724084 DNMT/HDAC inhibitors Azacitidine/Vorinostat Diffuse large B-cell lymphoma I/II, 2010 NCT01120834 DNMT/HDAC inhibitors Guadecitabine/Belinostat Metastatic primary central chondrosarcoma II, 2020 NCT04340843 DNMT/LSD1 inhibitors Azacitidine/GSK2879552 Acute myeloid leukemia and myeloproliferative-neoplasms II, 2017 NCT02929498 DNMT/LSD1 inhibitors Azacitidine/Seclidemstat chronic myelomonocytic leukemia I/II, 2021 NCT04734990 DNMT/LSD1 inhibitors Azacitidine/INCB059872/ATRA Acute myeloid leukemia I/II, 2016 NCT02712905 DNMT/LSD1 inhibitors Azacitidine/Iadademstat Acute myeloid leukemia Ib, 2024 NCT06357182 DNMT/PARP inhibitors Decitabine/talazoparib Acute myeloid leukemia I/II, 2016 NCT02878785 DNMT/Tim-3 inhibitors Azacitidine/Decitabine/TQB2618 Acute myeloid leukemia and myeloproliferative-neoplasms I, 2022 NCT05426798 DNMT/DOT1L inhibitors Pinometostat and Azacitidine Acute myeloid leukemia with 11q23-rearrangement Ib/II, 2018 NCT03701295 PRMT5/KRAS G12C inhibitors AMG 193/Sotorasib MTAP deletion advanced thoracic tumors Ib, 2024 NCT06333951 EZH2-EZH1/CD20 inhibitors Valemetostat/Rituximab/Lenalidomide Follicular lymphoma I/II, 2023 NCT05683171 LSD1/FLT3-AXL inhibitors Iadademstat/Paclitaxel Acute myeloid leukemia I, 2022 NCT05546580 LSD1/BCL-2 inhibitors Iadademstat/Venetoclax Acute myeloid leukemia Ib, 2024 NCT06357182 EZH2-EZH1 inhibitor/ADC Valemetostat/Trastuzumab Deruxtecan HER2 low breast cancer Ib, 2022 NCT05633979 EZH2-EZH1 inhibitor/ADC Valemetostat Tosylate/Trastuzumab Deruxtecan Advanced solid tumours Ib, 2024 NCT06244485 DNMT inhibitor/Androgen receptor Decitabine/Enzalutamide Metastatic castrate-resistant prostate cancer Ib, 2021 NCT05037500 LSD1 inhibitor/Androgen receptor CC-90011/Abiraterone Metastatic castrate-resistant prostate cancer I, 2020 NCT04628988 EZH2 inhibitor/Androgen receptor Tazemetostat/Abiraterone or Enzalutamide Metastatic castrate-resistant prostate cancer Ib/II, 2019 NCT04179864 EZH2 inhibitor/Androgen receptor CPI-1205/Abiraterone or Enzalutamide Metastatic castrate-resistant prostate cancer Ib/II, 2018 NCT03480646 DNMT inhibitor/NEDD8-activating enzyme Azacitidine/Pevonedistat Myeloproliferative-neoplasms II, 2017 NCT03238248 EZH2-1 inhibitor/CELMoD Valemetostat/CC-99282 Non-hodgkin lymphomas I/II, 2019 NCT03930953 DNMT/PDL1/CTLA4 Azacitidine/Nivolumab/Ipilimumab Acute myeloid leukemia I/II, 2017 NCT03019003 DNMT/PDL1/CTLA4 Guadecitabine/Nivolumab/Ipilimumab Melanoma and non-small cell lung cancer II, 2020 NCT04250246 DNMT inhibitor/PD1 inhibitor/Radiotherapy Decitabine/Pembrolizumab/Hypofractionated Index Site Radiation Pediatric and young adult patients with relapsed and refractory solid tumors or lymphoma I, 2018 NCT03445858 DNMT inhibitorinhibitor/PDL1 inhibitor/Vaccine Guadecitabine/Atezolizumab/CDX-1401 Recurrent ovarian, primary peritoneal, and fallopian tube cancer II, 2017 NCT03206047 DNMT/PD1/HDAC inhibitors Decitabine/Camrelizumab/Chidamide Relapsed non-hodgkin’s lymphoma I/II, 2020 NCT04337606 DNMT/PD1/HDAC inhibitors Decitabine/Camrelizumab/Chidamide Classical hodgkin lymphoma II, 2020 NCT04233294 DNMT/PD1/HDAC inhibitors Decitabine/Camrelizumab/Chidamide Classical hodgkin lymphoma II, 2020 NCT04514081 DNMT/PD1/HDAC inhibitors Decitabine/Anti-PD1/Chidamide Classical hodgkin lymphoma II, 2024 NCT06393361 DNMT/ICIs/HDAC inhibitors Decitabine/Anti-PD1/Anti-PDL1/Anti-CTLA4/Chidamide Non-hodgkin lymphoma and advanced solid tumors I/II, 2022 NCT05320640 DNMT/PD1/HDAC inhibitors Azacitidine/Nivolumab/Entinostat Non-small cell lung cancer II, 2013 NCT01928576 DNMT/PD1/HDAC inhibitors Guadecitabine/Pembrolizumab/Mocetinostat Advanced non-small cell lung cancer I, 2017 NCT03220477 DNMT/PD1/IDO1 inhibitors Azacitidine/Pembrolizumab/Epacadostat Advanced solid tumours I/II, 2017 NCT02959437 LSD1/PD1/IDO1 inhibitors INCB059872/Pembrolizumab/Epacadostat Advanced solid tumours I/II, 2017 NCT02959437 EZH2-1/PDL1/VEGF inhibitors Valemetostat/Atezolizumab/Bevacizumab Hepatocellular Carcinoma Ib/II, 2024 NCT06294548 DNMT inhibitor/PD1 inhibitor/Chemotherapy Decitabine/Camrelizumab/Gemcitabine/Vinorelbine/Doxorubicine Primary mediastinal large B-cell lymphoma I/II, 2017 NCT03346642 DNMT inhibitor/PD1 inhibitor/Chemotherapy Decitabine/Pembrolizumab/Paclitaxel Metastatic triple-negative breast cancer I, 2023 NCT05673200 DNMT inhibitor/PD1 inhibitor/Chemotherapy Azacitidine/Visilizumab/Homoharringtonine/Cytarabine Acute myeloid leukemia III, 2020 NCT04722952 DNMT inhibitor/LSD1 inhibitor/Chemotherapy Azacitidine/CC-90011/Venetoclax Acute myeloid leukemia I/II, 2021 NCT04748848 DNMT inhibitor/FTO inhibitor/Chemotherapy Decitabine/Bisantrene/Cytarabine Acute myeloid leukemia and myelodysplastic syndromes I, 2022 NCT05456269 DNMT/PD1/Tim3 inhibitors Azacitidine/Decitabine/Spartalizumab/Sabatolimab Acute myeloid leukemia and high-risk myeloproliferative-neoplasms Ib, 2017 NCT03066648 DNMT/PD1/Tim3 inhibitors Decitabine/PDR001/MBG453 Advanced malignancies I-Ib/II, 2015 NCT02608268 DNMT inhibitor/HDAC inhibitor/CART Decitabine/Chidamide/CART 19/20 B-cell non-hodgkin’s lymphoma I/II, 2020 NCT04553393 EZH2 inhibitor/CD20 inhibitor/Chemotherapy Tazemetostat/Rituximab/Bendamustine Follicular lymphoma I/II, 2022 NCT05551936 EZH2/BRAF/MEK inhibitors Tazemetostat/Dabrafenib/Trametinib Metastatic melanoma I/II, 2020 NCT04557956 DNMT/PDL1/HDAC inhibitors/Chemotherapy Azacitidine/Durvalumab/Romidepsin/Nab-Paclitaxel Pancreatic cancer I/II, 2020 NCT04257448 Trial information is taken from ClinicalTrials. gov DNMT DNA methyltransferase, LSD1 Lysine-specific demethylase 1, PRMT5 protein arginine methyltransferase 5, MTAP methylthioadenosine phosphorylase, EZH1/2 enhancer of zeste homolog 1/2, DOT1L histone 3 lysine 79 methyltransferase, PD1 programmed death 1, PDL1 programmed cell death ligand 1, CTLA-4 cytotoxic T-lymphocyte antigen 4, TGFβ transforming growth factor-β, PARP poly adenosine diphosphate ribose polymerase, ADC antibody–drug conjugate, NEDD8 neural precursor cell expressed developmentally downregulated 8, CELMoD cereblon E3 ligase modulator, ICIs immune checkpoint inhibitors, HDAC histone deacetylase, IDO1 indoleamine 2,3-dioxygenase 1, VEGF vascular endothelial growth factor, Tim3 T cell immunoglobulin and mucin domain-3, CART chimeric antigen receptor T-cell immunotherapy, MEK methyl ethyl ketone

Immunotherapy

Epigenetic dysregulation profoundly impacts both tumor and immune cell populations within the TME, enabling tumor cells to evade immune-mediated detection and clearance [ 230 , 231 ]. In immune cells, epigenetic alterations can modulate the expression of key effector cytokines and immune checkpoint molecules, affect MHC-mediated tumor-associated antigen presentation, and overall cancer immune surveillance [ 232 ]. Furthermore, malignant cells and other immune cells can exhibit epigenetic adaptations in response to immunotherapeutic interventions, rendering them resistant to immune checkpoint inhibitor-mediated destruction [ 233 ]. Consequently, agents capable of inhibiting or modulating methylation hold the potential to enhance T-cells cytotoxicity within the TME while simultaneously disrupting other epigenetic processes conducive to tumor growth, thereby disrupting cancer progression [ 233 ]. Inhibitors of DNMTs, LSD1, and EZH2 can suppress endogenous retrovirus (ERV) activity, activating anti-cellular antiviral RNA detection pathways and triggering interferon (IFN)-α production. This leads to interferon-stimulated gene (ISG) upregulation and the induction of proinflammatory chemokines, such as CCL5, CXCL9, and CXCL10 [ 20 , 234 , 235 ]. These inhibitors can also trigger the enhancement of MHC-I-mediated antigen presentation by increasing cancer-testis antigen (CTA) production, upregulating PD-L1, and downregulating Lag3, Tim3, and TIGIT to activate CD8 + T cells, triggering their invasion and cytotoxic functions [ 20 , 233 ]. Additionally, EZH2 inhibitors can also affect regulatory T cells (Treg), natural killer (NK) cells, and myeloid-derived suppressor cells (MDSCs) to enhance T cell proliferation and cytokine expression, thus amplifying the overall immune response [ 20 , 233 ]. Moreover, EZH2 inhibitors can reverse EZH2-mediated repression and reduce the differentiation of CD4 + Th1/Th2 cells, increasing the expression of effector cytokines in CD4 + T cells [ 233 ]. Inhibitors of FTO and METTL3 induce the upregulation of PD-1 and PD-L1, respectively, in addition to promoting CD8 + T cell activation and enhancing immunotherapeutic efficacy [ 236 , 237 ]. METTL3 inhibitors can also promote increased production of IFN-γ, CXCL9, and CXCL10, leading to enhanced T cell-mediated cytotoxicity [ 237 ] (Fig. 3 ). Fig. 3 Combination therapies with targeting methylation. At the moment, many clinical trials of DNA methyltransferase inhibitors (DNMTi), zeste homolog 2 enhancer inhibitors (EZH2i), lysine-specific demethylase 1 inhibitors (LSD1i), fat mass and obesity-associated protein inhibitors (FTOi), and methyltransferase like inhibitors (METTL3i) with Immunotherapy (programmed death-1 [PD-1]/programmed death-ligand 1 [PD-L1], cytotoxic T lymphocyte associate protein-4 [CTLA-4], lymphocyte activation gene 3 [LAG-3], and T cell immunoglobulin domain and mucin domain-3 [Tim-3], radiotherapy, targeted drugs (Anti-CD20 and B-cell lymphoma-2 inhibitors [BCL-2i]), and other epigenetic drugs (histone deacetylase inhibitors [HDACi]) were carried out vigorously. The combination of methylation-related drugs mainly affects the tumor microenvironment (TME). The mechanisms include: increasing and activating CD8 + and CD4 + T cells; Activate antigen processing and presentation mechanisms; and Up-regulated inflammatory genes and pathways that control tumor cell secretion of interferon (IFN), cytokines, and chemokines. Methylation can also manipulate the differentiation and function of tumor-infiltrating immune cells, including T cells, natural killer (NK) cells, macrophages, and bone marrow cells. There are also regulatory T cells (T regs ) that inhibit T cell proliferation and cells, which can be regulated by therapy. It is worth noting that radiotherapy immunity and DNMTi have a synergistic effect against tumors

Radiotherapy

Although radiotherapy exerts limited systemic control over tumor growth, it is highly effective for local disease control. Therefore, innovative combinations of radiotherapy and other therapeutic strategies are vital for achieving systemic control. Clinical trials have demonstrated the utility of epigenetic-modifying drugs as radiosensitizing agents capable of interfering with the cell cycle, impairing DNA damage repair, and inducing oxidative stress, ultimately leading to improved radiotherapy outcomes [ 238 ]. These combinations can also enhance tumor antigen release, resulting in superior immunogenicity, and highlighting the potential for combining radiotherapy, epigenetic drugs, and immunotherapy in clinical settings.

Irradiation of tumor cells induces double-stranded DNA break, releases tumor-specific peptide, and activates the cGAS/STING pathway. This results in the production of IFN-α and the subsequent induction of proinflammatory chemokines, including CXCL10 and CXCL16 [ 239 ]. Elevated IFN and CTA levels also trigger ISG upregulation, enhancing MHC-I-mediated antigen presentation and PD-L1 expression to promote antitumor immunity [ 240 , 241 ]. Furthermore, radiotherapy can trigger the production of cancer-specific peptides that can enhance DC-mediated antigen presentation and immune response induction [ 242 ]. Radiotherapy can also modulate MDSCs and M1 tumor-associated macrophages (TAMs); inhibition of MDSC leads to increased NK cell numbers and IFN-γ production, while M1 TAMs support CD8 + T cell activation, leading to increased antitumor immunity [ 240 – 242 ] (Fig. 3 ). Radiotherapy-induced double-stranded DNA damage can trigger the MAPK-dependent activation of DNA repair genes, including XRCC1 and ERCC1, within tumor cells. As METTL3 inhibitors suppress MAPK signaling and FTO inhibitors suppress the expression of ERCC1, they possess the potential to modulate radiotherapy outcomes [ 243 – 245 ]. Disruption of the m6A modification of circCUX1 with METTL3 inhibitors can lead to increased secretion of IL-1 and IL-18, potentially contributing to enhanced radiosensitivity [ 246 , 247 ]. Radiation may also trigger reductions in H3K27me3 levels, which are associated with chromatin condensation and impact the repair of double-stranded DNA breaks. The JMJD3 inhibitor GSKJ4 can increase tumor cell radiosensitivity, whereas the HDM UTX may counteract it [ 248 ]. At present, research focused on combining histone and RNA methylation-related modulators with radiotherapy is restricted to preclinical settings, highlighting an opportunity for future clinical development.

Targeted therapy

CD20, a non-glycosylated transmembrane protein of the MS4A family, is expressed by both normal and malignant B cells [ 249 ]. CD20 reportedly closely with other tetrameric molecules, including CD81, CD82, and CD53, to form supramolecular complexes [ 250 ]. It is capable of physically interacting with MHC-II, CD40, BCR, and CBP, enabling macrophage recognition and altering immune cell activity [ 251 ]. The loss of CD20 from CD20 + T cell populations may impair T cell-mediated immunity [ 252 ]. In addition to regulating Bcl-2, a key apoptosis-related protein, CD20 can be regulated by epigenetic modulators, including HDACs, DNMTs, and EZH2 [ 208 ]. DNMT and EZH2 inhibitors are capable of stimulating the expression of CD20 at both the mRNA and the cell surface level, thereby increasing sensitivity to anti-CD20 antibody treatment [ 208 ]. Since Bcl-2 is an inhibitor of apoptosis and CD20 can promote the expression of Bcl-2 and other targets, combination treatment with Bcl-2 inhibitors can activate apoptotic pathways to mediate tumor cell killing [ 253 ]. Inhibitors of Bcl-2, EZH2, and LSD1 can exert synergistic effects on programmed tumor cell death [ 254 , 255 ] (Fig. 3 ).

Epigenetic therapy

The combinatorial use of two or more epigenetic drugs can prolong or broaden therapeutic efficacy and/or reduce associated side effects by allowing for dose reductions. However, the underlying mechanisms of action must be carefully considered to achieve synergistic efficacy (Fig. 3 ). The combination of DNMT inhibitors with HDAC or LSD1 inhibitors is an area of active research interest. HDACs are enzymes that remove acetyl groups from histones and other proteins [ 256 ], and HDAC inhibitors can readily access the nucleus to markedly alter chromatin structure, gene expression, and functional outcomes such as apoptosis or proliferation [ 257 , 258 ]. Since DNA methylation and histone modifications are two predominant epigenetic mechanisms controlling gene silencing, combinatorial strategies with synergistic efficacy are important. LSD inhibitors are capable of inducing global DNA demethylation or inhibiting LSD1-driven H3K4me2 and H3K4me1 demethylation to establish active chromatin structures [ 259 ]. Combination treatment with DNMT inhibitors reportedly yields synergistic effects on abnormally silenced genes via H3K4me2 and H3K4me1 enrichment [ 259 ]. Further research aimed at balancing efficacy against adverse events will be vital to achieve more sophisticated and effective synergistic epigenetic treatment outcomes.

Chemotherapy and hormone therapy

In the absence of such intervention, the silencing of the androgen receptor gene, coupled with its functional blockade by hormone therapy, results in increased DNMT activity and expression in tumor cells, leading to impaired antitumor immunity. Thus, combining DNMT inhibitors with hormone therapy can effectively generate antitumor immunity, particularly in patients with prostate cancer. Since methylation inhibitors generally function in a genome-wide manner, they may simultaneously demethylate both tumor suppressor genes and oncogenes, potentially upregulating both and thereby limiting antitumor efficacy. Efforts to improve the gene specificity of these inhibitors may provide a means to enhance antitumor activity while reducing the incidence of adverse events.

Normal cells acquiring advantageous genetic or epigenetic alterations that modify gene expression can undergo clonal selection if these changes confer growth advantages through the silencing or activation of particular genes [ 260 , 261 ]. Interactions between these processes can lead to chromatin changes, and tumor cell therapeutic sensitivity can be further fueled by genetic and epigenetic pathways under conditions of chemotherapy or hormone treatments [ 260 ]. Increased DNMT expression and activity also coincide with upregulation of truncated androgen receptor subtype, which is conducive to the development of a hormone-resistant phenotype [ 262 ]. The use of a DNMT inhibitor can markedly reduce hormone resistance resulting from prolonged hormone treatment. In the absence of such intervention, the silencing of the androgen receptor gene, coupled with its functional blockage by hormone therapy, results in increased DNMT activity and expression in tumor cells, leading to impaired antitumor immunity [ 263 , 264 ]. Thus, combining DNMT inhibitors with hormone therapy can effectively generate antitumor immunity, particularly in patients with prostate cancer. Since inhibitors of methylation generally function in a genome-wide manner, they may simultaneously demethylate both tumor suppressor genes and oncogenes, potentially upregulating both and thereby limiting the antitumor efficacy. Efforts to improve the gene specificity of these methylation inhibitors may provide a means to enhance antitumor activity while reducing the incidence of adverse events.

Methylation, therapeutic resistance, and prognostic outcomes

Therapeutic resistance

Given that therapeutic resistance emerges through a range of complex and dynamic mechanisms, curative drug efficacy remains unsatisfactory for many cancer patients. Methylation studies provide an opportunity to analyze therapeutic resistance, facilitating the reliable selection of treatment regimens suitable for specific patient populations and better guiding clinical decision-making. This section guides the mechanistic link between methylation and therapeutic resistance, with several mechanisms established to date, including ATP-binding cassette (ABC) transporter overexpression, cancer stem cell (CSC) properties, autophagy, apoptosis, cell cycle arrest, gene mutation, DNA damage repair, epithelial-mesenchymal transition (EMT), and TME-related processes (Fig. 4 ). Fig. 4 Mechanisms of treatment resistance. Methylation can modulate various mechanisms to demonstrate therapeutic resistance (including drug resistance and radiological resistance). Including the ability to maintain or enhance the activity and function of cancer stem cells (CSCs); Reduce apoptosis and autophagy, and overexpression of ABC transporter protein, affecting drug delivery; Hypoxia can induce abnormal angiogenesis and change the function of surrounding stromal cells to indirectly regulate the stem cell of cancer cells and thus affect the tumor microenvironment. Can regulate DNA repair pathways or develop genetic mutations that allow them to evade anti-tumor therapy; Epithelial mesenchymal transformation (EMT) is driven to cause the dry features of tumor progression and metastasis; The cell cycle is affected, causing it to become dormant and thus resistant to treatment. It is worth noting that the main mechanisms causing radiotherapy resistance are non-homologous end joining (NHEJ) and homologous recombination (HR), G2/M stagnation and a large number of cells accumulation in S phase, CSCs production, self-inhibiting reactive oxygen species (ROS) production, TME changes, and hypoxia. Most of these processes are due to epigenetic regulation resulting in changes in metabolism or signaling pathway transduction

Drug resistance

Crosstalk between DNMTs and TET family members is closely associated with drug resistance in tumors. CSCs are key mediators of chemoresistance. Through the regulatory effects of DMNT1, BEX1/RUNX3 expression in the brain can activate Wnt/β-catenin signaling and maintain liver stem cell self-renewal, facilitating resistance to sorafenib and chemotherapy [ 265 ]. DNMT1 is capable of activating the cell cycle suppressors p21 and p27 via interactions with high mannose type CD133, maintaining glioma stem cell self-renewal and temozolomide resistance while reducing DNA damage [ 266 ]. DNA methylation also plays a role in resistance to targeted therapy. DNMT1-mediated miR-34a inhibition and Notch pathway activation can influence pancreatic cancer cell sorafenib sensitivity [ 267 ]. The CSC-related DNMT3b/OCT4 genes can promote HCC sorafenib resistance via the activation IL-6/STAT3 axis [ 268 ]. TET2 loss and the associated changes in mitogen-activated protein kinase (MEK) activity can mediate EGFR-TKI resistance in NSCLC cells via non-blocking transcriptional inhibition of TNF/NF-κB signaling, primarily through its ability to maintain CSC functionality [ 269 ]. DNMT1/UHRF1 complex-dependent promoter methylation suppresses MUC17 expression and NF-κB activity, leading to gefitinib or osimertinib resistance in NSCLC [ 270 ] (Fig. 5 ). Fig. 5 Methylation-induced chemotherapy or targeted drug resistance. Drug resistance is attributed to CSCs activity, the occurrence of apoptotic autophagy, EMTs, inhibition of DNA damage repair, and alterations in signal transduction pathways, all of which are regulated by methylation-related enzymes. Maintain the activity and function of CSCs: activation of the Wnt/β-catenin (DNMT1/3B, METTL3, FTO, ALKBH5, and LSD1/2), Notch (DNMT1 and METTL3), tyrosine kinase receptor (TKR) (DNMT3A, SETDB1, and EZH2), NF-κB (DNMT1, TET2, LSD1/2), JAK/STAT (FTO), and IGF-IR (DNMT3B and FTO) pathways; The activation of EMTs: LSD1/2 and PAD4; The inhibition of DNA damage repair: METTL3, KIAA1429, and PRMT5; The activation of autophagy: METTL3, YTHDF1, PRMT5, and PAD4. Other: chemotherapy can directly affect the function of RNA methylase and epithelial growth factor receptor tyrosine kinase inhibitors (EGFR-TKI) can regulate TKR and NF-κB pathways, thereby causing tumor cells to resist chemotherapy and targeted drugs

m6A modification represents the predominant form of RNA methylation implicated in tumor cells' drug resistance [ 271 ]. For instance, in breast cancer cells, METTL3 reportedly induces doxorubicin resistance via the miR-221-3p/HIPK2/Che-1, EGF/RAD51, and MALAT1/E2F1/AGR2 axes [ 272 – 274 ]. YTHDC1 can reverse METTL3-mediated EGF and RAD51 upregulation, thereby increasing doxorubicin resistance in breast cancer cells [ 273 ]. FTO can activate STAT3 signaling within breast cancer cells, further supporting doxorubicin resistance [ 275 ]. ALKBH5 functions as an inhibitor of Wnt signaling through its ability to upregulate Wnt inhibitory factor 1 (WIF-1) rather than function in β-catenin expression, reducing pancreatic ductal adenocarcinoma cell sensitivity to gemcitabine [ 276 ]. As an m6A methyltransferase, METTL3 can promote the upregulation of cytidine deaminases or the inhibition of the miR-3163/USP44 axis in pancreatic cancer, giving rise to p56-mediated gemcitabine resistance [ 277 , 278 ]. SEC62 can activate Wnt/β-catenin signaling to maintain the stem-like functions of CRC cells, contributing to 5-FU resistance in a manner mediated by METTL3 [ 279 ]. c-Myc can promote YTHDF1 expression in CRC and inhibit 5-FU responsiveness [ 280 ]. Zinc finger CCCH domain-containing protein 13 (ZC3H13) and PHD finger protein 10 (PHF10) are important regulators of pancreatic cancer cells’ gemcitabine susceptibility through their effects on the repair of DNA damage [ 281 ].

Both the METTL3-associated sustained activity of the miR-146a/Notch signaling axis and the YTHDF2-mediated suppression of TUSC7 contribute to erlotinib resistance in lung adenocarcinoma cells [ 282 ]. METTL3 can promote malignant melanoma cell resistance to the BFAF (V600E) kinase inhibitor PLX4032 through EGFR upregulation and subsequent activation of the RAF/MEK/ERK pathway [ 283 ]. METTL3 upregulation can itself promote higher levels of c-MET expression, enhancing signaling via the c-MET-HGF axis and increasing the ability of NSCLC cells expressing high levels of c-MET to tolerate crizotinib [ 284 ]. In HCC, METTL3-dependent m6A methylation can modulate circRNA-SORE to facilitate competitive Wnt/β-catenin pathway activation and induce resistance to sorafenib, while also activating CGCs and lenvatinib resistance via the respective FZD10/β-catenin/YAP1 and FZD10/β-catenin/c-Jun/MEK/EPR axes [ 285 , 286 ]. YTHDF1-dependent METTL3 downregulation can result in the degradation of FOXO3, which is observed in the context of autophagy-mediated sorafenib resistance in HCC [ 287 ]. Nilotinib resistance in leukemia is related to FTO overexpression-related m6A hypomethylation [ 288 ] (Fig. 5 ). For further details regarding the association between RNA methylation and drug resistance, refer to the review published by Zhuang et al. [ 237 ].

Methylases and demethylases are the primary proteins linking histone methylation to drug resistance. EZH2 hypermethylation can trigger the activation of miR-137/c-Myc cell survival pathways and the PI3K/AKT pathway, promoting gefitinib resistance in NSCLC and cisplatin resistance in ovarian cancer [ 289 , 290 ]. The hypermethylation of EZH2 can also suppress MEIS1 expression through the activity of the ELN1-AS1/EZH2/DNMT3A axis, leading to oxaliplatin resistance in CRC cells [ 291 ]. SETDB1 is capable of inducing the methylation of PELP1/AKT, promoting tamoxifen resistance in breast cancer [ 292 ]. Leukemia stem cells are a major cause of resistance to TKIs, and G9A is capable of reducing the expression of the tumor suppressor gene SOX6, thus maintaining leukemia stem cell survival and self-renewal [ 293 ]. MTAP-deficient cancer cells exhibit reduced MAT2A/PRMT5/RIOK1 axis activity, whereas class I PRMTs can enhance the ability of NSCLC cells or MTAP-deficient ovarian cancer cells to resist the PARP inhibitor BMN-673 [ 294 , 295 ]. PRMT5 is capable of inducing abnormal splicing of the DNA repair factor TIP60/KAT5, leading to defective homologous recombination repair that sensitizes acute leukemia cells to the inhibition of PARP [ 296 ]. LSD1 regulates therapeutic resistance in many cancer cells through changes in H3K4 and H3K9 methylation levels, primarily by maintaining CSC function; for instance, LSD1 overexpression increases breast cancer stem cell potential [ 297 ]. CD44, SOX2, and OCT4 in gastric cancer can promote resistance to oxaliplatin. Wnt/β-catenin pathway activation in thyroid cancer can induce chemotherapy resistance [ 298 ]. The enhancement of p65 demethylation with CD13 assistance can activate NF-κB and suppress the expression of spirgle1 and APC, promoting Lgr5 + cell proliferation and ultimately conferring sorafenib resistance to HCC cells [ 299 , 300 ]. LSD2 may function similarly, as when overexpressed, it upregulates NANOG, SOX2, LSD1, KDM4B, and KDM5B mRNA expression, promoting CSC-like characteristics and drug resistance [ 301 ]. LSD2 also plays a role in DNA damage repair and apoptosis, contributing to the resistance of ovarian, pancreatic cancer cells, and multiple myeloma to chemotherapy or proteasome inhibitors [ 302 – 304 ]. LSD1 can also induce chemoresistance in breast cancer by controlling EMT induction via interactions with protein kinase C-θ (PKC-θ) and BRCA1 [ 305 , 306 ]. Furthermore, LSD1 can interact with lncRNAs to regulate drug resistance; for instance, it interacts with FTH1P3 or HAS2 antisense RNA 1 (HAS2-AS1) to suppress the TIMP3 and EphB3, contributing to increased gefitinib and chemotherapy resistance in lung cancer cells [ 307 , 308 ]. LSD1 is also capable of activating the LINC01134/SP1/p62 axis, increasing resistance to oxaliplatin in HCC [ 309 ]. PRMTs are also related to therapeutic resistance. Hypoxia can induce the methylation of the PRMT5-mediated autophagic regulator ULK1, promoting tumor cell carboplatin resistance [ 310 ]. PRMT5 and MEP50 interaction-mediated hnRNPA1 methylation can stimulate internal ribosome entry site (IRES)-mediated cyclin D1 and c-Myc translation, helping glioblastoma cells better resist mTOR inhibitor treatment [ 311 ]. For further details regarding the association between histone methylation and therapeutic resistance, see the article published by Wang et al. [ 312 ] (Fig. 5 ).

Radiotherapy resistance

The formation of double-stranded DNA damage and the consequent inhibition of repair processes are major causes of radioresistance. Many studies of the responses of DNA damage repair (DDR) activity to methylation-related enzymes have thus been conducted. DNMT1 can promote the overexpression of DSTN and activate Wnt/β-catenin signaling to promote radioresistance in CRC cells. It can also promote miR-24 downregulation and alter H2AX expression, impairing the non-homologous end joining (NHEJ) process [ 313 , 314 ]. DNMT3A silencing in rhabdomyosarcoma can trigger senescence through the upregulation of p16 and p21. Depleting DNMT3B results in severe DNA damage and the impairment of DNA repair mediators, including reductions in ATM, DNA-PKcs, and Rad51 levels [ 315 ]. Conversely, DNMT3A and DNMT3B overexpression in nasopharyngeal carcinoma cells may render them resistant to radiotherapy through the promotion of p53 and p21 methylation [ 316 ]. Positive correlations between DNMT3B and hTERT promoter methylation levels have been reported, promoting EZH2 expression and gold histone methylation while also affecting small-cell lung cancer radioresistance [ 317 ]. BRCA1 and DAPK1 demethylation leads to the increased expression of these genes in cervical cancer, altering DNA damage repair and reducing apoptosis following irradiation, thereby giving rise to radioresistance [ 318 ] (Fig. 6 ). Fig. 6 Methylation-induced radiotherapy resistance. Radiotherapy resistance is attributed to the inhibition of DNA damage repair and the promotion of important signal transduction pathways by methylation-related enzymes. Regulate the DNA damage repair system (inhibit its repair or promote cell cycle dormancy, and the change of cell functional status affects its repair): DNMT3A, METTL3, ALKBH5, and EZH2; The activation of the relevant signal pathways: Wnt/β-catenin (DNMT1, DNMT3B, METTL3, and FTO), JAK/STAT3 (KDM4B), mitogen-activated protein kinase (MAPK) (METTL3), and PI3K-AKT/S6 (METTL3 and YTHDC1)

m6A modification is the most prominent type of RNA methylation in the context of radioresistance. METTL3-mediated m6A methylation can activate SOX2 and Wnt/β-catenin signaling to regulate glioma and GSC cell radiosensitivity by maintaining a dedifferentiated state [ 319 ]. It can also suppress MAPK signaling to render pancreatic cancer cells more radioresistant [ 320 ] and reduce radiosensitivity by promoting circCUX1/Caspase-1 pathway-mediated IL-1β and IL-18 production, thus reducing the programmed death of tumor cells [ 246 ]. METTL3 can increase osteosarcoma resistance to ionizing radiation through the recruitment of YTHDC1 and the homologous recombination factors BRCA1 and RAD51 [ 321 ]. TRDMT1 (DNMT2) is capable of inducing m5C modifications, increasing the recruitment of the RAD52/RAD51 or the expression of TET1/FMRP, altering homologous recombination repair activity and timing, and thereby improving the resistance of osteosarcoma cells to ionizing radiation [ 322 , 323 ]. Inhibition of ALKBH5 expression in GSC results in the downregulation of DNA damage repair-related factors (Rad51, XRCC2, BRCA2, EXO1, CH1, and γ-H2AX), leading to inhibition of homologous recombination, rapid repair, and enhanced radioresistance. FOXM1 expression can also be modulated to regulate cell cycle activity and radioresistance [ 324 ]. FTO can influence Wnt/β-catenin signaling to promote ERCC1, rendering cervical squamous cell carcinoma cells more resistant to chemoradiotherapy [ 325 ]. YTHDC2 can enhance IGF1R expression in nasopharyngeal carcinoma, thereby triggering PI3K-AKT/S6 pathway activation and inhibiting the apoptotic death of tumor cells in a manner that can promote resistance to radiotherapy [ 326 ] (Fig. 6 ).

Histone methylation is closely related to radiation-induced autophagic, which protects NSCLC cells. H4K20me3 modifications can facilitate GABARAPL1 upregulation and enhanced autophagy, promoting radioresistance that can be overcome by EZH2 inhibition [ 327 ]. In addition, radiation may trigger the methylation of histone H3 at the promoter of the CSC marker aldehyde dehydrogenase 1A1 (ALDH1A1), stimulating its transcription and rendering cancer cells resistant to radiotherapy [ 328 ]. Knocking down JMJD1A can affect RNF8 levels and the expression of various DNA damage repair factors, impairing double-stranded DNA break repair in prostate cancer cells [ 329 ]. KDM4B can activate JAK/STAT3 signaling through its ability to bind to cAMP-response element binding protein (CREB), reducing DNA damage repair and the ionizing radiation sensitivity of CRC [ 330 ] (Fig. 6 ).

Immunotherapy resistance

DNMT activity may be a potential mechanism underpinning immunotherapeutic resistance in melanoma. Hypermethylation can limit the efficacy of immune checkpoint blockade through the inhibition of endogenous IFN responses necessary for tumor cell recognition. Conversely, global hypomethylation can lead to immunosuppressive changes in EMT activity, inflammatory gene expression, and PD-L1 expression [ 331 ] (Fig. 7 ). However, the drivers of these contrasting methylation states remain unclear. Fig. 7 Methylation-induced immunotherapy resistance. Immunotherapy resistance is attributed to changes in signaling pathway transduction and immune microenvironment by methylation-related enzymes. Inhibit the adaptive immunity of cells against tumor cells: Toll-like receptor (TLR) signaling pathway (DNMT), mitogen-activated protein kinase (MAPK) signaling pathway (DNMT), cGAS/STING axis (DNMT and PRMT), IFN-γ/STAT1/IRF1 axis (YTHDF2), CD163/Nrf2 axis (PRMT), hypoxia, and Wnt/β-catenin pathway (FTO); Changes in the immune microenvironment: ALKBH5 can promote the secretion of lactic acid and increase the immunosuppressive effect of MDSC and T reg cells; Inhibit the expression of PD-L1 on the cell surface: DNMT, METTL, and YTHDF2

Most research on m6A modifications in this context has been focused on resistance to PD-1/PD-L1 checkpoint inhibitors [ 332 ]. METTL3 can support NSCLC cell evasion of CD8 + T cell cytotoxicity through YTHDC1-mediated activation of the circIGF2BP3/PKP3/PD-L1 pathway [ 333 ]. It can also be regulated by JNK to increase the resistance of bladder cancer cells to CD8 + T cell cytotoxicity [ 334 ]. METTL3/METTL14 can also suppress IFN-γ/STAT1/IRF1 signaling in a YTHDF2-dependent manner, suppressing chemokine and cytokine expression to enhance CRC and melanoma resistance to anti-PD-1 treatment [ 335 ]. ALKBH5 deletion can reduce MCT4/SLC16A3 expression to alter lactic acid levels within the TME, thus modifying Treg and MDSC recruitment and enhancing the efficacy of anti-PD-1 treatment in malignant melanoma [ 336 ]. FTO overexpression leads to the Wnt/β-catenin pathway-mediated upregulation of SOX10 and the inhibition of the IFN-γ-induced melanoma cells' death, contributing to immunotherapy resistance [ 337 ] (Fig. 7 ).

Relatively little research focused on histone methylation and immunoresistance. PRMT1 can methylate cGAS to inhibit cGAS/STING pathway activity, supporting tumor cell immune escape [ 338 ]. Methylation-mediated stabilization of KEAP1 by PRMT5 can lead to NRF2/HMOX1 pathway suppression and reduced ferroptosis, rendering triple-negative breast cancer cells more resistant to immune-mediated clearance [ 339 ] (Fig. 7 ).

Predictive and prognostic evaluation

The primary objective of clinical oncology is the improvement of patient survival. Maximizing the efficacy of novel therapeutic strategies necessitates the identification of biomarkers capable of predicting clinical outcomes, enabling the selection of patients most likely to benefit from a given treatment. Methylation analyses in patients undergoing epigenetic therapy provide an opportunity to determine the clinical responses of these drugs. Genome-wide methylation analyses from various tissues may represent another important area for future study, potentially opening new avenues for treatment. The ability of certain methylation patterns to predict cancer patient treatment outcomes and overall prognosis is summarized in Table 4 [ 340 – 421 ]. Table 4 Methylation biomarkers used for prediction and prognostic evaluation Cancer Methylation biomarkers Country Therapy Simple No Methylation situation Prognosis Survival outcome References Bladder cancer CDH11 China Surgery 146 Methylation vs. Unmethylation Poor prognosis HR (95% CI) of OS: 6.852 (3.461–16.177), p = 0.008 [ 340 ] Bladder cancer MSH6/THBS1 Spain Surgery/Bacillus Calmette-Guerin (BCG) 101 Methylation vs. Unmethylation Favorable prognosis HR (95% CI) of PFS: 0.226 (0.074–0.692), p = 0.009 [ 341 ] Bladder cancer RASSF1A Korea Surgery/Adjuvant radiotherapy 301 Methylation vs. Unmethylation Poor prognosis HR (95% CI) of PFS: 8.559 (1.547–47.364), p = 0.014 [ 342 ] Bladder cancer PCDH8 China Surgery 233 Methylation vs. Unmethylation Poor prognosis HR (95% CI) of PFS: 2.523 (1.654–7.431), p = 0.0036; HR (95% CI) of OS: 3.017 (1.542–8.251), p = 0.002 [ 343 ] Bladder cancer PMF-1 Spain Surgery/BCG 77 Unmethylation vs. Methylation Favorable prognosis HR (95% CI) of PFS: 2.91 (1.18–7.19), p = 0.020; HR (95% CI) of RFS: 2.03 (1.03–4.02), p = 0.020 [ 344 ] Bladder cancer PRAC Korea Surgery 136 Hypermethylation vs. Hypomethylation Poor prognosis HR (95% CI) of PFS: 9.531 (1.172–77.497), p = 0.035; HR (95% CI) of DFS: 2.652 (1.241–5.667), p = 0.012 [ 345 ] Bladder cancer RB1/PYCARD Spain Surgery/BCG 101 Methylation vs. Unmethylation Favorable prognosis HR (95% CI) of PFS: 0.149 (0.032–0.687), p = 0.015 [ 341 ] Bladder cancer RUNX3 Korea Surgery/BCG or Chemotherapy 186 Methylation vs. Unmethylation Poor prognosis HR (95% CI) of DFS: 1.709 (1.010–2.890), p = 0.046; HR (95% CI) of PFS: 5.126 (1.049–25.050), p = 0.043; HR (95% CI) of OS: 1.959 (1.129–0.398), p = 0.017 [ 346 ] Bladder cancer SFRP America Surgery 355 Methylation vs. Unmethylation Poor prognosis HR (95% CI) of OS: 1.78 (1.08–2.92), p = 0.042 [ 347 ] Bladder cancer SYNPO2 Spain Surgery/BCG 170 Methylation vs. Unmethylation Poor prognosis HR (95% CI) of DFS: 2.541 (1.207–5.350), p = 0.014; HR (95% CI) of PFS: 11.227 (1.530–82.361), p = 0.017 [ 348 ] Breast cancer CDH13 China Chemotherapy/Endocrine therapy or radiotherapy 238 Methylation vs. Unmethylation Favorable prognosis HR (95% CI) of DFS: 0.163 (0.051–0.520), p = 0.002; HR (95% CI) of OS: 0.374 (0.110–1.276), p = 0.067 [ 349 ] Breast cancer BRCA1 Multinational Surgery 3,205 Methylation vs. Unmethylation Poor prognosis HR (95% CI) of DFS: 1.38 (1.04–1.84), p = 0.005; HR (95% CI) of OS: 3.92 (1.49–10.32), p = 0.000 [ 350 ] Breast cancer HOXD13 China Surgery 196 Methylation vs. Unmethylation Poor prognosis HR (95% CI) of OS: 1.889 (1.151–3.099), p = 0.012 [ 351 ] Breast cancer SFRP1 Germany Surgery 130 Methylation vs. Week/no methylation Poor prognosis HR (95% CI) of OS: 1.565 (1.001–3.657), p = 0.047 [ 352 ] Breast cancer SALL2 China Endocrine therapy 238 Hypermethylation vs. Hypomethylation Poor prognosis HR (95% CI) of DFS: 1.939 (1.167–3.222), p = 0.011; HR (95% CI) of OS: 2.220 (1.251–3.940), p = 0.006 [ 353 ] Breast cancer PAX2 Iran Surgery/Adjuvant anti-hormone therapy 72 Hypermethylation vs. Hypomethylation Poor prognosis HR (95% CI) of DFS: 2.161 (1.041–4.486), p = 0.039; HR (95% CI) of OS: 0.887 (0.357–2.207), p = 0.797 [ 354 ] Breast cancer ESR1 China Surgery/Adjuvant chemotherapy 43 Methylation vs. Unmethylation Poor prognosis Median PFS: 3.0 months and 6.0 months; Median OS: 14.0 months and 20.5 months [ 355 ] Breast cancer MDR1 India Surgery 100 Methylation vs. Unmethylation Poor prognosis HR (95% CI) of OS: 8.40 (1.00–65.50), p = 0.040 [ 356 ] Breast cancer LDH-C4 China Surgery 136 Methylation vs. Unmethylation Poor prognosis HR (95% CI) of OS: 2.304 (1.115–3.457), p = 0.015 [ 357 ] Breast cancer CST6 Austria Chemotherapym Targeted therapy, or Hormone therapy 62 Methylation vs. Unmethylation Poor prognosis HR (95% CI) of OS: 7.99 (2.33–26.84), p < 0.000 [ 358 ] Breast cancer RASSF1 Austria Chemotherapym Targeted therapy, or Hormone therapy 62 Methylation vs. Unmethylation Poor prognosis HR (95% CI) of OS: 3.17 (1.10–9.17), p = 0.033 [ 358 ] Breast cancer H3K9me3 China Untreated 917 Methylation vs. Unmethylation Favorable prognosis HR (95% CI) of PFS: 0.70 (0.49,-0.99), p = 0.046; HR (95% CI) of OS: 0.77 (0.49–1.20), p = 0.244 [ 359 ] Cervical cancer CDH1/CDH13 Austria Surgery 93 Methylation vs. Unmethylation Poor prognosis HR (95% CI) of DFS: 2.50 (1.30–4.60), p = 0.005; HR (95% CI) of OS: 2.50 (1.30–4.80), p = 0.005 [ 360 ] Cervical cancer HHEX China Untreated 271 Hypermethylation vs. Hypomethylation Favorable prognosis HR (95% CI) of OS: 0.778 (0.635–0.953), p = 0.015 [ 361 ] Cervical cancer ITGA5 China Untreated 271 Hypermethylation vs. Hypomethylation Poor prognosis HR (95% CI) of OS: 1.012 (1.002–1.022), p = 0.019 [ 361 ] Cervical cancer PAX1 China Raidotherapy/Chemotherapy 125 Hypomethylation vs. Hypermethylation Favorable prognosis HR (95% CI) of OS: 4.433 (1.131–17.380), p = 0.033 [ 362 ] Cervical cancer RASSF1A India Surgery 110 Methylation vs. Unmethylation Poor prognosis HR (95% CI) of OS: 2.83 (1.11–7.22), p = 0.029 [ 363 ] Cervical cancer RASSF2A Spain Surgery 152 Methylation vs. Unmethylation Poor prognosis HR (95% CI) of DFS: 6.20 (1.20–32.40), p = 0.03; HR (95% CI) of OS: 6.00 (1.50–24.50), p = 0.012 [ 364 ] Cervical cancer S1PR4 China Untreated 271 Hypermethylation vs. Hypomethylation Favorable prognosis HR (95% CI) of OS: 0.787 (0.657–0.944), p = 0.010 [ 361 ] Cervical cancer SOX1 China Surgery 205 Methylation vs. Unmethylation Favorable prognosis 5-year OS rate: 93.35% vs. 68.29% (p = 0.048) [ 365 ] Cervical cancer VIM Korea Surgery 54 Methylation vs. Unmethylation Favorable prognosis 5-year DFS rate: 91.70% vs. 68.90% (p = 0.036) [ 366 ] Cervical cancer ZNF582 China Raidotherapy/Chemotherapy 219 Methylation vs. Unmethylation Poor prognosis HR (95% CI) of DFS: 2.826 (1.125–7.099), p = 0.030; HR (95% CI) of OS: 2.039 (0.700–5.938), p = 0.191 [ 367 ] Cervical cancer CENPK China Surgery 154 Methylation vs. Unmethylation Poor prognosis HR (95% CI) of OS: 1.413 (1.078–1.853), p = 0.012 [ 368 ] Cervical cancer H3K4me3 Germany Surgery 250 Methylation vs. Unmethylation Poor prognosis HR (95% CI) of PFS: 2.278 (1.084–4.790), p = 0.030; HR (95% CI) of OS: 1.503 (0.853–2.651), p = 0.159 [ 369 ] Colorectal cancer BCAT1/IKZF1 Australia Surgery 175 Methylation vs. Unmethylation Poor prognosis HR (95% CI) of PFS: 9.69 (2.50–37.59) [ 370 ] Colorectal cancer HLTF Germany Surgery 311 Methylation vs. Unmethylation Poor prognosis HR (95% CI) of OS: 2.4 (1.4–4.0), p = 0.001 [ 371 ] Colorectal cancer HPP1 Germany Surgery 311 Methylation vs. Unmethylation Poor prognosis HR (95% CI) of OS: 2.2 (1.4–3.4), p = 0.000 [ 371 ] Colorectal cancer LINE-1 America Surgery/Adjuvant chemotherapy 643 Hypermethylation vs. Hypomethylation Favorable prognosis HR (95% CI) of OS: 0.80 (0.64–0.89), p = 0.020 [ 372 ] Colorectal cancer p16 Multinational Radiotherapy or Chemotherapy 3,968 Hypermethylation vs. Hypomethylation Poor prognosis HR (95% CI) of OS: 1.64 (1.33–2.02); HR (95% CI) of DFS: 1.91 (1.16–3.15) [ 373 ] Colorectal cancer RASSF1A China, Greece, and Denmark Multiple 448 Methylation vs. Unmethylation Poor prognosis HR (95% CI) of OS: 1.91 (1.16–3.15) [ 374 ] Colorectal cancer SFRP China Surgery 307 Hypermethylation vs. Hypomethylation Favorable prognosis HR (95% CI) of OS: 0.333 (0.159–0.694), p = 0.032 [ 375 ] Colorectal cancer SHISA3 China Surgery/Adjuvant chemotherapy 127 Hypermethylation vs. Hypomethylation Poor prognosis HR (95% CI) of OS: 2.90 (1.50–5.80), p = 0.002; HR (95% CI) of DFS: 4.00 (1.60–10.20), p = 0.003 [ 376 ] Colorectal cancer TFAP2E Korea Surgery 193 Hypermethylation vs. Hypomethylation Favorable prognosis HR (95% CI) of OS: 2.24 (1.10–4.56), p < 0.025; HR (95% CI) of DFS: 2.44 (1.12–5.32), p = 0.025 [ 377 ] Colorectal cancer MGMT Spain Surgery/Adjuvant chemotherapy or radiotherapy 123 Hypomethylation vs. Hypermethylation Favorable prognosis HR (95% CI) of OS: 2.809 (1.269–6.214), p = 0.011; HR (95% CI) of DFS: 1.55 (0.81–2.99), p = 0.182 [ 378 ] Colorectal cancer ERCC1 Egypt Chemotherapy 80 Hypomethylation vs. Hypermethylation Favorable prognosis HR (95% CI) of OS: 2.809 (1.269–6.214), p = 0.011; HR (95% CI) of EFS: 3.480 (1.703–7.115), p = 0.001 [ 379 ] Colorectal Cancer PTEN America Targeted therapy with or without chemotherapy 76 Methylation vs. Unmethylation Favorable prognosis HR of OS: 0.496, p = 0.012; HR of PFS: 0.900, p = 0.734 [ 380 ] Colorectal cancer H3K4me2 Japan Surgery 54 Methylation vs. Unmethylation Favorable prognosis HR of OS: 0.338 (0.146–0.783), p = 0.001 [ 381 ] Lung cancer CDH1 America Surgery 132 Methylation vs. Unmethylation Poor prognosis HR (95% CI) of OS: 5.07 (1.32–19.40), p = 0.018 [ 382 ] Lung cancer CXCL12 Japan Surgery 236 Methylation vs. Unmethylation Poor prognosis HR (95% CI) of OS: 1.681 (1.107–2.551), p = 0.015 [ 383 ] Lung cancer DAPK America Surgery 132 Methylation vs. Unmethylation Poor prognosis HR (95% CI) of OS: 1.93 (1.06–3.53), p < 0.001 [ 382 ] Lung cancer KMT2C Greece Surgery or Chemotherapy 139 Unmethylation vs. Methylation Favorable prognosis Surgery: HR (95% CI) of PFS: 0.239 (0.099–0.575), p = 0.001; HR (95% CI) of OS: 0.324 (0.135–0.865), p = 0.023 Chemotherapy: HR (95% CI) of PFS: 0.431 (0.239–0.779), p = 0.005; HR (95% CI) of OS: 0.306 (0.173–0.541), p < 0.001 [ 384 ] Lung cancer MGMT Germany Multiple 859 Methylation vs. Unmethylation Poor prognosis HR (95% CI) of OS: 2.05 (1.41–2.98), p = 0.000 [ 385 ] Lung cancer NPTX1 China Surgery 188 Methylation vs. Unmethylation Poor prognosis HR (95% CI) of OS: 1.974 (1.107–3.517), p = 0.021 [ 386 ] Lung cancer PTGER2 Japan Surgery 133 Methylation vs. Unmethylation Favorable prognosis HR (95% CI) of OS: 0.481 (0.263–0.878), p = 0.017 [ 387 ] Lung cancer RASSF1 America Surgery 132 Methylation vs. Unmethylation Poor prognosis HR (95% CI) of OS: 2.71 (1.05–7.00), p = 0.040 [ 382 ] Lung cancer TFPI-2 China Surgery 133 Methylation vs. Unmethylation Poor prognosis HR (95% CI) of OS: 1.922 (1.149–3.214), p = 0.013 [ 388 ] Lung cancer TGFBI Korea Chemotherapy 138 Methylation vs. Unmethylation Poor prognosis HR (95% CI) of DFS: 2.88 (1.19–6.99), p = 0.019 [ 389 ] Lung cancer miR-34b/miR-34c/miR-124–3 Korea Surgery/Adjuvant therapy 157 Methylation vs. Unmethylation Poor prognosis HR (95% CI) of OS: 4.44 (2.15–9.18), p < 0.000 [ 390 ] Lung cancer H3K27me3 China Complete ablative surgical 71 Hypermethylation vs. Hypomethylation Poor prognosis HR (95% CI) of OS: 6.04 (1.42–25.70), p = 0.015 [ 391 ] Esophageal carcinoma FOXF2 China Surgery 135 Methylation vs. Unmethylation Poor prognosis HR (95% CI) of OS: 2.385 (1.360–4.182), p < 0.002 [ 392 ] Esophageal carcinoma IGF-1/IGF-1R/IGFBP3 China Untreated 264 Methylation vs. Unmethylation Poor prognosis HR (95% CI) of OS: 5.821 (2.922–11.600), p < 0.001 [ 393 ] Esophageal carcinoma p16 Thailand Untreated 213 Methylation vs. Unmethylation Poor prognosis HR (95% CI) of OS: 2.82 (1.13–5.06), p = 0.027 [ 394 ] Esophageal carcinoma TP53 Thailand Untreated 213 Methylation vs. Unmethylation Poor prognosis HR (95% CI) of OS: 2.95 (1.34–5.47), p = 0.007 [ 394 ] Endometrial cancer CDH1 China Surgery 152 Methylation vs. Unmethylation Poor prognosis 5-year OS rate: 50.00% vs. 78.80% (p = 0.021) [ 395 ] Endometrial cancer RASSF1A Korea Surgery 70 Hypermethylation vs. Hypomethylation Poor prognosis 5-year DFS rate: 77.80% vs. 97.00% (p = 0.039) [ 396 ] Gastric cancer MAGI2 China Surgery/Adjuvant chemotherapy 70 Methylation vs. Unmethylation Poor prognosis HR (95% CI) of PFS: 2.245 (1.154–4.369), p = 0.012 [ 397 ] Gastric cancer TRAF2 China Surgery 129 Hypomethylation vs. Hypermethylation Favorable prognosis HR (95% CI) of OS: 18.827 (3.103–114.222), p = 0.001 [ 398 ] Glioblastoma HOXA 9/10 Italy Surgery/Adjuvant chemoradiotherapy 63 Hypermethylation vs. Hypomethylation/Unmethylation Favorable prognosis Median survival 16.8 and 5.9 months, HR of OS: 0.256, P = 0.004 [ 399 ] Glioblastoma MGMT Multinational Radiotherapy or Chemotherapy 2,593 Methylation vs. Unmethylation Favorable prognosis HR (95% CI) of OS: 0.48 (0.35–0.65), p < 0.001; HR (95% CI) of PFS: 0.43 (0.32–0.56), p < 0.001 [ 400 ] Head and neck cancer PDCD1 Germany Surgery 527 Methylation vs. Unmethylation Poor prognosis HR (95% CI) of OS: 2.14 (1.19–3.84), p = 0.011 [ 401 ] Head and neck cancer SEPT Germany Surgery/Adjuvant radioyherapy or chemoradiotherapy 219 Methylation vs. Unmethylation Poor prognosis HR (95% CI) of DFS: 2.72 (1.30–5.68), p = 0.008 [ 402 ] Liver cancer SOCS3 China Transarterial chemoembolization (TACE) 246 Hypermethylation vs. Hypomethylation Poor prognosis HR (95% CI) of OS: 3.44 (2.57–6.31), p < 0.001 [ 403 ] Liver cancer TFPI2 China Surgery 198 Hypermethylation vs. Hypomethylation Poor prognosis HR (95% CI) of OS: 2.718 (1.590–4.647), p = 0.000; HR (95% CI) of DFS: 2.522 (1.726–3.684), p = 0.000 [ 404 ] Lymphoma MGMT Japan Chemotherapy 116 Methylation vs. Unmethylation Favorable prognosis 5-year OS rate: 65.10% vs. 47.80% (p = 0.036) [ 405 ] Lymphoma PCDH10 China Targeted therapy/Chemotherapy 107 Methylation vs. Unmethylation Poor prognosis HR (95% CI) of OS: 1.829 (1.059–3.158), p = 0.030; HR (95% CI) of PFS: 2.977 (1.245–7.119), p = 0.014 [ 406 ] Melanoma LKB1 China Untreated 107 Methylation vs. Unmethylation Poor prognosis HR (95% CI) of OS: 3.688 (1.124–12.100), p = 0.031 [ 407 ] Melanoma PTEN Korean Untreated 158 Hypermethylation vs. Hypomethylation Poor prognosis HR (95% CI) of OS: 3.76 (1.20 to 11.10), p = 0.017 [ 408 ] Myeloid neoplasms ANGPT2 Italy Untreated 88 Hypomethylation vs. Hypermethylation Favorable prognosis HR (95% CI) of OS: 2.435 (1.137–5.215), p = 0.022 [ 409 ] Myeloid neoplasms DLX5 China Untreated 270 Methylation vs. Unmethylation Poor prognosis HR (95% CI) of OS: 2.345 (1.113–4.942), p = 0.025 [ 410 ] Myeloid neoplasms ITGBL China Untreated 160 Methylation vs. Unmethylation Poor prognosis HR (95% CI) of OS: 4.045 (1.287–12.711), p = 0.017 [ 411 ] Nasopharyngeal carcinoma RAB37 China Radioyherapy/Chemotherapy 110 Hypermethylation vs. Hypomethylation Poor prognosis HR (95% CI) of OS: 4.265 (1.291–14.10), p = 0.017; HR (95% CI) of DMFS: 8.316 (1.119–61.774), p = 0.038 [ 412 ] Nasopharyngeal carcinoma TIPE3 China Radioyherapy/Chemotherapy 441 Hypermethylation vs. Hypomethylation Poor prognosis HR (95% CI) of OS: 2.99 (1.59–5.64), p = 0.001; HR (95% CI) of DFS: 2.56 (1.35–4.85), p = 0.004 [ 413 ] Ovarian cancer BRCA1 Multinational Surgery/Adjuvant chemotherapy 2,636 Methylation vs. Unmethylation Poor prognosis HR (95% CI) of PFS: 1.26 (1.02–1.56), p = 0.030; HR (95% CI) of OS: 1.11 (0.88–1.41), p = 0.035 [ 414 ] Ovarian cancer OPCML China Surgery 102 Methylation vs. Unmethylation Poor prognosis HR (95% CI) of OS: 13.55 (1.85–98.97), p = 0.010 [ 415 ] Prostate cancer GSTP1 Multinational Surgery 1,045 Hypermethylation vs. Hypomethylation/Unmethylation Poor prognosis HR (95% CI) of PFS: 2.57 (1.30–5.10), p = 0.007; HR (95% CI) of OS: 2.73 (2.05–3.62), p < 0.000 [ 416 ] Prostate cancer RASSF1/DAPK1 Lithuania Surgery 149 Methylation vs. Unmethylation Poor prognosis HR (95% CI) of OS: 2.20 (1.06–4.54), p = 0.027 [ 417 ] Renal cancer CDO1 Netherlands Surgery with or without (neo) adjuvant therapy 365 Hypermethylation vs. Hypomethylation Poor prognosis HR (95% CI) of OS: 1.66 (1.13–2.44), p = 0.006 [ 418 ] Renal cancer PCDH17 China Surgery 191 Methylation vs. Unmethylation Poor prognosis HR (95% CI) of OS: 2.876 (1.347–8.065), p = 0.010; HR (95% CI) of PFS: 3.014 (1.235–7.463), p = 0.003 [ 419 ] Thyroid carcinoma HOXD10 China Surgery 152 Methylation vs. Unmethylation Poor prognosis HR (95% CI) of OS: 3.779 (1.283–11.128), p = 0.016 [ 420 ] Thyroid carcinoma RASSF1A Multinational Multiple 498 Methylation vs. Unmethylation Poor prognosis HR (95% CI) of DFS: 2.63 (1.28–5.38), p < 0.011; HR (95% CI) of OS: 1.26 (0.45–3.62), p < 0.664 [ 421 ] HR hazard ratio, CI confidence interval, OS overall survival, PFS progression-free survival, DFS disease-free survival, EFS event-free survival, DMFS distant metastasis-free survival

Currently, liquid biopsy programs focused on ctDNA/cfDNA methylation are a major focus of clinical attention. The short half-life of ctDNA makes it directly related to the tumor cells' status, reflecting the instantaneous changes in treatment response and tumor load, achieving accurate tumor staging, and predicting recurrence. Detection involves single-target and multi-target assays. SEPT9 methylation reflects postoperative recovery in CRC, with reduced levels closely related to disease remission [ 422 ]. Preoperative SEPT9 methylation levels correlate linearly with tumor diameter and accurately reflect distant metastasis or recurrence [ 423 ]. Methylation levels of EYA4, GRIA4, ITGA4, MAP3K14-AS1, and MSC reflect tumor size dynamics in CRC patients, indicating poor or good treatment response [ 424 ]. The combined BCAT/IKZF1 methylation test has higher sensitivity than routine CEA for diagnosing postoperative recurrence of stage II/III CRC [ 425 ]. Methylation markers of 10 liver cancer-related genes are correlated highly with tumor load, treatment response, and clinical stage, offering good monitoring and prognostic value [ 426 ]. MGMT hypermethylation in cfDNA predicts response and time to progression in glioblastoma multiforme or metastatic CRC patients treated with cytotoxic alkylating agents [ 427 ]. Diffuse breast cancer has a poor prognosis and is difficult to identify by imaging techniques. A study identified and optimized a specific and highly expressed methylation tag EFC#93 in diffuse breast cancer, containing 5 associated CpG patterns, whose elevated level is closely related to metastatic breast cancer and can diagnose poor prognosis 12 months before metastasis [ 428 ]. In metastatic breast cancer, methylation of WNT5A, KLK10, MSH2, GATA3, and SOX17 is associated with poorer prognosis, disease progression, or death, with higher methylation scores indicating non-response to treatment [ 429 ]. Dynamic methylation changes of HOXA9 have been used to predict the therapeutic effect of novel therapies for ovarian cancer, filling the gap in predictive biological indicators of therapeutic effect [ 430 ].

Methylated biomarkers and ctDNA methylated liquid biopsy are widely used in clinical diagnosis and prognosis; some have entered clinical trials or been approved for clinical practice, showing good application prospects. However, challenges remain for large-scale clinical implementation. First, when single-test reliability is insufficient, combining screening programs can improve the detection effect. Second, costs must be considered to reduce the financial burden on patients. Clinically, strict inspection procedures and quality management norms are necessary to eliminate batch differences and human errors as much as possible, thereby improving result reproducibility.

Conclusions and perspectives

Recent years have witnessed increasingly comprehensive investigations into the roles that epigenetic modifications-including DNA, RNA, and histone methylation oncogenesis, driven by advances in genomics and molecular biology. The widely accepted results of these studies have major implications for drug development, clinical management, and patient care. Small-molecule drugs modulating these modifications have been developed, providing unprecedented opportunities for diagnosis, treatment, and prognostic evaluation. Specific genes shape cancer-related processes, including growth, development, differentiation, and immunomodulation. Tumor cell-derived DNA enters the blood through secretion, apoptosis, and other mechanisms; the resultant ctDNA may harbor the DNA methylation profiles of the parental malignant cells. The expression and methylation of specific genes in cancers have fueled the development of methylation-focused tests for detecting latent cancer cells. These tests are ideally situated to predict tumor development or monitor patients in a convenient, minimally invasive manner. Future methylation-focused strategies may further improve screening, stratified treatment, and follow-up strategies.

Tumor cell proliferation coincides with genomic abnormalities and chromatin modifications. The mechanisms underlying these dysregulated processes enable tumor cells to evade detection or killing, with epigenetic changes, supporting these evasive behaviors. Formulating personalized treatment options remains challenging, partly due to therapeutic resistance. The processes controlling resistance are dynamic; many epigenetic changes exist pre-treatment or evolve during treatment, yielding various phenotypes. Since epigenetic modifications are reversible, they can readily alter tumor cells and shape prognosis. However, the complex, changeable nature of the epigenetic code represents a persistent barrier to a comprehensive understanding of the molecular mechanisms linking epigenetic changes to cancer treatment outcomes. While epigenetic therapeutics offer unprecedented options, they also increase the complexity of personalized care. There is a pressing need to further study resistance mechanisms, particularly the roles of CSCs, the TME, and specific signaling pathways. As many epigenetic drugs are under development, their continued entry into clinical trials is expected, and combination therapies may provide superior patient outcomes when single-agent benefits are inadequate.

A review of contemporary clinical trials highlights that choosing suitable treatment plans poses significant clinical challenges, which need to be explored in detail and even overcome. Experimental failure is one such challenge. A University of Tokyo study found that adult T-cell leukemia/lymphoma treated with the histone methylation inhibitor valemetostat experienced progression or recurrence after an average of 4 months [ 431 ]. Multiple substitutions of EZH2 (Y111) and EZH2 (Y661) were detected in relapsed somatic cells, restoring the pre-treatment H3K27me3 pattern and reversing the effect of valemetostat [ 432 ]. In addition, decreased TET2 mutation and high expression of DNMT3A were detected; cells developed resistance even without the EZH2 mutation [ 432 ]. Furthermore, quiescent leukemia stem cells cannot be eradicated, making methylation therapy difficult to be effective. In some clinical trials of combination therapy, failures resulted from drug interactions or poor patient tolerance. In the phase Ib clinical trial of azacitidine plus vorinostat for the treatment of relapsed/refractory diffuse large B-cell lymphoma ( NCT01120834 ), 5 cases had poor dose tolerance, 17 cases showed disease progression, and the trial ended prematurely. Uncontrollable toxicity is also a huge challenge, with myelosuppression being the most common adverse event of methylation inhibitors. In the clinical trial of azacitidine plus R-CHOP in the treatment of diffuse large B-cell lymphoma, all patients presented with grade 3 or 4 neutropenia and required increased blood transfusion support. Severe gastrointestinal toxicity also causes treatment interruptions. After the initiation of phase I clinical trial of 4′-thio-2′-deoxycytidine (TdCyd) for the treatment of advanced solid tumors ( NCT02423057 ), more than 50% of the patients experienced pulmonary infection, with one death leading to study suspension. Wang et al. found that DNMT3A inhibitors could increase infarction area and inflammation in stroke patients [ 433 ]. To promote innovative treatment plans, broader exploration is needed to overcome these challenges. Future clinical trials require more detailed analysis, strict operational control, and consideration of individual differences. For methylation inhibitors, while ensuring the therapeutic effect, drug dosage and regimen must be adjusted, and synergistic treatment sought to reduce drug toxicity while ensuring efficacy. Foreseeable adverse events should be managed with advanced intervention or supportive care.

In summary, epigenetic inheritance is closely tied to the onset, proliferation, metastatic progression, and therapeutic resistance of tumor cells. Targeting specific epigenetic processes can synergize with conventional radiotherapy, chemotherapy, and immunotherapy strategies to help kill target tumor cells. Given the poor prognostic implications of therapeutic resistance, it is vital that a more detailed understanding of the mechanisms that govern drug resistance be established and that approaches to targeting epigenetic regulatory mechanisms be devised more reliably to achieve better patient outcomes. Large-scale clinical trials in the future will inevitably help advance this field and improve patient outcomes. The integration of methylation therapy into precision oncology remains a great challenge, and the application of multi-omics fusion in the methylation model of personalized medicine is needed in the future. With the development of science and technology, the reuse of simple and effective programmable gene editing tools by the CRISPR-Cas system has greatly promoted basic and applied research in many fields, laying the foundation for the development of targeted gene therapy and various biotechnology applications, and may promote joint strategies with methylation therapy. With the attention of the computer field, the combination of medicine and industry has become a prominent trend, which has significantly promoted the diagnosis and treatment of cancer. AI is increasingly integrated with multimodal data, including imaging omics and pathomics, to identify primary tumors and methylation levels, more favorably guiding personalized cancer treatment and prognostic prediction.

Abbreviations

5-Methylcytosine

Cytosine-phosphate-guanine

N6-methyladenosine

Methyltransferase like 3

Single-nucleotide polymorphisms

Lysine-specific histone demethylase 1

Histone demethylase

DNA methyltransferase

Methyl-CpG-binding domain

Kaiso, and set the ring finger-associated

Colorectal cancer

Human papillomavirus

Epstein-Barr virus

N 1 -methyladenosine

5-Methylcytidine

N 3 -methylcytosine

N 7 -methylguanosine

2′-O-methylation

Pseudouridine

Adenosine-to-inosine

Hepatocellular carcinoma

Histone methyltransferases

Protein/histone arginine methyltransferases

S-adenosine methionine

S-adenosine homocysteine

Histone 3 and histone 4

Plant homeodomain

Cell-free DNA

Circulating tumor-derived DNA

The Food and Drug Administration

The communate Europpene

The National Medical Products Administration

Tumor microenvironment

5-Azacytidine

5-Aza-2′-deoxycytidines

5-Fluoro-2′-deoxycytidine

Myelodysplastic syndrome

Acute myeloid leukemia

Chronic myeloid leukemia

Guadecitabine

Polycomb repressor complex 2

Polycomb group

3-Deazaneplanocin A

Tazemetostat

Asymmetric dimethylarginine

Symmetric dimethylarginine

Methyl-thioadenosine phosphorylase

MLL-rearrangement

Flavin adenine dinucleotide

2-Oxoglutarate

α-Ketoglutaric acid

Methyltransferase complex

Non-small cell lung cancer

Overall response rate

Interferon-stimulated gene

Cancer-testis antigen

Regulatory T

Natural killer

Myeloid-derived suppressor cells

Tumor-associated macrophages

ATP-binding cassette

Cancer stem cell

Epithelial-mesenchymal transition

Internal ribosome entry site

Non-homologous end joining

Aldehyde dehydrogenase 1A1

CAMP-response element binding protein

Bronchoalveolar lavage fluid

BRCA1-associated protein-1

Donor lymphocyte infusion

Fat mass and obesity

All-trans retinoic acid

Protein arginine methyltransferase 5

Methylthioadenosine

Methylthioadenosine phosphorylase

Enhancer of zeste homolog 1/2

Embryonic ectoderm development

Histone 3 lysine 79 methyltransferase

Nuclear receptor-binding SET domain-containing 2

SET domain containing 2

Programmed death-

Programmed death-ligand 1

Cytotoxic T lymphocyte-associated protein-4

Transforming growth factor-β

Poly adenosine diphosphate ribose polymerase

Antibody–drug conjugate

Neural precursor cell expressed developmentally downregulated 8

Cereblon E3 ligase modulator

Immune checkpoint inhibitors

Histone deacetylase

Indoleamine 2,3-dioxygenase 1

Vascular endothelial growth factor

T cell immunoglobulin domain and mucin domain-3

Chimeric antigen receptor T-cell immunotherapy

Mitogen-activated protein kinase

Hazard ratio

Confidence interval

Overall survival

Progression-free survival

Disease-free survival

Event-free survival

Distant metastasis-free survival

Ten-eleven translocation

Thymine DNA glycosylase

DNA methyltransferase inhibitors

Zeste homolog 2 enhancer inhibitors

Lysine-specific demethylase 1 inhibitors

Fat mass and obesity-associated protein inhibitors

Methytransferase-like inhibitors

Lymphocyte activation gene 3

B-cell lymphoma-2 inhibitors

Histone deacetylase inhibitors

Interferon

Homologous recombination

Reactive oxygen species

Epithelial growth factor receptor tyrosine kinase inhibitors

Mitogen-activated protein kinase

Toll-like receptor

Publisher's Note

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Acknowledgements

All figures are drawn by the BioRender ( https://BioRender.com ) platform and have been published with permission.

Author contributions

Hong Zhu: Conceptualization, methodology, software, resources, data curation, writing—original draft, and writing—review and editing, supervision, project administration, and funding acquisition. Youwen Zhu: Conceptualization, methodology, validation, formal analysis, investigation, writing-original draft, and writing-review and editing, and visualization. Kun Liu: Conceptualization, methodology, validation, formal analysis, investigation, writing—original draft, and writing—review and editing. All authors have read and approved the manuscript.

Funding

This work was partly supported by the Changsha Natural Science Foundation of Hunan Province of China (Grant/Award Number: kq2208376 to H. Zhu).

Data availability

All authors had full access to all of the data in this study and took complete responsibility for the integrity of the data and accuracy of the data analysis. The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Declarations

Ethics approval and consent to participate

This article is based on previously conducted studies and does not contain any new studies with human participants or animals performed by any of the authors; it does not require the approval of the independent ethics committee.

Competing interests

The authors declare no competing interests.

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