INTRODUCTION
Cancer is a major public health concern worldwide, with approximately 20 million new cases and nearly 10 million cancer-related deaths reported in 2022 [ 1 ]. The top five cancer cases among both males and females were lung (12.4%), breast (11.5%), colorectal (9.6%), prostate (7.3%), and stomach (4.8); meanwhile, breast cancer became the top cancer case among females (23.8%) [ 1 ]. Cancer and its treatments may accelerate aging by disrupting metabolic and biological processes, leading to increased risks of comorbidities such as cardiovascular disease [ 2 ], physical and psychological frailty, and cognitive decline [ 3 ] among survivors compared to cancer-free individuals. These changes likely stem from both cancer-related molecular alterations and the lasting impact of cancer treatments on aging pathways, highlighting the importance of understanding these effects to improve long-term survivorship outcomes.
Cancer survivors are defined as individuals from the time of diagnosis through the balance of life [ 4 ]. There is increasing evidence that cancer survivors experience more accelerated biological aging compared to their healthy counterparts, with major disruption in numerous physiological systems [ 5 ]. The biological age, an index of the functional status of tissues and the likelihood of age-associated disease, may deviate greatly from the chronological age, especially following cancer and its therapy [ 6 ]. Among the prominent indicators of premature aging are altered DNA methylation, such as hypermethylation of tumor suppressor genes and hypomethylation of oncogenes, which has been extensively observed among survivors [ 7 ]. These epigenetic changes, particularly on genes like death associated protein kinase 1 (DAPK1) and P16INK4a, are linked to cognitive dysfunction and neurodegenerative risks [ 8 ]. Similarly, elevated levels of biomarkers such as growth differentiation factor 15 (GDF-15) are associated with increased susceptibility to cardiovascular and age-related conditions in this population [ 9 ].
Beyond genetic markers, lipid metabolism also undergoes profound changes in cancer survivors [ 10 ]. Dyslipidemia profiles, as evidenced by low high-density lipoprotein (HDL) and elevated levels of oxidative stress markers such as malondialdehyde (MDA), are predictive of cardiovascular risk [ 11 , 12 ]. Disruptions in hormone axes, e.g., the leptin-adiponectin axis, also promote risks of metabolic disorders, such as obesity and insulin resistance [ 13 - 15 ]. Chronic inflammation is yet another hallmark of aging in cancer survivors, where elevated levels of inflammatory markers like C-reactive protein (CRP) and TNF-α result can cause immune dysfunction, endothelial dysfunction, and even psychological conditions like anxiety and depression [ 16 - 18 ].
Additionally, long-term inflammation has also been linked to cardiovascular diseases and psychological disorders like anxiety and depression, which are common in cancer survivors [ 19 ]. Also, immune senescence, or aging of the immune function, appears to be hastened secondarily to treatments like chemotherapy and radiation, which cause oxidative stress, T-cell depletion, and promote systemic inflammation [ 20 , 21 ]. This is superimposed on disrupted energy regulation, with survivors often having mitochondrial dysfunction and reduced metabolic efficiency, thus suffering from chronic fatigue and further predisposition to metabolic disease [ 13 , 14 ]. Some studies have shown that energy balance (decreased energy expenditure for mitochondria and impaired mtDNA stability) in cancer survivors is higher than in healthy subjects [ 22 , 23 ]. Collectively, these findings emphasize the complex interplay between cancer, its treatment, and biological aging, with a critical need for targeted interventions to preserve metabolic, immune, and cognitive health among cancer survivors over the long term.
To the best of our knowledge, no prior review has examined the metabolic and molecular aspects of aging in both healthy individuals and cancer survivors. Thus, this review aims to compare the metabolic and biological processes associated with aging among cancer survivors and healthy populations, and link abnormalities in these processes to aging phenotype. For this purpose, we have addressed biological age, lipid profiles, inflammatory indicators, immune senescence, and energy regulation. Additionally, oral and bone health aspects were also addressed in this review.
METHODOLOGY
Review framework
The primary objective of this systematic review is to elucidate the differences in aging phenotypes between cancer survivors and their control counterparts. To address the research question, this study employed the Patient, Intervention, Comparison, Outcomes (PICO) framework: Patient = adult cancer patients or survivors; Intervention = cancer treatment; Comparison = control group or non-cancer patients; Outcomes = lipid profile, biological age, inflammatory markers, immune senescence, energy regulation, bone architecture and oral health.
Search strategy
This systematic review was carried out following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO CRD42024502513). The primary techniques for searching strategy included database filters, Boolean operators, and hand screening. Searches were limited to human studies and English-language articles. The database search took place between May 2024 and February 2025 utilizing the Web of Science, Scopus, and PubMed databases. These databases were used, together with truncation symbols and Boolean operators (OR, AND, AND NOT) for advanced searches to help find relevant articles. The Boolean operators were applied to combine synonyms for the term. The term was interchangeably based on the MeSH term while searching through the databases for articles ( Table 1 ). The research team also conducted a manual search for the relevant review papers found via the search from Google Scholar, in addition to the references listed in the articles selected for data extraction. The combination by using Boolean operators was as follows: ((Cancer OR tumo?r OR carcinoma OR neoplasm) AND (Survivor* OR patient*) AND (“Healthy population*” OR control* OR “non-cancer*”) AND (“Biological age*” OR telomere OR “DNAm age” OR epigenetic*) OR (Lipid OR “Lipid profile” OR cholesterol) OR (“Inflammatory marker*”) OR (“Immuno-senescence*” OR senescence*) OR (“Energy regulation” OR “mitochondrial dysfunction*”) OR (Bone* OR osteoporosis) OR (oral OR dental OR hyposalivation*) AND (adult*) AND NOT (animal*) AND NOT (review) AND NOT (randomized controlled trial*) AND NOT (elderly)).
Eligibility criteria
The inclusion and exclusion criteria were used to guide the article selection process. The inclusion criteria for the selected articles were: 1) Adults (18-64 years old); 2) Types of study: cross-sectional, case-control study, longitudinal study. Randomized control trials (RCTs) were not included in this review; 3) written in English and published from 2019 to 2024. Publications were excluded if they were conference abstracts, chapters in a book, book, book series, review articles, grey literature, protocol papers, editorial manuscripts, preprints, and foreign language articles (except English). The search was limited to human studies only.
To specifically examine cancer-related aging trajectories during early and mid-adulthood, adults between the ages of 18 and 64 were included. This review fills the gap to explore early biological and metabolic changes that might arise shortly after a cancer diagnosis and treatment among adult cancer survivors compared to the healthy population, as most studies solely focus on the elderly, potentially overlooked among adult cancer survivors. Focusing on this adult group allows for a better understanding of the onset and progression of aging-related phenotypes in cancer survivors before reaching geriatric age. Moreover, the RCT studies were excluded in this review due to the nature of this study which primarily involves targeted or structured interventions.
Selection process
First, the reference manager of Mendeley software (Elsevier Inc.) was used to compile the articles from the chosen databases to eliminate duplicate entries. Subsequently, the remaining articles were loaded into the online systematic article management system, commonly referred to as Rayyan ( https://rayyan.qcri.org ). This free online and mobile application speeds up the semi-automated process of screening abstracts and titles, ensuring a high degree of usability [ 24 ]. The remaining papers were screened using this software for their title and abstracts, following the inclusion and exclusion criteria. The articles were screened by two impartial reviewers (N.A.M.Z. and M.R.S). In the next step, the reviewers evaluated the studies through a full article reading, assessed eligibility and proceeded with the data abstraction. The conflicts over data that arise between reviewers during the full-text, abstract, and/or data extraction processes were settled by consensus, with the involvement of a third reviewer if necessary.
Data extraction
The selected studies were categorized and analyzed according to the biomarker types, including biological age, lipid profile, inflammation, immune-senescence, energy regulation, oral health, and bone architecture. The authors, study design, nation in which the study was carried out, sample size, study population (case groups, control or comparison groups), follow-up duration, cancer treatment obtained, biomarkers or assessment used, and interest outcome are among the data that were collected.
Quality assessment
Two reviewers (N.A.M.Z. and M.R.S) independently completed a quality review for each selected article using the National Institutes of Health (NIH) checklist for observational cohort and cross-sectional studies [ 25 ]. The NIH quality assessment consists of a series of 14 questions comprised of the research question (Question 1), study population (Questions 2 and 3), groups recruited from the same population and uniform eligibility criteria (Question 4), sample size justification (Question 5), exposure assessed before outcome measurement (Question 6), sufficient timeframe to see an effect (Question 7), different levels of the exposure of interest (Question 8), exposure measures and assessment (Question 9), repeated exposure assessment (Question 10), outcome measures (Question 11), blinding of outcome assessors (Question 12), follow-up rate (Question 13), statistical analyses (Question 14). For questions six and seven, the marks were automatically marked as “no” for cross-sectional and case-control studies [ 25 ]. The minimum score of the NIH quality assessment was 0, and the maximum score was 14. After answering a series of 14 questions, the quality of each study was reported as poor, fair, or good. Quality was rated as ‘0’ for poor (0-4 marks), ‘I’ for fair (5-10 marks), or ‘II’ for good (11-14 marks) [ 26 ]. Figure 1 shows the quality ranking of cross-sectional studies across different criteria with green circles for “yes”, red for “no” and yellow and blue circles for “not reported”, and “not applicable”, respectively. The 26 selected articles had a fair quality assessment, meanwhile one had poor quality assessment.
RESULTS
Study selection
The PRISMA was utilized to report this systematic review process, encompassing identification, screening, and inclusion of studies ( Fig. 2 ). Three main databases, PubMed, Web of Science, and Scopus, were used to search the articles, resulting in 6,520 articles. After removing 873 duplicates, 5,647 articles were screened. The initial screening was conducted by thoroughly reviewing the abstracts of these 5,647 articles, and studies involving animal studies (n = 368), irrelevant topics (n = 1,540), inappropriate design based on inclusion criteria (n = 1,455), and non-eligible populations (n = 1,850) were excluded, resulting in 5,213 articles excluded in this phase. Next, 434 articles were sought for retrieval and then assessed for eligibility upon successful retrieval of full papers. From here, a total of 284 articles were excluded due to the non-eligible population, and another 130 articles were excluded due to the inappropriate design based on the inclusion criteria. This left 20 reports deemed eligible and included in the systematic review. Additional searches on the website (Google Scholar) and citation searching added 7 more articles, resulting in 27 articles in the review.
Characteristics of included studies
This systematic review included 27 studies involving a total of 2,641 cancer survivors and 2,308 healthy individuals. The study was conducted across various countries, including eight in China [ 9 , 16 , 27 - 32 ], five in India [ 7 , 11 , 12 , 33 , 34 ], three in the United States of America (USA) [ 5 , 6 , 35 ], two in Iran [ 36 , 37 ], one each in Egypt [ 38 ], Italy [ 39 ], Greece [ 40 ], Canada [ 10 ], Thailand [ 41 ], Japan [ 42 ], Russia [ 43 ], and another study conducted in multiple countries of Chile, Colombia, Costa Rica, Mexico, and Brazil [ 17 ]. One study did not mention the study location as the authors used secondary data of the GSE121248 dataset, which was directly downloaded from the Gene Expression Omnibus (GEO) database ( https://www.ncbi.nlm.nih.gov/gds/ ) [ 44 ].
The included study design consisted of 14 cross-sectional [ 5 , 6 , 11 , 12 , 17 , 27 , 28 , 32 , 33 , 37 , 39 , 41 - 43 ], 11 case-control [ 7 , 9 , 10 , 16 , 29 , 30 , 34 - 36 , 38 , 40 ], and one study employed machine learning modelling [ 31 ]. The enrolled cancer survivors in studies comprised 12 studies conducted among cancer treatment-naive patients [ 6 , 9 , 12 , 17 , 27 - 30 , 34 , 35 , 38 , 42 ], five studies among post-surgery [ 5 , 33 , 36 , 37 , 41 ], one study among post-chemotherapy [ 16 ], one study among a combination of pre- and post-chemotherapy [ 7 ], one study experienced genetic testing on the BRCA1 mutation [ 39 ], and one study on the combination of treatment-naive and post-chemotherapy [ 40 ], one study among post-chemotherapy and radiotherapy [ 10 ], one study among post-surgery and chemotherapy survivors [ 44 ], and four studies did not mention the cancer treatment status among their study population [ 11 , 31 , 32 , 43 ]. Survivors come from various primary cancer types, including oropharyngeal, gastric, breast, ovarian, acute myeloid leukemia, oral, hepatocellular, gastrointestinal stromal, lung, chronic lymphocytic leukemia, and osteosarcoma. Next, the authors utilized various types of biomarkers and assessments in their study.
Firstly, 15 studies of biological age were assessed with DNA methylation, long non-coding RNA (lncRNA), collagen type VI alpha 3 chain (COL6A3), serpin family H member 1 (SERPINH1), PLEKHG1, DNA methylation-epigenetic clock, telomerase reverse transcriptase (TERT) gene polymorphisms, serum insulin-like growth factor (IGF)-1, serum GDF-15, and microRNA (miRNA) expression. Next, five studies on the lipid profile used HDL, a total cholesterol level, a low-density lipoprotein level (LDL), an MDA level, serum apolipoprotein C-1 (ApoC-1), and phospholipid metabolism-related genes; three studies on inflammatory markers of CRP, interleukin (IL), TNF-α, IFN-Î 3 ; one on immune-senescence assessing Cellular Communication Network Factor 3 (CCN3) protein; two studies on energy regulation was measured using serum leptin, adiponectin and mitochondrial function profiles; one study on oral health identifying dysbiosis of oral microbiota; and lastly one study on bone architecture assessing oxidative stress and total oxidant status as bone densitometry indicators. Table 2 summarizes the data extraction from the selected articles, including authors, publication years, study locations, study populations, follow-up duration, types of cancer treatments, biomarkers, methods, and overall findings. The effects of aberrant DNA methylation and abnormal expression levels for each of the biomarkers toward aging phenotypes are summarized in Table 2 .
Metabolic and biological processes
This review encompasses an observational difference on metabolic and biological processes between cancer survivors and the healthy population, including biological age, lipid profile, inflammatory markers, immune senescence, energy regulation, oral health, and bone architecture. All 15 studies reported aberrant DNA methylation (hypermethylation and hypomethylation) [ 5 - 7 , 31 , 35 , 36 , 38 , 39 ] and abnormal expression levels of genes [ 9 , 27 - 30 , 33 , 40 ] related to biological age biomarkers among cancer survivors compared to controls. Furthermore, regarding lipid profile status, two studies found low total cholesterol, LDL, and HDL [ 10 , 11 ]; one study reported higher MDA levels [ 12 ]; one study found lower serum lipid profile and ApoC-1 [ 37 ]; and one study found an imbalance of phospholipid metabolism-related genes among cancer survivors compared to their controls [ 44 ].
Next, two studies found high inflammatory markers of CRP, ILs, and TNF-α [ 16 , 17 ]; meanwhile, one study reported low levels of TNF-Î among cancer survivors [ 32 ]. A study investigating immune senescence through the use of CCN3 protein indicated increased levels of CCN3 among cancer survivors [ 34 ]. In contrast, another study revealed that cancer survivors exhibited lower levels of leptin, adiponectin, and mitochondrial function profiles (mitochondrial mass and mitochondrial biogenesis), suggesting energy deficiency when compared to a healthy population [ 12 , 41 ]. Table 2 indicates a total of 28 articles; however, the precise number of articles selected for this review was 27. This is due to one author addressing both lipid profile and energy regulation [ 12 ].
DISCUSSION
The discussion section is divided into several subtopics comprising the lipid profile, energy regulation, epigenetic biological age, inflammation, immune-senescence and oral health status. To the best of our knowledge, this is the first systematic review that synthesized the observational differences on metabolic and biological processes between cancer survivors and the healthy population. Comparison analysis on several aging biomarkers has been observed in past studies, including biomarkers of lipid profile, energy regulation, biological age, inflammatory markers, immune senescence, oral health and bone architecture. The current review also highlights each biomarker’s function and alteration linked to potential aging phenotypes decline among cancer survivors.
Lipid profile biomarkers
Firstly, low levels of total cholesterol, LDL, HDL, and ApoC-1, together with high levels of MDA, were found among cancer survivors compared to controls [ 10 - 12 , 37 ]. Interestingly, despite the differences in cancer treatment history across included survivors in this review, three studies reported that cancer survivors had undergone cancer treatments (surgery, chemotherapy, and radiotherapy) [ 10 , 37 , 44 ]. One study enrolled survivors who did not receive any treatments [ 12 ], with the same pattern of lipid profile alteration observed. These findings suggest that the lipid profile may not only be affected by cancer treatments, but also by cancer itself. Cancer itself has been associated with the abnormal protein composition, oxidative stress environment and liver function, which may promote lipogenic phenotypes and metabolism alteration [ 10 ]. In another study, it was found that paclitaxel-based and anthracycline-based chemotherapy significantly worsened dyslipidemia among cancer survivors [ 45 ].
While LDL reduction is beneficial for atherosclerotic cardiovascular disease prevention [ 46 ], low levels of HDL are commonly related to a high risk of obesity, hypertension, and dyslipidemia [ 47 ]. Additionally, elevated levels of MDA, as an oxidative stress biomarker in lipid, potentially contribute to increased cardiovascular risk and atherosclerosis [ 48 ]. The imbalanced expression of phospholipid metabolism-related genes was also observed among cancer survivors, which may lead to neurodegenerative diseases [ 49 ] and body composition decline [ 50 ].
Energy regulation biomarkers
Furthermore, in comparison to healthy controls, studies found energy regulation disruption among cancer survivors. One study including post-surgery survivors and another one including treatment-naive survivors showed significantly reduced levels of leptin, adiponectin [ 12 ] and mitochondrial function profiles of mitochondrial mass and biogenesis [ 41 ]. It is possible that the post-surgical stress response may have caused hormonal and metabolic changes faced by cancer survivors [ 51 ]. The cancer also induces mitochondrial dysfunction, suggesting that the decrease in mitochondrial mass may be impacted by cancer-related metabolic alterations via the reactive oxygen species-mitophagy feedback loop [ 41 ]. Besides, it is plausible that the reduction of leptin and adiponectin could stem from the weight loss and a decrease in fat mass among cancer survivors [ 52 ]. Further, there was the reduction of leptin (3.62 ± 1.11 ng/mL vs. 9.13 ± 2.96 ng/mL) and adiponectin (8.85 ± 3.06 ng/mL vs. 18.71 ± 2.06 ng/mL) together with weight loss based on body mass index (BMI; 18.13 ± 1.97 kg/m 2 vs. 21.82 ± 1.93kg/m 2 ), among cancer survivors as compared to controls [ 12 ].
Leptin is essential for controlling body weight and energy homeostasis [ 53 ]. Adiponectin helps preserve the equilibrium of lipid and carbohydrate metabolism [ 54 ], whereas the mitochondrial function profiles are used in energy production, regulation of metabolism, cellular adaptation, and oxidative stress management. It has also been stated that these hormone imbalances affect an individual’s phenotype and are potentially linked to several metabolic disorders, such as dyslipidemia and hypertriglyceridemia, as well as severe obesity brought on by hyperphagia, impaired satiety signal in the brain, insulin resistance, abnormal glucose tolerance, hepatic steatosis, cardiovascular disease, sleep apnea, and diabetic retinopathy [ 13 - 15 ]. The observed imbalance between leptin, adiponectin, and mitochondrial profiles in cancer survivors implies that their risk of these consequences is higher than that of the general population.
Epigenetic biological age biomarker
A common aging biomarker for determining biological age refers to epigenetic modulation of DNA methylation and gene expression status. The primary mechanism of DNA methylation is the covalent addition of a methyl group to the carbon-5 atom of cytosine in a cytosine-guanine (CpG) dinucleotide. Aberrant DNA methylation among cancer survivors may result from the loss of CpG methylation in cancer cells, which includes low-density CpG site areas, repeat elements, retrotransposons, and laminin-associated domains [ 55 ]. Additionally, the cancer cells also exhibit disrupted gene expression via genetic mutations and transcriptional dysregulation, as well as epigenetic changes [ 56 ].
An increase in oxidative stress and DNA damage brought on by cancer treatments also possibly leads to DNA methylation and gene expression disruption [ 57 ]. A study conducted with older adult cancer survivors revealed chemotherapy significantly linked to abnormal gene expression [ 40 ]. In contrast, another study found that chemotherapy was not statistically significantly associated with abnormal DNA methylation among cancer survivors [ 7 ]. The possible explanation for such disparity might be attributed to the younger age of survivors included in the latter study [ 7 ] compared to older adults examined in the study by former one [ 40 ]. It is speculated that older adult cancer survivors tend to be less resilient towards the toxicities linked to chemotherapy drugs, as well as high doses of radiation [ 58 ]. As mentioned above, abnormal DNA methylation and gene expression are potentially affected by cancer and its treatments, which further disrupts the specific genes’ function and may impair aging phenotypes.
For instance, a previous study revealed that the Erythrocyte Membrane Protein Band 4.1 Like 3 (EPB41L3) gene was hypermethylated, rendering it dysfunctional and acting as a predictor of aging and cancer metastasis [ 8 ]. It has been determined that EPB41L3, a member of the FERM domain-containing 4.1 family, is essential for cytoskeleton-associated functions including intracellular transport, signal transduction, and cytoskeletal rearrangement. This gene’s malfunction causes a decrease in E-cadherin expression, disrupted intercellular adhesion, loss of polarity, elevated individual motility, and metastasis of cancer [ 59 ]. These cellular disturbances are possibly linked to aging because they result in a diminished capacity for regeneration, heightened fragility, and diminished tissue elasticity, which are indicative of alterations brought on by aging [ 60 ]. Together, these observations highlight not only the role of EPB41L3 in cancer progression more broadly within an aging framework, but also how its dysregulation may intricately change with age.
Apart from this, hypermethylation of DAPK1 has been found among cancer survivors [ 7 ]. DAPK1 participates in multiple signaling pathways that could regulate autophagy, apoptosis, caspase-independent cell death, cell adhesion, oxidative stress, and neurodegeneration [ 61 ]. Dysregulation of this tumor suppressor gene due to hypermethylation of DAPK1 has been linked to the emergence of neurological disorders (including stroke) [ 8 ]. In addition, the fact that this gene was found to be upregulated in Alzheimer’s patients suggests that cognitive decline and motor deficits were brought on by high levels of DAPK1 expression [ 19 ]. Also, overexpression of DAKP1 tau phosphorylation at several Alzheimer’s disease-related locations may contribute to tau protein stability [ 62 ]. These findings suggest potential association between DAPK1 abnormal DNA methylation with decline in cognitive phenotypes among cancer survivor populations.
Furthermore, hypermethylation on the P16INK4a gene, known as the tumor suppressor gene suggests an increase in the risk of cancer, which may reflect an accelerated cellular senescence [ 63 ]. Apart from this, in light of its critical involvement in the development of organ and structural development (such as heart, brain, limbs, and teeth) [ 64 ], the aberrant DNA methylation on the Short Stature Homeobox 2 (SHOX2) gene suggests an association of an increased risk of age-related cardiovascular diseases and heart function with cognitive decline [ 65 ].
In addition, tumor suppressor genes BRCA1 and RAD51C are vital in maintaining genomic integrity through precise DNA repair by homologous recombination [ 66 ]. These genes also potentially play a significant role in the process of mitophagy, a specialized autophagy pathway that occurs in defective mitochondria and is important for mitochondrial quality control [ 67 ]. Cancer chemotherapy treatment has been reported to inhibit autophagy in BRCA1-deficient cells, rendering them sensitized to chemotherapeutic drugs. This might partially contribute to the cognitive impairment among cancer survivors as compared to the healthy population. Furthermore, the instability of these genes can cause various age-related diseases, including degenerative conditions and cancer [ 68 ]. Taken together, BRAC1 and RAD51C are essential for DNA repair, and aberrant DNA methylation may disrupt their function, which potentially increases cancer risks and manifestation of aging-related phenotypes.
Aberrant methylation on cell-free DNA (cfDNA) has also been proposed as a potential influence on the cancer survivor’s aging phenotypes and may reflect accelerated aging [ 69 ]. cfDNA originates from necrotic and apoptotic cells and plays a crucial role in both maintaining the stability of chromatin dynamics and physiological homeostasis [ 33 , 34 ]. A previous study found the aberrant DNA methylation on cfDNA among hepatocellular carcinoma survivors compared to their counterparts, suggesting potential disruption in chromatin stability and homeostasis [ 34 ]. This scenario might be explained by the impacts of aberrant DNA methylation on cfDNA, as changes in DNA methylation affect the chromatin structure and nucleosome positioning, which is crucial for maintaining genomic integrity [ 70 ]. However, aberrant DNA methylation of cfDNA has been reported to lead to neurogenerative diseases, including Alzheimer’s disease and frontotemporal dementia [ 69 ]. Taken all together, these findings underscore the various roles of balanced cfDNA regulation, which may serve as a potential diagnostic tool for age-related diseases and cancer prevention. Furthermore, compared to their counterparts, cancer survivors commonly showed an expression issue related to lncRNAs, according to a prior study [ 26 ].
Numerous disorders, including cardiovascular, cerebrovascular, malignant, and neurogenerative diseases, as well as hyperphosphorylation of the tau protein, oxidative stress, neuroinflammation, mitochondrial dysfunction, and autophagy, might be brought on by this imbalance and disorder of lncRNA expression, particularly PCSK2-2:1 [ 71 ]. This is due to the possibility that unbalanced lncRNA expression may reduce a neuron’s capacity to withstand oxidative stress or other harmful substances, making it more susceptible to neurodegenerative illnesses and cognitive decline [ 72 ].
In addition, prior research has suggested a connection between sarcopenia and cognitive impairment with high levels of COL6A3 and SERPINH1 [ 65 , 73 ]. High levels of COL6A3 may affect the muscle tissue, resulting in sarcopenia, impaired muscle regeneration, and increased fat infiltration [ 74 ]. Further, elevated levels of SERPINH1 and COL613 in the brain can affect the remodeling of the extracellular matrix, which may result in neurodegenerative illnesses, including Alzheimer’s and cognitive decline [ 75 , 76 ]. Besides that, hypermethylated miR-125b might contribute to cellular senescence, and this process is often marked by an increase in senescence-associated beta-galactosidase (SA-β-gal) activity [ 77 ].
Moreover, abnormal TERT gene expression may also reflect the age acceleration among cancer survivors due to the progressive shortening of the telomere length [ 78 ]. TERT is vital in maintaining telomeres and helps prevent chromosomal end degradation during cell division [ 78 ]. Moreover, several studies have demonstrated that shorter telomeres are associated with a greater likelihood of developing Alzheimer’s onset, dementia, and autophagy, resulting in cognitive impairment [ 79 , 80 ], and an increase in BMI and subcutaneous obesity [ 81 ]. Additionally, telomere dysfunction led to cell death by apoptosis or autophagy in some cell types [ 82 ]. These findings may explain the greater likelihood of cognitive decline among cancer survivors as compared to the healthy population, potentially associated with accelerated telomere length shortening.
Abnormal expression of IGF among cancer survivors compared to the healthy population has been reported [ 30 , 40 ]. IGF-1 influences metabolism and tissue growth, may promote bone health, and has a role in preventing osteoporosis [ 83 ]. A previous study conducted Japanese adults found abnormal expression of low levels of IGF-1 among cancer survivors as compared to their counterparts [ 84 ]. While IGF-1 levels tend to decline due to age and metabolic disorders [ 85 ], several studies reported that cancer and its treatments may also reflect the disruption of regulatory mechanisms that control the expression of IGF, potentially leading to aberrant IGF-1 expression [ 86 ]. Abnormal levels of IGF-1 can be attributed to obesity, insufficient growth hormone (GH) production, nutritional deficiencies, and age, in which IGF-1 declines with aging [ 86 ]. These findings may explain the disparities in findings on the expression level of IGF-1 which might be due to various factors, including cancer and its treatment, nutritional status, metabolic and aging-related causes in altering the IGF-1 levels, etc. Furthermore, low levels of IGF-1 have been suggested to be associated with several health issues (including cardiovascular diseases), whilst excessive levels may lead to disorder conditions such as acromegaly or gigantism due to abnormal and excessive GH production [ 87 , 88 ]. This suggests that maintaining IGF-1 levels within the normal middle range is necessary for longevity and overall health, as well as normal bone growth.
GDF-15 employed as a biomarker in determining biological age plays a vital role in individual phenotypes. A study reported abnormal expression levels of GDF-15 among treatment-naive cancer survivors as compared to the healthy population [ 9 ]. Excessive levels of GDF-15 have been associated with age-related diseases, including metabolic disorders, cognitive impairment, and cardiovascular diseases [ 89 , 90 ].
Additionally, epigenetic clocks (such as Horvath, Levine, DNAm PhenoAge, Yang, and Lin) have been widely implemented to determine biological age, as these clocks are considered accurate biomarkers in determining an individual’s biological age [ 91 ]. The excellent accuracy of this epigenetic clock has been extensively confirmed by independent datasets, and Horvath’s clock is the first multi-tissue age estimator that can accurately calculate age utilizing DNA from numerous sources of cells, tissues, and organs (except sperm) throughout the lifespan [ 92 ]. A study that employed different epigenetic clocks and DNA methylation-based aging biomarkers found that cancer survivors had a notable acceleration of age in comparison to their non-survivor counterparts [ 5 ]. In line with earlier research, which compared the epigenetic age of breast cancer tissue to normal tissues, breast cancer tissue was shown to have a greater epigenetic age [ 93 ]. This showed that, in comparison to the healthy population, cancer survivors’ ages may reflect accelerated aging. However, the findings on biological age need to be taken cautiously because, in addition to cancer therapy, age, sex, BMI, and lifestyle factors (such as diet and smoking) can also impact the aging biomarkers [ 94 ]. Therefore, to assess the precise impact of cancer and its therapy, which focuses on reducing age acceleration, these variables must be taken into consideration when executing comparative analysis and interpreting these aging indicators among cancer survivors and healthy populations.
Another biomarker used to determine biological age is miRNAs [ 33 ]. Non-coding RNA molecules of miRNAs play a critical role in gene expression regulation, cell proliferation, immune response, differentiation, and regulating metabolic pathways [ 95 ]. Interestingly, the expression level of miR-221, miR-222, miR-494 and miR-34a was significantly down-regulated in tumors relative to non-tumor tissues [ 33 ]. The imbalance expression of these specific miRNAs may reflect accelerated aging by promoting autoimmune diseases such as psoriasis, systemic lupus erythematosus, and rheumatoid arthritis [ 96 ]. Additionally, it is plausible that the abnormal expression of miRNAs disrupts cardiac and synaptic function, suggesting a potential contribution to myocardial infarction [ 97 ] and cognitive decline [ 98 ] among cancer survivors. Hence, these findings underscore the role of miRNAs as potential biomarkers in assessing accelerated aging and as a key regulator of cellular homeostasis.
Inflammatory biomarkers
Next, chronic systemic inflammation suggests an association with chronic age and impaired aging phenotypes. A systematic review of the association between inflammatory markers and age-related diseases found that elevated levels of IL-6, TNF-α, and IL-8 are linked to chronic inflammation, such as age-related diseases of metabolic disorders and cardiovascular diseases, frailty, sarcopenia, and cognitive decline [ 99 ]. Moreover, elevated levels of CRP and IL biomarkers have also been suggested to be associated with an increased risk of cancer [ 100 ]. It is plausible that cancer and its treatment may be associated with a long-term chronic inflammatory response, which leads to abnormal levels of inflammatory markers [ 101 ]. Chen et al. [ 16 ] reported abnormal inflammatory markers among post-chemotherapy cancer survivors in a case-control study. In contrast, cross-sectional studies by Gao et al. [ 32 ] and Fontvieille et al. [ 17 ] observed similar alterations among treatment-naive survivors. These findings may reflect both cancer and its treatment effects towards the alteration of inflammation among cancer survivors.
Apart from that, cancer survivors often experience comorbidities potentially due to increased inflammation, including obesity, metabolic disorders, cardiovascular diseases and rheumatoid arthritis [ 102 - 104 ]. Rheumatoid arthritis is a chronic systemic autoimmune disease that eventually leads to cartilage and bone destruction and joint dysfunction [ 105 ]. In addition, sedentary behavior or lifestyle changes, due to fatigue, after cancer treatment, might also contribute to elevated levels of inflammatory markers [ 106 ]. These findings suggest that the disparities in inflammatory markers between cancer survivors and healthy populations might be attributed to the long-term chronic inflammation induced by cancer and its treatment, as well as associated with lifestyle changes and comorbidities.
Immuno-senescence biomarker
Immuno-senescence has also been classified into metabolic and biological processes using cellular communication network (CCN) protein as a biomarker. The CCN family of scaffolding proteins may regulate and govern the connections between various signaling pathways. Recent studies have shown that CCN proteins influence the function of hematopoietic stem cells and regulatory T cells, which are essential for immunological homeostasis and the response to inflammation [ 105 ]. Nonetheless, Siddiqui et al. [ 34 ] observed immune-senescence status and abnormal expression levels of CCN among untreated cancer survivors, as compared to the healthy population. These findings suggest the cancer itself may alter immune dysregulation, tumor metabolism and chronic inflammation, promoting upregulated CCN levels [ 107 ]. Several studies have shown a potential association between CCN abnormal expression and a higher risk of age-related disorders, including rheumatoid arthritis, systemic sclerosis (also known as an autoimmune disease), and neurodegenerative diseases [ 18 , 108 ].
Overall, the discrepancies in CCN expression between cancer survivors and healthy populations underscore the complex interplay between cancer and immune system dynamics, warranting further investigation into therapeutic strategies that could mitigate these effects and improve cancer survivors’ outcomes.
Oral and bone health
Finally, unhealthy oral and bone health among cancer survivors has been reported. It was found that the Bacilli class, Streptococcic family, Streptococcus genus, Firmicutes phylum , and Lactobacillus’s order were more abundant among treatment-naïve cancer survivors as compared to their counterparts [ 42 ]. These dysbiosis are possibly associated with the cancer and gut microbiome shifts, which increase systemic inflammation and immune dysregulation, thereby causing unhealthy oral and bone health among cancer survivors [ 109 ]. Besides, cancer treatments such as chemotherapy, radiotherapy and immunotherapy may further disrupt the microbiome balance via immune dysregulation, mucosal injury and direct cytotoxicity, suggesting the impacts of cancer therapies towards oral and bone health [ 109 ].
The dysbiosis of the microbiome may negatively impact oral hygiene, including tonsillopharyngitis and bad breath; and systemic diseases such as neurological, cardiovascular, autoimmune and gastrointestinal diseases [ 110 ]. Further, earlier studies found that the cancer survivors with chronic lymphocytic leukemia and osteopenia showed signs of oxidative stress and total antioxidant status reduction in serum and bone tissue [ 43 ]. These findings suggest that elevated levels of oxidative stress and concomitant reduction of antioxidants can impair bone matrix and bone formation, potentially leading to osteopenia among cancer survivors [ 111 ]. The interplay among microbial dysbiosis, oral health, oxidative stress, antioxidant levels, and bone health warrants the need for strategies to mitigate these negative effects towards cancer survivors’ quality of life.
CONCLUSION
The accelerated aging among cancer survivors might be associated with a poor prognosis before or after receiving cancer treatment. Notably, the metabolic and biological disruption pattern was comparable among short- and long-term survival or follow-up durations across all types of cancers. This systematic review reveals that cancer survivors experience considerable metabolic and biological changes, which may reflect accelerated aging relative to healthy counterparts, as summarized in Figure 3 . Cancer survivors have abnormal metabolic and biological processes, including abnormal levels of lipid profiles, DNA methylation profiling (hypomethylation or hypermethylation), immune-senescence, energy regulation, excessive inflammation, and poor oral and bone health. These aberrant metabolic and biological processes are often associated with age-related pathologies such as cardiovascular diseases, cognitive decline (including Alzheimer’s and dementia), and metabolic and psychological disorders. Thus, aging may not only be an inevitable process for cancer survivors but also the long-term health outcomes of cancer survivors being even more challenging.
Moreover, this review highlights uncertain issues in tackling the intricate relations between cancer treatments and aging phenotypes, implying approaches that address the intricate processes leading to age-related declines among cancer survivors, compared to the healthy population. Future research should prioritize longitudinal studies examining cancer and its treatment-specific aging effects, biomarker trajectories across survivorship phases, and lifestyle interventions targeting modifiable aging risks to develop personalized strategies for improving long-term outcomes. Finally, the review underscores the importance of the complex interplay between cancer treatments and aging phenotypes, suggesting the need for tailored interventions aimed at mitigating age-related decline in this vulnerable population.
FUNDING
This research was supported by the Ministry of Higher Education (MOHE) Malaysia through the Fundamental Research Grant Scheme (FRGS/1/2021/SKK06/UKM/02/10).
CONFLICTS OF INTEREST
No potential conflicts of interest were disclosed.
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