Exploring the evolving role of apolipoproteins in oncology: global trends and emerging frontiers.

Introduction

The recognition of metabolic reprogramming as a hallmark of cancer has sparked a surge of interest in oncology research (Faubert et al. 2020 ; Ward and Thompson 2012 ; Jin et al. 2023 ). Central to this growing focus is the study of lipid metabolism (Bian et al. 2021 ). Enhanced lipid synthesis and storage, a key feature of metabolic reprogramming, is now recognized as a hallmark of malignancy (Cheng et al. 2018 ). Furthermore, the uptake of free fatty acids by CD36 + cancer cells can initiate a metastasis cell subpopulation, which gets energy from the fatty acid oxidation (FAO) pathway (Cao 2019 ). As evolutionarily conserved mediators of lipid transport and metabolism (Wong et al. 2022 ), apolipoproteins have long been recognized for their role in cardiovascular diseases (Emerging Risk Factors Collaboration, 2009 ; Kastelein et al. 2008 ). However, mounting evidence suggests that apolipoprotein dysregulation is also associated with several types of malignancies, including breast (Yiu et al. 2010 ; Cedó et al. 2019 ; Chang et al. 2005 ), lung (Ko et al. 2014 ; Shi et al. 2018 ), and prostate (Ifere et al. 2013 ; Bancaro et al. 2023 ; Hall et al. 2004 ) cancers. This linkage occurs through various pathways, including autophagy modulation (Yang et al. 2024a ; Levy et al. 2017 ), oxidative stress regulation (Morry et al. 2017 ), and chemoresistance development (Yiu et al. 2010 ; Wan et al. 2024 ). By unraveling the complex interplay between apolipoprotein-mediated lipid regulation and oncogenic signaling pathways, researchers can identify novel biomarkers and design targeted strategies that may potentially improve patient outcomes.

Key knowledge gaps in apolipoprotein research include the evolution of conceptual frameworks, geographic disparities, and the disconnect between mechanistic studies and clinical applications. A systematic bibliometric approach is necessary to map these gaps, utilizing publication data, citation networks, and collaboration dynamics to identify trends and emerging hotspots (Hicks et al. 2015 ; Jiang et al. 2023 ). This approach offers a more efficient and objective way to navigate the vast and complex literature, overcoming the limitations of traditional manual reviews (Agarwal et al. 2016 ; Donthu 2021 ). Although several publications have reported links between cancer and specific apolipoproteins, a comprehensive investigation that fully integrates these two areas is still missing. An additional innovation of our study lies in its combined bibliometric and bioinformatic approach, which aimed to build a knowledge map, explore underlying molecular mechanisms, and establish a theoretical basis for future multimodal diagnostic strategies and intervention frameworks.

Materials and methods

Data source and literature search strategy

Web of Science (WoS), which encompasses over 12,000 academic journals, is widely regarded as an excellent resource for bibliometric research due to its unique citation indexing system, which allows for precise tracking of scholarly influence across a broad range of disciplines (Yeung 2019 ). Relevant articles in the Web of Science Core Collection (WoSCC) were searched and exported on October 11, 2024, and the only selected edition is the Science Citation Index Expanded (SCI-EXPANDED), using the following keywords and criteria: TS = (“Apolipoprotein” OR “Apolipoproteins” OR “APOA” OR “APOA1” OR “APOA-I” OR “APOA2” OR “APOA-II” OR “APOA5” OR “APOA-V” OR “APOB” OR “APOB-100” “APOB-48” OR “APOC” OR “APOC1” OR “APOC-I” OR “APOC2” OR “APOC-II” OR “APOC3” OR “APOC-III” OR “APOD” OR “APOE” OR “APOE2” OR “APOE3” OR “APOE4” OR “APOL” OR “APOL1” OR “APOL-I” OR “APOM”) AND TS = (“Tumors” OR “Neoplasia” OR “Neoplasias” OR “Neoplasm” OR “Tumor” OR “Cancer” OR “Cancers” OR “Malignant Neoplasm” OR “Malignancy” OR “Malignancies” OR “Malignant Neoplasms” OR “Neoplasm, Malignant” OR “Neoplasms, Malignant” OR “Benign Neoplasms” OR “Neoplasms, Benign” OR “Neoplasm, Benign” OR “Benign Neoplasm” OR “Hematologic Neoplasms” OR “Hematologic Malignancies” OR “Blood Neoplasms” OR “Blood Cancer” OR “Blood Cancers” OR “Leukemia” OR “Leukemias” OR “Lymphoma” OR “Lymphomas” OR “Myeloma” OR “Multiple Myeloma” OR “Hodgkin Disease” OR “Non-Hodgkin Lymphoma” OR “Acute Lymphoblastic Leukemia” OR “Chronic Lymphocytic Leukemia” OR “Acute Myeloid Leukemia” OR “Chronic Myeloid Leukemia” OR “Oncology” OR “Phymatology”) The document types were limited to “article” or “review”. The language was limited to English.

The bibliometric analysis was established by systematically removing duplicates using reference management software EndNote 20, yielding 977 non-redundant publications. Two primary researchers (DZT and ZYY) independently conducted blinded screening based on predefined exclusion criteria, which resulted in the removal of non-oncological studies ( n = 465, 47.6%) and non-apolipoprotein functional research (n = 102, 10.4%). This process yielded 410 eligible records, reflecting a 42.0% retention rate. 29 records cause discrepancies between DZT and ZYY (3.0% of the initial dataset), all of which were successfully resolved under the supervision of the principal investigator (LZ).

WoS is a multidisciplinary database, whereas PubMed, originating from the United States National Library of Medicine (NLM), focuses on clinical aspects. Consequently, we employed PubMed as an addition, enabling federated analysis across multiple databases. PubMed is particularly well-suited for aggregating clinical research data, offering comprehensive access to clinical trials, which serves as a great supplement to the WoS, thereby facilitating the synthesis of evidence in clinical fields. A systematic search for apolipoprotein-related cancer research using the Clinical Trial filter in PubMed was conducted to assess the progress of clinical research, resulting in 25 eligible studies. All studies were rigorously screened for relevance to the application of apolipoproteins in cancer research, and eventually, 10 clinical trials were selected for display. All these 10 studies show the relationship between at least one type of apolipoprotein with cancer. Detailed criteria: ((“Apolipoprotein” [Title/Abstract] OR “Apolipoproteins” [Title/Abstract] OR “APOA” [Title/Abstract] OR “APOA1” [Title/Abstract] OR “APOA-I” [Title/Abstract] OR “APOA2” [Title/Abstract] OR “APOA-II” [Title/Abstract] OR “APOA5” [Title/Abstract] OR “APOA-V” [Title/Abstract] OR “APOB” [Title/Abstract] OR “APOB-100” “APOB-48” [Title/Abstract] OR “APOC” [Title/Abstract] OR “APOC1” [Title/Abstract] OR “APOC-I” [Title/Abstract] OR “APOC2” [Title/Abstract] OR “APOC-II” [Title/Abstract] OR “APOC3” [Title/Abstract] OR “APOC-III” [Title/Abstract] OR “APOD” [Title/Abstract] OR “APOE” [Title/Abstract] OR “APOE2” [Title/Abstract] OR “APOE3” [Title/Abstract] OR “APOE4” [Title/Abstract] OR “APOL” [Title/Abstract] OR “APOL1” [Title/Abstract] OR “APOL-I” [Title/Abstract] OR “APOM” [Title/Abstract])) AND ((“Tumors” [Title/Abstract] OR “Neoplasia” [Title/Abstract] OR “Neoplasias” [Title/Abstract] OR “Neoplasm” [Title/Abstract] OR “Tumor” [Title/Abstract] OR “Cancer” [Title/Abstract] OR “Cancers” [Title/Abstract] OR “Malignant Neoplasm” [Title/Abstract] OR “Malignancy” [Title/Abstract] OR “Malignancies” [Title/Abstract] OR “Malignant Neoplasms” [Title/Abstract] OR “Neoplasm, Malignant” [Title/Abstract] OR “Neoplasms, Malignant” [Title/Abstract] OR “Benign Neoplasms” [Title/Abstract] OR “Neoplasms, Benign” [Title/Abstract] OR “Neoplasm, Benign” [Title/Abstract] OR “Benign Neoplasm” [Title/Abstract] OR “Hematologic Neoplasms” [Title/Abstract] OR “Hematologic Malignancies” [Title/Abstract] OR “Blood Neoplasms” [Title/Abstract] OR “Blood Cancer” [Title/Abstract] OR “Blood Cancers” [Title/Abstract] OR “Leukemia” [Title/Abstract] OR “Leukemias” [Title/Abstract] OR “Lymphoma” [Title/Abstract] OR “Lymphomas” [Title/Abstract] OR “Myeloma” [Title/Abstract] OR “Multiple Myeloma” [Title/Abstract] OR “Hodgkin Disease” [Title/Abstract] OR “Non-Hodgkin Lymphoma” [Title/Abstract] OR “Acute Lymphoblastic Leukemia” [Title/Abstract] OR “Chronic Lymphocytic Leukemia” [Title/Abstract] OR “Acute Myeloid Leukemia” [Title/Abstract] OR “Chronic Myeloid Leukemia” [Title/Abstract] OR “Oncology” [Title/Abstract] OR “Phymatology” [Title/Abstract])).

Software for bibliometric analysis

In this study, software such as R (Ihaka and Gentleman 1996 ) (version 4.2.2), VOSviewer (Eck and Waltman 2010 , 2007 ) (version 1.6.20), and CiteSpace (Chen 2004 , 2006 ) (version 6.4.R1) were utilized as tools for performing the bibliometric analysis. Bibliometrix (Aria et al. 2016 ) R package (version 4.1.4) was used to calculate the frequency of collaboration between countries. Based on VOSviewer, we calculated the number of publications, citations, and keyword frequency. Co-occurrence networks of keywords from scientific literature were constructed and visualized using the software’s embedded clustering algorithm. This tool primarily focuses on co-authorship and co-occurrence analysis. If keywords exhibited similar meanings, they were merged using VOSviewer. VOSviewer and the Bibliometrix R package were used to analyze country, institution, and author collaborations (Eck and Waltman 2010 ). This study used CiteSpace to identify keywords that gained significant citations during a specified period. The online bibliometrics website made our visualization of international collaborations possible ( https://bibliometric.com/ ). Microsoft Excel was used to analyze the exponential growth in the number of publications (Jiang et al. 2023 ).

Bioinformatics analysis

Protein–protein interaction (PPI) network analysis

The apolipoprotein-related genes and cancer-associated genes were retrieved from the GeneCards database (Stelzer et al. 2016 ) using search terms “apolipoprotein” and “cancer” (version 5.12, https://www.genecards.org/ , every protein is screened with a relevance score > 5). PPI networks were constructed using the STRING database (Kanehisa et al. 2010 ) with a high confidence level (version 12.0, https://string-db.org , confidence threshold = 0.9). Human-specific (Homo sapiens) interactomes were retrieved using the STRINGdb R package (version 2.10.0, https://bioconductor.org/packages/STRINGdb/ ) with Ensembl gene IDs, followed by the removal of disconnected nodes to optimize topological clarity. The resulting networks were imported into Cytoscape (Yu et al. 2012 ) (version 3.9.1) for advanced visualization and computational analysis. Centrality analysis was conducted via Cytoscape’s plugin cytoHubba Maximum Clique Centrality (MCC) algorithm (Chin et al. 2014 ) to identify hub genes and construct the sub-network. The Date of accession for all databases used in the PPI network analysis is September 29, 2025.

Functional and disease ontology enrichment analysis

Functional enrichment analysis of Gene Ontology (GO) terms (including Biological Process (BP), Molecular Function (MF), Cellular Component (CC)) and Kyoto Encyclopedia of Genes and Genomes (KEGG) (Kanehisa et al. 2010 ) pathways was performed using the R package clusterProfiler (version 4.4.4) (Yu et al. 2012 ). Gene symbols were converted to Entrez IDs (Maglott et al. 2007 ) via the org.Hs.eg.db annotation database (version 3.17.0, https://bioconductor.org/packages/org.Hs.eg.db/ ) to ensure compatibility with human (Homo sapiens) genomic resources. Enrichment parameters included a Benjamini-Hochberg (BH) adjusted p-value threshold of < 0.05 to control the false discovery rate, with hypergeometric testing applied to identify statistically overrepresented terms (Yu et al. 2012 ). For disease ontology (DO) enrichment, the DOSE package (Zamanian-Daryoush et al. 2013 ) (version 3.26.0) was used to calculate term similarities using the Jaccard index, followed by hierarchical clustering (Ward’s minimum variance method) to group related disease terms. Redundant terms were pruned at a tree height cutoff of 0.85. This operation is primarily intended to eliminate information redundancy caused by highly similar disease terms and to avoid the presence of duplicate or overly overlapping disease categories in the final enrichment results. While maintaining the integrity of core disease enrichment information, significantly enhance the conciseness and readability of the results to support the focused interpretation of key disease-associated pathways. Functional and disease enrichment results were visualized using ggplot2 (Wickham 2009 ) (version 3.4.4), with node sizes scaled to gene counts and color gradients mapped to the significance levels. All analyses were conducted in R (Ihaka and Gentleman 1996 ) (version 4.2.2) under standardized computational protocols to ensure reproducibility. The Date of accession for all databases used in functional and DO enrichment analysis is September 29, 2025. The flowchart of the whole analysis is shown in Fig. 1 . Fig. 1 An overview of the flowchart of the study. The flowchart of the research study on apolipoproteins in cancer, utilizing bibliometric analysis and bioinformatic analysis. GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; DO, Disease Ontology

Results

Overview of publication status

This study included 410 conventional articles on apolipoproteins in cancer, as shown in Fig. 1 . Figure 2 summarizes the annual and cumulative publication counts related to apolipoproteins in cancer. The number of publications about apolipoproteins began rising steadily in 2015 and peaked in 2017 ( N = 33). The relationship between cumulative publications and publication year was evaluated using an exponential growth function, which accurately matched the trend in cumulative publications (R 2 = 0.9699). Fig. 2 The number of publications per year and the cumulative number. The solid line represents the original data points of cumulative publication counts. In contrast, the dashed line represents the fitted curve based on an exponential function model ( y = 3.4688 e 0.1402 x , R. 2 = 0.9699, where y is the accumulated number of articles published, x is the published year)

Our bibliometric analysis of national/regional contributions revealed distinct geographical patterns in apolipoprotein-cancer research productivity. As illustrated in Fig. 3 A, China demonstrated a dominant scholarly output, with 218 publications, substantially exceeding those of the United States ( n = 91), Japan ( n = 24), and Sweden ( n = 18). The longitudinal analysis revealed a pivotal transition in 2013, when China’s publication volume surpassed that of the United States (US) and subsequently established sustained leadership within two years. Geospatial visualization (Fig. 3 C) further mapped the global contribution landscape, confirming China’s central role in productivity and international collaboration. Our network analysis (Fig. 3 B, D ) revealed that China maintains the most substantial collaborative ties with the United States (collaborative frequency = 20) and Sweden (frequency = 9). Fig. 3 The individualized involvement of each nation in the domain of apolipoprotein in cancer. A The ratio of publications of the top 10 countries each year; B The network map of countries’ cooperation; C The geographical visualization of the national publication, with darker colors denoting higher publication volumes; D . Cooperation frequency of the top 16 countries/regions

The bibliometric analysis also identified 657 global institutions. As delineated in Table 1 , Chinese institutions dominated the top contributors, representing 7 of the top 10 ranked organizations, followed by Sweden ( n = 2) and the United States ( n = 1). Sun Yat-sen University emerged as the most prolific contributor, producing 69 publications (335 total citations, h-index = 12), surpassing second-ranked Soochow University (31 publications, 58 citations). Co-authorship network analysis revealed 26 distinct collaboration clusters among institutions meeting the inclusion threshold of 5 publications. As illustrated in Fig. 4 B, early foundational work (2005–2015) centered on Western institutions, while post-2018 collaborations focused on Chinese research consortia. Temporal analysis revealed distinct research phases: pre-2012 scholarship was predominantly driven by Western institutions (e.g., Johns Hopkins School of Medicine ). At the same time, post-2012 saw exponential growth in Chinese output (Fig. 4 C), particularly from Sun Yat-sen University (Fig. 4 A). By 2020, geographic clustering became apparent, with southern Chinese institutions (e.g., Sun Yat-Sen University and Soochow University ) producing most of the national output. Table 1 The top 10 institutions that contributed to publications in the Apolipoprotein-cancer research Rank Institution Country Counts TCs Average TCs 1 Sun Yat Sen University China 69 335 4.86 2 Soochow University China 31 58 1.87 3 Shandong University China 28 6 0.21 4 Zhengzhou University China 15 21 1.4 5 Duke University US 15 16 1.07 6 Zhejiang University China 14 26 1.86 7 Guangxi Med University China 14 14 1 8 Lund University Sverige 13 105 8.08 9 Karolinska Institute Sverige 13 41 3.15 10 Wannan Med College China 13 12 0.92 TCs, total citations. Fig. 4 The contributions of organizations to apolipoprotein-cancer research. A The number of articles of each affiliation. B The knowledge map of the institutions (the lines represent cooperation relationships). C The knowledge map of the institution’s publication frequency by year

Analysis of journal and author impact

The investigation encompassed 410 articles from 104 journals. Table 2 delineates the top 10 journals, ordered by publication quantity, along with their latest 2024 impact factors (IF) (Garfield 1999 ). 2 out of the top 10 journals were in the first quartile (Q1) of the Journal Citation Reports (JCR). Table 3 delineates the top 10 co-cited journals. Table 2 The top 10 journals that contributed to publications in the field of apolipoprotein within cancer Rank Journal Counts TCs Average TCs IF JCR area 1 Molecular Medicine Reports 47 16 0.34 3.4 Q2 2 Oncotarget 28 38 1.36 – – 3 International Journal of Clinical and Experimental Pathology 26 5 0.19 1.1 Q4 4 Experimental Cell Research 16 6 0.38 3.3 Q3 5 International Journal of Cancer 12 43 3.58 5.7 Q1 6 American Journal of Translational Research 12 1 0.08 1.7 Q3 7 Cancer Research 11 45 4.09 12.5 Q1 8 BMC Cancer 11 31 2.82 3.4 Q2 9 Folia Biologica 11 3 0.27 1.1 Q3 10 Cancer Management and Research 10 28 2.8 2.5 Q3 TC, total citation; IF, impact factor; JCR, journal citation reports. Table 3 Top 10 co-cited journals associated with apolipoprotein in cancer Rank Journal TCs IF JCR area 1 Cancer Research 45 12.5 Q1 2 International Journal of Cancer 43 5.7 Q1 3 Oncotarget 38 0 0 4 BMC Cancer 31 3.4 Q2 5 Breast Cancer Research and Treatment 28 3.0 Q2 6 Cancer Management and Research 28 2.5 Q3 7 Oncotargets and Therapy 27 2.7 Q3 8 Medical Oncology 25 2.8 Q2 9 Oncogene 24 6.9 Q1 10 International Journal of Oncology 20 4.5 Q1 TCs, total citations; IF, impact factor; JCR, journal citation reports.

The collaborative relationships among researchers are depicted in Fig. 5 A. A total of 45 authors, each with 5 or more articles, were organized into 5 distinct clusters. The time-overlapping network of clustering outcomes is presented in Fig. 5 B. The number of articles and total citations per year was visualized in Fig. 5 C. We then analyzed the co-citation network, as shown in Table 4 . Eight of the top 16 co-cited authors came from China, followed by Norway, which has four authors in the line. Xu Ning has the most articles ( N = 7), and Baak, Jan P.A., has the highest H-index (H-index = 63). Heeneman Sylvia, Hoving Saske, and Stewart Fiona A. share the most extensive total citations ( N = 236). Fig. 5 The knowledge map of active and co-cited authors, along with the situation of their article citations. A The active co-cited cluster of authors. B The active cited period of the authors. C The total citations and number of articles by authors each year. The size of each circle corresponds to the number of publications, while each color indicates a specific cluster Table 4 The top 10 authors and co-cited authors in the Apolipoprotein-cancer research Rank Author Country Documents H-index TCs 1 Xu, Ning China 7 18 65 2 Luo, Guanghua China 5 21 42 3 Heeneman, Sylvia Netherlands 4 44 236 4 Hoving, Saske Netherlands 4 19 236 5 Stewart, Fiona A Germany 4 28 236 6 Baak, Jan P.A Norway 4 63 70 7 Janssen, Emiel A.M Norway 4 33 70 8 Korner, Hartwig Norway 4 33 70 9 Soiland, Havard Norway 4 18 70 10 Soreide, Jon Arne Norway 4 44 70 11 Zhang, Yao China 4 18 66 12 Zhang, Yun China 4 40 55 13 Yu, Yang China 4 11 54 14 Zheng, Lu China 4 10 36 15 Chang, Hui China 4 39 33 16 Zhang, Jun China 4 8 26 TCs, total citations.

Research hotspot analysis

Most cited publications

Assessment of the most cited articles within a field is feasible by considering the frequency of citations (Chen et al. 2010 ). Table 5 compiles the top 10 most cited publications with over 116 citations. Three of the ten publications were related to APOE, all emerging before 2005. Table 6 shows the most co-cited references. The most co-cited article is “ The cardioprotective protein apolipoprotein A1 promotes potent anti-tumorigenic effects Apolipoprotein E: cholesterol transport protein with expanding role in cell biology ” (Zamanian-Daryoush et al. 2013 ), which highlights the role of APOA1 in inhibiting tumor growth and metastasis by modulating the immune system. Table 5 The top 10 cited manuscripts in the field of apolipoprotein in cancer Rank Title Journal Type Publication year TCs 1 The relationship of APOE genotype to neuropsychological performance in long-term cancer survivors treated with standard dose chemotherapy Psycho-Oncology Article 2003 248 2 Silencing expression of the clusterin/apolipoprotein J gene in human cancer cells using small interfering RNA induces spontaneous apoptosis, reduced growth ability, and cell sensitization to genotoxic and oxidative stress Cancer Research Article 2004 191 3 Apolipoproteins, lipids and risk of cancer International Journal of Cancer Article 2016 146 4 Serum levels of an isoform of apolipoprotein A-II as a potential marker for prostate cancer Clinical Cancer Research Article 2005 136 5 Apolipoprotein A-I (ApoA-I), Immunity, Inflammation and Cancer Cancers Review 2019 135 6 Spontaneous plaque rupture and secondary thrombosis in apolipoprotein E-deficient and LDL receptor-deficient mice Journal of Pathology Article 2001 130 7 Apolipoprotein E is required for cell proliferation and survival in ovarian cancer Cancer Research Article 2005 116 8 Single-dose and fractionated irradiation promote initiation and progression of atherosclerosis and induce an inflammatory plaque phenotype in ApoE-/- mice International Journal of Radiation Oncology Biology Physics Article 2008 112 9 Apolipoprotein L6, a novel proapoptotic Bcl-2 homology 3-only protein, induces mitochondria-mediated apoptosis in cancer cells Molecular Cancer Research Article 2005 105 10 Apolipoproteins and cancer Cancer Medicine Review 2019 89 TCs, total citations. Table 6 The top 13 co-cited references of apolipoprotein research in cancer Rank Co-cited references Journal Type Publication year TCs 1 The cardioprotective protein apolipoprotein A1 promotes potent anti-tumorigenic effects Journal of Biological Chemistry Article 2013 38 2 Apolipoprotein E: cholesterol transport protein with expanding role in cell biology Science Article 1988 36 3 Apolipoprotein AI (apoA-I) and apoA-I mimetic peptides inhibit tumor development in a mouse model of ovarian cancer The Proceedings of the National Academy of Sciences Review 2010 27 4 Apolipoprotein E is required for cell proliferation and survival in ovarian cancer Cancer Research Article 2005 21 5 Analysis of Relative Gene Expression Data Using Real-Time Quantitative PCR and the 2 −ΔΔC T Method Methods Article 2001 20 6 Apolipoproteins, lipids and risk of cancer International Journal of Cancer Article 2016 18 7 Identification of potential markers for the detection of pancreatic cancer through comparative serum protein expression profiling Pancreas Article 2007 15 8 Apolipoprotein A1: a novel serum biomarker for predicting the prognosis of hepatocellular carcinoma after curative resection Oncotarget Article 2016 15 9 The association between lipid profiles and breast cancer among Taiwanese women Clinical Chemistry and Laboratory Medicine Article 2007 14 10 Expression and prognostic significance of apolipoprotein D in breast cancer American Journal of Pathology Article 1994 14 11 Global cancer statistics CA-A Cancer Journal for Clinicians Article 2011 14 12 Elevated apolipoprotein AI levels are associated with favorable prognosis in metastatic nasopharyngeal carcinoma Medical Oncology Article 2014 14 13 Apolipoprotein C-1 maintains cell survival by preventing from apoptosis in pancreatic cancer cells Oncogene Article 2008 14 TCs, total citations.

Analysis of keywords

Out of 2324 keywords, 79 surpassed the threshold of 9 occurrences. Figure 6 A exhibits the top 17 keywords organized by frequency, with ‘apolipoprotein e’ as the most frequently utilized keyword, followed by ‘expression’ and ‘inflammation’. These top 79 keywords underwent co-occurrence analysis, as shown in Fig. 6 B and D . Figure 6 B systematically categorizes 79 keywords through cluster analysis, revealing four distinct thematic clusters. Cluster 1 (red) predominantly encompasses genetic alterations and molecular pathogenesis in oncogenesis. Cluster 2 (green) highlights preclinical investigations utilizing APOE -deficient murine models for in vivo validation. Cluster 3 (blue) consolidates research on apolipoprotein-derived biomarkers for diagnostic and prognostic applications. Cluster 4 (yellow) delineates the regulatory functions of apolipoproteins in lipid metabolic reprogramming, a hallmark of malignant progression. Figure 6 C demonstrated that the top 11 keywords exhibited robust citation bursts lasting at least one year. Notably, while “messenger RNA” maintained prolonged attention, terms like “inflammation,” “survival,” and “metabolism” experienced the strongest bursts and emerged more recently. Notably, “apolipoprotein E” emerged as the most prominent keyword, exhibiting a citation burst from 2013 to 2016 (Fig. 6 C). Figure 6 D maps the temporal evolution of research foci through chronological clustering analysis. Early-stage investigations (blue) primarily addressed transcriptional regulation and gene expression profiling. In contrast, recent studies (yellow) prioritize immune checkpoint interactions, modulation of the tumor microenvironment, and clinical outcome assessments. Fig. 6 Focal points of research in the field of apolipoproteins in cancer. A The ratio of each keyword in the publications each year; B Clustering network of keywords co-occurrence; C A compilation of the top 11 keywords with the strongest citation burst; D Time-overlapping network of keywords co-occurrence. The size of the nodes reflects the frequency of keywords, and the distance between them signifies the strength of the relationships

Figure 7 offers a more precise visualization of these bursts. In Fig. 7 A, aside from Alzheimer’s disease, the keyword “apolipoprotein” showed numerous bursts spanning from 1991 to 2024. During 1991–2000, keywords such as “apoc 1,” “apolipoprotein A-1,” and “apolipoprotein M” surged, whereas from 2000 to 2010, bursts related to apolipoproteins generally diminished except for “apo 1.” After 2010, secondary bursts appeared for “apoc 1,” “apolipoprotein A-I,” “apolipoprotein B,” and “apolipoprotein M.” Fig. 7 B shows that research activity peaked from 1989 to 1997, with bursts like expression cancer and metabolism, followed by another surge from 2003 to 2013, centered on inflammation and activation. Figure 7 C presents clusters formed by co-occurring keywords. Fig. 7 The distribution of the keywords by year. A The map of the frequency of the appearance of the 15 keywords over time. B The map displays the frequency of appearance of all keywords by year. C The overlapping map of the clusters of 15 keywords illustrates their co-occurrence

Clinical trials indexed in PubMed

PubMed further enriched the research results from the clinical research perspective, complementing those of the Web of Science (WoS). Table 7 shows the relevant clinical trials listed in PubMed. These studies mainly explore the potential roles of apolipoproteins, especially APOA1, APOE, and APOD, in cancer diagnosis, prognosis, treatment response, and supportive care. The research includes various cancer types, such as colorectal, breast, bladder, and oral squamous cell carcinoma, using different methods such as serum or urine testing, biomarker screening, and genetic polymorphism analysis. Table 7 Clinical Trials of apolipoproteins in cancer indexed in PubMed Number Title Cancer Type Research Problem Results Journal PMID 1 Identification of Differentially Expressed Proteins in the Serum of Colorectal Cancer Patients Using 2D-DIGE Proteomics Analysis Colorectal Cancer Relationship of APOA1 level to colorectal cancer risk APOA1 showed positive immunoreactivity, but its expression did not correlate with Dukes’ staging ( p = 0.314), tumor grading ( p = 0.880), and lymph node involvement ( p = 0.108) Pathology & Oncology Research 26,463,353 2 Analysis of differentially expressed novel post-translational modifications of plasma apolipoprotein E in Taiwanese females with breast cancer Breast Cancer Relationship of post-translational modulation of APOEs to breast cancer risk Plasma levels of the APOE protein did not significantly differ between standard controls and breast cancer patients. Eleven sites with novel PTMs were identified from 7 pairs of differentially expressed targeted and modified peptides according to the relative modification ratio including methylation at the E3 (↑1.45-fold), E7 (↑1.45-fold), E11 (↑1.19-fold), E77 (↑2.02-fold), E87 (↑2.02-fold), and Q98 (↑1.62-fold) residues; dimethylation at the Q187 (↑1.44-fold) residue; dihydroxylation at the R92 (↑1.25-fold), K95 (↑1.25-fold), and R103 (↑1.25-fold) residues; and glycosylation at the S129 (↑1.14-fold) residue. The clustered methylation and dihydroxylation of plasma APOE proteins may play a role in breast cancer Journal of Proteomics 26,079,612 3 Radioimmunoassay for serum apolipoprotein D, an atypical apolipoprotein: validation and clinical application Breast Cancer Relationship of APOD level to breast cancer risk There was no difference in the serum concentrations of APOD between female controls, patients with benign breast disease, and early breast cancer. However, serum APOD was significantly lower in breast cancer patients with bone metastasis Annals of Clinical Biochemistry 9,293,309 4 Association of fall rate and functional status by APOE genotype in cancer survivors after exercise intervention Pan-cancer Relationship of APOE4 genotypes to cancer therapy side effects Data from 126 female cancer survivors, a median of 49 months out from cancer diagnosis, were analyzed. ApoE4 carriers trended toward a higher fall rate at baseline ( p = 0.059), but after exercise intervention had a fall rate lower than E4 non-carriers both immediately after structured intervention ( p = 0.013) and after 6 months of follow-up ( p = 0.002). E2 carriers did not show improved measures of depressive symptoms and self-report disability after exercise intervention. E3 homozygotes showed increased self-report physical activity after the 6-month exercise intervention, but E4 and E2 carriers did not Oncotarget 36,441,715 5 Identification of potential bladder cancer markers in urine by abundant-protein depletion coupled with quantitative proteomics Bladder Cancer Relationship of APOA genotypes to bladder cancer diagnosis Six apolipoproteins (APOA1, APOA2, APOB, APOC2, APOC3, and APOE) were able to differentiate bladder cancer from hernia. SAA4 significantly increased in bladder cancer subgroups, whereas ProEGF significantly decreased in bladder cancer subgroups. Additionally, the combination of SAA4 and ProEGF exhibited higher diagnostic capacity (AUC = 0.80 and p < 0.001) in discriminating bladder cancer from hernia than either marker alone Journal of Proteomics 23,631,828 6 Immune-featured stromal niches are associated with a response to neoadjuvant immunotherapy in oral squamous cell carcinoma Oral Squamous Cell Carcinoma Relationship of APOD genotypes to the response of immunochemotherapy Single-cell analysis reveals increased TSCs within the TME of responders in immunochemotherapy. Notably, significant post-treatment upregulation of SELP + high endothelial venules (HEVs) and APOD + myofibroblastic cancer-associated fibroblasts (myCAFs), alongside a decline in STMN1 + capillary endothelial cells (cECs), is specific to the immunochemotherapy cohort. In contrast, MYF5 + muscle satellite cells (MSCs) are upregulated in non-responders to mono-immunotherapy. SELP + HEVs and APOD + myCAFs foster favorable immunomodulatory stromal niches for improved outcomes, while STMN1 + cECs and MYF5 + MSCs form immunosuppressive niches in tumor invasion regions, highlighting therapeutic targets Cell Reports Medicine 40,107,247 7 Moderating Effects of Genetic Polymorphisms on Improvements in Cognitive Impairment in Breast Cancer Survivors Participating in a 6-Week Mindfulness-Based Stress Reduction (MBSR) Program Breast Cancer The relationship of SNPs in APOE to the response of MBSR(BC) Results indicated that SNPs in ANKK1, APOE, MTHFR, and SLC6A4 are associated with cognitive impairment. Additionally, rs1800497 in ANKK1 was significantly associated with improved cognitive function in response to MBSR(BC). These findings may help identify individuals who would benefit more from MBSR(BC) or other interventions Biological Research for Nursing 25,882,604 8 Multiplex protein signature for the detection of bladder cancer in voided urine samples Bladder cancer The relationship of urine biomarkers, including APOE, to the bladder cancer diagnosis Seven of the 8 urine biomarkers were increased in subjects with bladder cancer relative to those without bladder cancer. The 7 biomarkers were assessed in a new model with an AUROC of 0.88 (95% CI 0.84–0.93), 74% sensitivity, and 90% specificity. In contrast, the sensitivity of voided urine cytology and the UroVysion® cytogenetic test in this cohort was 39% and 54%, respectively Journal of Urology 23,764,080 9 Translational genomic research: the role of genetic polymorphisms in MBSR program among breast cancer survivors (MBSR [BC]) Breast cancer The relationship of SNPs in APOE to the response of MBSR (BC) Three SNPs (rs4680 in COMT, rs6314 in HTR2A, and rs429358 in APOE) emerged as having the strongest and most consistent effects in moderating the impact of the MBSR program on symptom outcomes. This translational behavioral research may help identify genetic profiles that moderate the impact of MBSR(BC) Translational Behavioral Medicine 30,137,607 10 Association of fall rate and functional status by APOE genotype in cancer survivors after exercise intervention Pan-cancer The relationship of ApoE genotypes to the fall rate of cancer survivors ApoE4 carriers trended toward a higher fall rate at baseline, but after exercise intervention had a fall rate lower than E4 non-carriers both immediately after structured intervention ( p = 0.013) and after 6 months of follow-up ( p = 0.002). E2 carriers did not show improved measures of depressive symptoms and self-report disability after exercise intervention. E3 homozygotes showed increased self-report physical activity after the 6-month exercise intervention, but E4 and E2 carriers did not Oncotarget 36,441,715

Key mechanisms linking apolipoproteins to cancer

After screening of genes with a threshold of a relevance score > 5, integrated analysis of 158 apolipoprotein-related genes and 4,773 cancer-associated genes revealed 55 overlapping genes (Fig. 8 A). Detailed gene information for apolipoproteins and cancer can be separately found in Table S1 and Table S2 . The overlapping gene list is presented in Table S3 . We further utilize their overlap for downstream analysis. The interaction status of the genes was analyzed using the STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) database (detailed results are presented in Table S4 and Fig. 8 B), with a combined score of > 0.9 for the highest confidence. Further centrality analysis, conducted via Cytoscape’s cytoHubba plugin using the MCC algorithm, identified 15 hub genes, with APOA1 , APOE , APOA2 , and ALB exhibiting dominant network roles (Fig. 8 C). The GO enrichment results show significant BP enrichment in the regulation of lipid localization (adjusted p = 3.53066E-19) and plasma lipoprotein particle levels (adjusted p = 7.2845E-17). CC enrichment was found in lipoprotein particles (adjusted p = 2.00047E-16) and protein-lipid complexes (adjusted p = 3.31563E-16), suggesting localization in these structures. MF enrichment included protein-lipid complex binding (adjusted p = 1.76621E-14) and lipoprotein particle receptor binding(adjusted p = 1.02689E-12) (Fig. 8 D; detailed results are provided in Table S5 ). Notably, the peroxisome proliferators-activated receptors (PPARs) signaling pathway is essential to focus on through KEGG enrichment (adjusted p = 0.000185521). DO analysis of the common genes reveals strong correlations between apolipoprotein-related genes and multiple diseases, including arteriosclerosis (adjusted p = 3.14717E-29), familial hyperlipidemia (adjusted p = 1.0755E-28), and chronic kidney disease (adjusted p = 9.15003E-22). Detailed results are presented in Table S6 . Fig. 8 The results of enrichment analysis. A Common genes of apolipoproteins and cancer. B Network map of PPI interaction network analysis through the STRING database. C 15 hub genes calculated through the Cytoscape plugin cytoHubba using the MCC algorithm. D Functional enrichment analysis of apolipoprotein-cancer research. PPI, Protein–Protein Interaction; MCC, Maximum Clique Centrality

Discussion

Bibliometric characteristics

Advancements across multiple fields drive the shift in research focus. High-throughput lipidomic profiling has enabled the discovery of apolipoproteins as dynamic biomarkers for tumor staging and monitoring (Agarwala et al. 2022 ; Hornburg et al. 2023 ). Simultaneously, the immunotherapy revolution, particularly Food and Drug Administration (FDA)-approved checkpoint inhibitors, spurred research on apolipoprotein-mediated immunomodulation, enhancing T-cell infiltration in immunosuppressive tumor environments. Coordinated funding initiatives also promoted multi-omics exploration of cancer-related metabolic reprogramming, highlighting lipid metabolism as an emerging hallmark of cancer (Martin-Perez et al. 2022 ; Snaebjornsson et al. 2020 ). These developments led to multi-center validation studies, solidifying apolipoproteins as dual diagnostic and therapeutic targets in precision oncology.

The most highly cited journal, Cancer Research , has the highest IF (IF = 12.5) among the top 10 journals published, indicating that it simultaneously possesses both high quality and quantity. Thus, the ideas and results in this journal can reveal a relatively promising direction in the field of cancer and apolipoproteins.

Among the top 10 cited manuscripts, four focus on how apolipoproteins influence cancer and their potential as markers, while two are clinical studies examining their connection to cancer or its aftermath. Additionally, two manuscripts on apolipoproteins and atherosclerosis indicate a possible link between cancer and cardiovascular diseases. The second most cited article, published in March 2004 in Cancer Research by Trougakos, IP et al., affiliated with the National Hellenic Research Foundation, has 191 citations. It used small interfering RNA (siRNA) to demonstrate that silencing APOJ gene expression can induce apoptosis and decrease the resistance of cancer cells to chemotherapy. The article published in 2005 by Chen, YC et al. is the 4th most co-cited and the 7th most cited, confirming that inhibition of APOE expression by siRNA technology can arrest the cell cycle in the G2 phase and induce apoptosis in cancer cells. The 5-year follow-up of patients also confirmed this pro-cancer effect of APOE. The most co-cited authors, Hoving, Saske, Heeneman, Sylvia, Stewart, and Fiona A., have done extensive work in collaboration to confirm the anti-atherosclerotic effect of APOE.

Research hot spots and frontiers

Co-occurrence mapping combined with keyword clustering techniques provides a solid framework for identifying main research areas through frequency-weighted lexical patterns. This approach systematically outlines disciplinary hotspots by revealing terminological patterns that reflect shared research priorities, while also detecting emerging conceptual boundaries that indicate new areas of investigation directions (Chen 2004 ; Chen et al. 2010 ). The key term “apolipoprotein E” appeared frequently across various themes. Increasing attention is given to “biomarker validation,” “inflammatory microenvironment,” and new apolipoprotein subtypes, indicating a shift toward immuno-oncology and clinical applications. Analysis of the field’s development shows a transition from early molecular research to more clinical studies. Recent keyword surges, such as “inflammation,” “survival,” and “metabolism,” highlight emerging research areas in cancer immunology and metabolism. Co-occurrence analysis reveals strong links between apolipoproteins and metabolic pathways, suggesting that “apolipoproteins,” “inflammation,” and “immune modulation” will be key areas of future research.

Hot spot 1: genetic polymorphisms and apolipoprotein-related cancer risks

The polymorphism of APOE is the most established, which can influence susceptibility and prognosis. APOE has 3 alleles, APOEε2 , APOEε3 , and APOEε4 . Several studies revealed an association between APOEε4 and the risk of breast cancer in Asian populations (Chang et al. 2005 ; Moysich et al. 2000 ; Saadat 2012 ). It can modify the relationship between high serum triglycerides and breast cancer risk (Saadat 2012 ). However, the serum triglycerides of patients with APOEε4 are significantly reduced in response to tamoxifen (Chang et al. 2009 ). Research from Taiwan (Chang et al. 2005 ; Wu et al. 2012 ) has shown that the HER2/neu-negative status is correlated with APOEε4 . In the progression of triple-negative breast cancer (TNBC), the overexpression of APOE-LRP8/VLDLR is observed in TNBC cells, which can promote the growth and survival of cancer cells in vitro. The APOE4 also has the most substantial growth-stimulatory effect (Shiang et al. 2011 ).

Ostendorf, BN et al. demonstrated that the melanoma in the mice carrying APOE4 grew the most slowly and spread the least compared with the other isoforms. The following experiments revealed that the APOEε4 allele enhances resistance to cancer by activating the immune system. Compared to APOEε4 mice, the tumors in APOEε4 mice have a stronger ability to recruit CD45 + leukocytes, and the activity of NK cells and CD8 + T cells is also higher. The same result was obtained by Nneoma Adaku et al. (Adaku et al. 2023 ) in 2023, who further found that the difference in melanoma progression between different APOE alleles is mediated via the low-density lipoprotein receptor-related protein 1 (LRP1).

An experiment using human prostate adenocarcinoma cells concluded that cells with the APOE ( ε2/ε4 ) genotype exhibit high expression of cav-1 simultaneously, which leads to the intracellular accumulation of cholesterol. The high cholesterol cells were remarkably more aggressive and more likely to cause advanced prostate cancer compared to the cells that had at least one ε3 allele (Ifere et al. 2013 ). Wild-type APOE3 can remove cholesterol from the cells, and the loss of APOE function increases the risk of malignant carcinoma. The APOE can bind to the triggering receptor expressed on myeloid cells 2 (TREM2) and induce the senescence of neutrophils (Bancaro et al. 2023 ) through the DAP12(DNAX-activating protein of 12 kDa)/SYK(Spleen Tyrosine kinase) pathway. The high expression of APOE and TREM2 correlates with a poor prognosis of prostate cancer.

Previous research on APOE polymorphism suggests that each APOE allele has distinct effects on patients. The potential of those alleles to serve as cancer biomarkers or therapy targets, as well as the deeper relationship between each isoform and cancer, is worth exploring further through additional research.

Hot spot 2: in-vivo models and mechanistic insights into APOE-related cancer progression and inflammation

Immune checkpoint inhibitor (ICI) therapy is a relatively safe and effective cancer treatment method. However, patients with high myeloid-derived suppressor cells (MDSCs) show no response to that therapy. Activation of liver-X nuclear receptor (LXR) can promote the transcription of APOE and mediate the activation of the cytotoxic T lymphocyte (CTL) to deplete the MDSCs. The APOE also binds to the low-density lipoprotein receptor-related protein 8 (LRP8), which is expressed on MDSCs, leading to the depletion of these cells. Tavazoie conducted the research above, and Masoud et al. demonstrated that the LXR/APOE axis is a target that enhances the effect of immune therapy (Tavazoie et al. 2018 ).

ICIs are also used in TNBC patients, but some patients still do not respond to them. It has been discovered that non-responding patients have a higher level of APOE + macrophages, which can interact with CD8 + T ex cells, which is one reason for therapy failure. The APOE inhibitor may enhance the therapeutic effect of ICIs.

APOE is also related to inflammatory reactions in pathological conditions. It has been discovered that the absence of APOE will enhance the expression of inflammatory markers in macrophages, which may contribute to the progression of atherosclerosis. The complex inflammatory responses and the heterogeneity of these responses between different cancers remind us that the effect of APOE on specific groups of immune cells is a promising and necessary area of research.

In papillary thyroid cancer (PTC) cells, APOE mRNA, the target of m6A modification, can promote the glycolysis of cancer cells and favor their proliferation. Insulin-like growth factor 2 mRNA-binding protein 2(IGF2BP2) can read and bind to the m 6 A site of APOE mRNA, thereby stabilizing it. The fat mass and obesity-associated protein (FTO), a m 6 A demethylase, can inhibit the proliferation of cancer cells, and it is downregulated in PTC cells. The downstream signal of the FTO/APOE axis is through the IL(Interleukin)-6/JAK(Janus kinase)2/STAT(Signal Transducer and Activator of Transcription)3 pathway to promote glycolysis. Therefore, the FTO analog or APOE inhibitor can be a promising medicine for cancer. (Huang et al. 2022 ).

Hot spot 3: apolipoprotein biomarkers in the diagnosis and prognosis of cancers

A cluster of keywords, including “diagnosis,” “expression,” “mechanism,” and “biomarker,” among others (see Fig. 6 B), suggests the potential of apolipoproteins as biomarkers for the diagnosis and prognosis of cancers. In gastric cancer, studies have identified Apolipoprotein C1 (APOC1) as a potential diagnostic and prognostic biomarker, with elevated APOC1 expression associated with a poor prognosis, suggesting its role in tumor progression (Yi et al. 2019 ). Research also indicates that Apolipoprotein A-I (APOA1) levels are downregulated in patients with gastric cancer (GC). Lower serum APOA1 levels correlate with a poor prognosis, highlighting its potential as a biomarker for early detection and outcome prediction (Li et al. 2024 ). A high level of APOD in gastric cancer patients is proven to correlate with a worse prognosis ( p = 0.013) and less response to immune therapy (Wang et al. 2024 ). There are changes in the expression of APOE-related genes in gastric cancer, including the upregulation of STAT2 and STAT3 (Shi et al. 2015 ). APOE is also elevated in GC tissues, relating to immune activation and infiltration of GC, and is strongly associated with poor survival outcomes (Peng et al. 2023 ). The tumor-associated macrophages (TAMs) are the only cell type synthesizing APOE in the gastric cancer microenvironment (Miao et al. 2023 ). In ovarian cancer, elevated expression of APOC1 has been linked to poorer overall survival in patients, indicating its potential as a prognostic marker (Yang et al. 2024b ). At the same time, the overexpression of human APOA1 has been associated with improved overall survival in ovarian cancer models, suggesting its protective role and potential as a therapeutic target (Su et al. 2010 ). APOB100 functioned as a tumor suppressor in ovarian cancer. Lower APOB mRNA levels were significantly associated with a poorer prognosis. Its overexpression can inhibit cell proliferation and autophagy (Yin et al. 2025 ). These findings underscore the importance of apolipoproteins as biomarkers in gastric and ovarian cancers, offering insights into disease mechanisms and potential avenues for early detection and targeted therapies.

Hot spot 4: lipid metabolism, apolipoproteins, and metabolic syndrome in gastrointestinal tumors

Sterol-regulatory element binding proteins (SREBPs) are transcription factors that regulate lipid synthesis. Its activation through PKB/Akt will induce the enzyme involved in the cholesterol and fatty acid synthesis pathway (Porstmann et al. 2005 ). High blood cholesterol is a dangerous factor for HCC. The high expression of SREBPs will increase the production of cholesterol. Secreted protein acidic and rich in cysteine (SPARC) can bind to APOE and block its connection with an E3 enzyme TRIM21 (ligase tripartite motif containing 21) so that the ubiquitination of it is stopped (Wan et al. 2024 ). APOC3 transgenic mice with high APOC3 expression in HCC exhibited hyperlipidemia and increased infiltration of CD8 + T cells. The increased antitumor activity depends on T cell activation (Hu et al. 2023 ). The accumulation of APOE can cause hypercholesterolemia, which is related to sorafenib resistance (Wan et al. 2024 ). While the APOB has a contrary effect, every 0.1 g/L increase in circulating APOB level causes an 11% decrease in liver cancer risk (Liu et al. 2024 ). APOM is specifically expressed in the liver and regulates the liver’s lipid metabolism. It was revealed that mouse liver cancer cells lacking APOM are more invasive, and the overexpression of APOM can inhibit proliferation, migration, and invasion, while also stimulating apoptosis of cancer cells (Bai et al. 2021 ). APOF is downregulated in HCC progression. The decrease in its expression is associated with a poor prognosis for patients. Migration and proliferation of cancer cells are inhibited by APOF both in vitro ( p < 0.05) and in vivo ( p < 0.001) (Wang et al. 2019 ).

Apolipoproteins also influence lipid metabolism in colorectal cancer. APOB and APOA-I levels are significantly lower in colorectal cancer patients than LDL-C. The total cholesterol (TCH) and total triglycerides (TG) levels of TNM stage III and IV patients are significantly reduced, and HDL-C level is increased in cancerous tissues and serum compared to the TNM stages I and II patients of colorectal cancer ( p < 0.05 and p < 0.01) (Zhang et al. 2014 ). Another study shows that the APOB/APOA-I ratio is a decisive prognostic factor (HR = 107.07, p = 0.002). The higher ratio is associated with a worse prognosis (Pan et al. 2022 ). Clinical research reveals that the APOA-IV (APOA4) level is an independent indicator of poor survival (HR = 2.83, p = 0.024) (Sugimachi et al. 2016 ). A 2014 study identified a molecular classifier related to metabolic syndrome, comprising ABCA1, APOA2, APOC1, and APOD, which can predict the risk of colon cancer relapse (HR = 4.47, p < 0.001) in a multivariate analysis. The metabolic syndrome-related genes also serve as a molecular marker of prognosis, such as APOA2 (HR = 3.1, p = 0.045) (Vargas et al. 2014 ).

Clinical trial landscapes

Apolipoproteins show promise as non-invasive diagnostic markers and modulators of treatment response. Their involvement in immune regulation and metabolic reprogramming further supports their potential as integrated diagnostic and therapeutic targets in precision oncology. Apolipoproteins show potential as diagnostic markers in cancer. While APOA1 expression in colorectal cancer showed no significant correlation with staging (Lim et al. 2016 ), bladder cancer studies revealed multiple apolipoproteins with strong diagnostic potential in urine-based assays, outperforming conventional tests (Chen et al. 2013 ; Rosser et al. 2013 ). Although plasma APOE levels were unchanged in breast cancer, novel post-translational modifications were identified, suggesting a mechanistic role in tumor biology (Uen et al. 2015 ). Additionally, lower APOD levels in breast cancer patients with bone metastases may serve as a marker for disease progression (Osundeko et al. 1997 ). Regarding therapeutic relevance, APOD-positive stromal cells in oral squamous cell carcinoma were associated with favorable immune microenvironments and improved response to immunotherapy (Liu et al. 2025 ).

Furthermore, APOE genotypes emerged as potential modifiers of intervention outcomes in breast cancer and other cancer survivors, influencing fall risk, physical activity, and cognitive performance following exercise and mindfulness-based programs (McGinnis et al. 2022 ). Apolipoproteins can also serve as biomarkers to predict the severity of specific cancer sequelae. It has been proven in 2 different studies that some single-nucleotide Polymorphisms (SNPs) in APOE are associated with cognitive impairment in breast cancer survivors (Park et al. 2019 ; Lengacher et al. 2015 ). These findings underscore the role of apolipoproteins as biomarkers and mediators of therapeutic efficacy and patient-reported outcomes.

Limitations

Translational application of apolipoproteins in cancer faces significant challenges, as different subtypes may be associated with different types of cancer. Although clinical outcomes show promise, a more in-depth understanding of how apolipoproteins interact during cancer progression is necessary, as well as further validation in large cohorts. While this bibliometric study offers new insights into the field of apolipoprotein-cancer research, methodological limitations should be carefully considered. Relying solely on English literature may introduce potential database selection bias, as emerging regional innovations, such as recent studies indexed in the China National Knowledge Infrastructure (CNKI) (Yan et al. 2023 ), may be underrepresented. Second, the inherent citation accumulation lag creates temporal blind spots for sleeping beauty (Donthu 2021 ) publications with delayed recognition, necessitating updates in subsequent research.

Conclusion

This study provides a comprehensive bibliometric and bioinformatic analysis of apolipoproteins in cancer research from 1985 to 2024, quantifying productivity and collaboration patterns at the national, institutional, and individual levels. Temporal analysis shows a shift from foundational molecular studies to translational research. Currently, the focus is on the genetic influence of apolipoprotein variants on cancer risk, the role of apolipoproteins in immune modulation, the development of apolipoprotein-related biomarkers, and the integration of lipid metabolism pathways in tumor progression. Future efforts should focus on global collaborations to reduce geographic disparities, enhance functional research with spatial omics and patient-derived models, and develop apolipoprotein-targeted strategies into combination therapies, such as immunotherapies or metabolic inhibitors. At the same time, prioritizing biomarker validation, understanding allele-specific mechanisms, and creating targeted therapies will improve risk stratification, early detection, and personalized treatments in oncology.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Material 1 Table S1. GeneCards search results for apolipoproteins.

Supplementary Material 2 Table S2. GeneCards search results for cancers.

Supplementary Material 3 Table S3. Common genes between apolipoproteins and cancers.

Supplementary Material 4 Table S4. Detailed results of PPI interaction network analysis through the STRING database.

Supplementary Material 5 Table S5. Detailed results of the GO/KEGG functional enrichment analysis.

Supplementary Material 6 Table S6. Detailed results of DO enrichment analysis.

Abbreviations

Hepatocellular carcinoma

Metabolic dysfunction-associated steatotic liver disease

Apolipoprotein A-I

Apolipoprotein A4

Apolipoprotein A2

Apolipoprotein C1

Apolipoprotein C3

Apolipoprotein D

Apolipoprotein B

Apolipoprotein B100

High-density lipoprotein

High-density lipoprotein cholesterol

Low-Density lipoprotein

Low-density lipoprotein cholesterol

Very low-density lipoprotein receptor

Apolipoprotein E

Apolipoprotein E4

Low-density lipoprotein receptor-related protein 1

Low-density lipoprotein receptor-related protein 8

Apolipoprotein M

Apolipoprotein F

Web of Science

United Kingdom Biobank

United States of America

Web of science core collection

Cholesteryl ester transfer protein

Apolipoprotein M

Paraoxonase 1

Protein-protein interaction

Gene ontology

Kyoto encyclopedia of genes and genomes

Disease ontology

Biological process

Molecular function

Cellular component

Benjamini-Hochberg

Impact factors

Journal citation reports

Total citation

Peroxisome proliferator-activated receptor

Food and drug administration

Triple-negative breast cancer

Triggering receptor expressed on myeloid Cell 2

Myeloid-derived suppressor cells

Liver-X nuclear receptor

Cytotoxic T lymphocyte

Immune checkpoint inhibitor

Papillary thyroid cancer

Fat mass and obesity-associated protein

Gastric cancer

Tumor-associated macrophages

Sterol-regulatory element binding Proteins

Secreted protein acidic and rich in cysteine

Tripartite motif containing 21

Total Cholesterol

Total triglycerides

China national knowledge infrastructure

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Dianzhe Tian, Zixuan Hu and Zuyi Yang have contributed equally to this work. .

Acknowledgements

The authors thank CiteSpace, VOSviewer, and all relevant R packages for providing researchers with free access.

Authors’ contributions

Dianzhe Tian: Conceptualization, Methodology, Software, Writing—Original draft preparation; Zixuan Hu: Validation, Data curation, Writing—Reviewing and Editing; Zuyi Yang: Investigation, Visualization, Writing—Reviewing and Editing; Lvyuxing Zhao: Investigation; Haitao Zhao: Yiyao Xu: Supervision; Xinting Sang: Supervision; Shunda Du: Supervision; Yunping Luo: Supervision, Project administration; Lei Zhang: Supervision, Validation, Funding acquisition; Yiyao Xu: Supervision, Validation, Project administration, Funding acquisition, Writing—Reviewing and Editing; Xin Lu: Supervision, Project administration.

Funding

This article was partially sponsored by the National High-Level Hospital Clinical Research Funding (2022-PUMCH-C-049 and 2022-PUMCH-A-237) and Beijing Students’ Funding for Innovation and Entrepreneurship Training Program (2024dcxm034 and 2025dcxm165).

Data availability

The datasets analyzed in the current study are available in public databases, including Scopus, Medline, PubMed, and Web of Science.

Declarations

Ethics approval and consent to participate

Not applicable.

Competing interests

The authors declare no competing interests.

References

Adaku N Ostendorf BN Mei W Tavazoie SF Apolipoprotein E2 stimulates protein synthesis and promotes melanoma progression and metastasis Cancer Res 2023 83 3013 3025 10.1158/0008-5472.CAN-23-1252 37335131 PMC10740391 Adaku N, Ostendorf BN, Mei W, Tavazoie SF (2023) Apolipoprotein E2 stimulates protein synthesis and promotes melanoma progression and metastasis. Cancer Res 83:3013–3025 37335131 10.1158/0008-5472.CAN-23-1252 PMC10740391

Agarwal A Durairajanayagam D Tatagari S Esteves SC Harlev A Henkel R Bibliometrics: tracking research impact by selecting the appropriate metrics Asian J Androl 2016 18 296 309 10.4103/1008-682X.171582 26806079 PMC4770502 Agarwal A, Durairajanayagam D, Tatagari S, Esteves SC, Harlev A, Henkel R et al (2016) Bibliometrics: tracking research impact by selecting the appropriate metrics. Asian J Androl 18:296–309 26806079 10.4103/1008-682X.171582 PMC4770502

Agarwala PK Aneja R Kapoor S Lipidomic landscape in cancer: actionable insights for membrane-based therapy and diagnoses Med Res Rev 2022 42 983 1018 10.1002/med.21868 34719798 Agarwala PK, Aneja R, Kapoor S (2022) Lipidomic landscape in cancer: actionable insights for membrane-based therapy and diagnoses. Med Res Rev 42:983–1018 34719798 10.1002/med.21868

Aria M, Cuccurullo C. (2016) bibliometrix: Comprehensive Science Mapping Analysis [Internet]. [cited 2025 Feb 23]. p. 4.3.2. Available from: https://CRAN.R-project.org/package=bibliometrix

Bai Y Pei W Zhang X Zheng H Hua C Min J ApoM is an important potential protective factor in the pathogenesis of primary liver cancer J Cancer 2021 12 4661 4671 10.7150/jca.53115 34149930 PMC8210568 Bai Y, Pei W, Zhang X, Zheng H, Hua C, Min J et al (2021) ApoM is an important potential protective factor in the pathogenesis of primary liver cancer. J Cancer 12:4661–4671 34149930 10.7150/jca.53115 PMC8210568

Bancaro N Calì B Troiani M Elia AR Arzola RA Attanasio G Apolipoprotein E induces pathogenic senescent-like myeloid cells in prostate cancer Cancer Cell 2023 41 602 619.e11 10.1016/j.ccell.2023.02.004 36868226 Bancaro N, Calì B, Troiani M, Elia AR, Arzola RA, Attanasio G et al (2023) Apolipoprotein E induces pathogenic senescent-like myeloid cells in prostate cancer. Cancer Cell 41:602-619.e11 36868226 10.1016/j.ccell.2023.02.004

Bian X Liu R Meng Y Xing D Xu D Lu Z Lipid metabolism and cancer J Exp Med 2021 10.1084/jem.20201606 33601415 PMC7754673 Bian X, Liu R, Meng Y, Xing D, Xu D, Lu Z (2021) Lipid metabolism and cancer. J Exp Med. 10.1084/jem.20201606 33601415 10.1084/jem.20201606 PMC7754673

Cao Y Adipocyte and lipid metabolism in cancer drug resistance J Clin Invest 2019 129 3006 3017 10.1172/JCI127201 31264969 PMC6668696 Cao Y (2019) Adipocyte and lipid metabolism in cancer drug resistance. J Clin Invest 129:3006–3017 31264969 10.1172/JCI127201 PMC6668696

Cedó L Reddy ST Mato E Blanco-Vaca F Escolà-Gil JC HDL and LDL: potential new players in breast cancer development J Clin Med 2019 8 853 10.3390/jcm8060853 31208017 PMC6616617 Cedó L, Reddy ST, Mato E, Blanco-Vaca F, Escolà-Gil JC (2019) HDL and LDL: potential new players in breast cancer development. J Clin Med 8:853 31208017 10.3390/jcm8060853 PMC6616617

Chang N-W Chen D-R Wu C-T Aouizerat BE Chen F-N Hung S-J Influences of apolipoprotein E polymorphism on the risk for breast cancer and HER2/neu status in Taiwan Breast Cancer Res Treat 2005 90 257 261 10.1007/s10549-004-4656-7 15830139 Chang N-W, Chen D-R, Wu C-T, Aouizerat BE, Chen F-N, Hung S-J et al (2005) Influences of apolipoprotein E polymorphism on the risk for breast cancer and HER2/neu status in Taiwan. Breast Cancer Res Treat 90:257–261 15830139 10.1007/s10549-004-4656-7

Chang N-W Chen F-N Wu C-T Lin C-F Chen D-R Apolipoprotein E4 allele influences the response of plasma triglyceride levels to tamoxifen in breast cancer patients Clin Chim Acta 2009 401 144 147 10.1016/j.cca.2008.12.005 19103187 Chang N-W, Chen F-N, Wu C-T, Lin C-F, Chen D-R (2009) Apolipoprotein E4 allele influences the response of plasma triglyceride levels to tamoxifen in breast cancer patients. Clin Chim Acta 401:144–147 19103187 10.1016/j.cca.2008.12.005

Chen C Searching for intellectual turning points: progressive knowledge domain visualization Proc Natl Acad Sci U S A 2004 101 5303 5310 10.1073/pnas.0307513100 14724295 PMC387312 Chen C (2004) Searching for intellectual turning points: progressive knowledge domain visualization. Proc Natl Acad Sci U S A 101:5303–5310 14724295 10.1073/pnas.0307513100 PMC387312

Chen C CiteSpace II: detecting and visualizing emerging trends and transient patterns in scientific literature J Am Soc Inf Sci Technol 2006 57 359 377 10.1002/asi.20317 Chen C (2006) CiteSpace II: detecting and visualizing emerging trends and transient patterns in scientific literature. J Am Soc Inf Sci Technol 57:359–377

Chen C Ibekwe-SanJuan F Hou J The structure and dynamics of cocitation clusters: a multiple-perspective cocitation analysis J Am Soc Inf Sci Technol 2010 61 1386 1409 10.1002/asi.21309 Chen C, Ibekwe-SanJuan F, Hou J (2010) The structure and dynamics of cocitation clusters: a multiple-perspective cocitation analysis. J Am Soc Inf Sci Technol 61:1386–1409

Chen C-L Lin T-S Tsai C-H Wu C-C Chung T Chien K-Y Identification of potential bladder cancer markers in urine by abundant-protein depletion coupled with quantitative proteomics J Proteomics 2013 85 28 43 10.1016/j.jprot.2013.04.024 23631828 Chen C-L, Lin T-S, Tsai C-H, Wu C-C, Chung T, Chien K-Y et al (2013) Identification of potential bladder cancer markers in urine by abundant-protein depletion coupled with quantitative proteomics. J Proteomics 85:28–43 23631828 10.1016/j.jprot.2013.04.024

Cheng C Geng F Cheng X Guo D Lipid metabolism reprogramming and its potential targets in cancer Cancer Commun 2018 38 27 10.1186/s40880-018-0301-4 PMC5993136 29784041 Cheng C, Geng F, Cheng X, Guo D (2018) Lipid metabolism reprogramming and its potential targets in cancer. Cancer Commun 38:27 10.1186/s40880-018-0301-4 PMC5993136 29784041

Chin C-H Chen S-H Wu H-H Ho C-W Ko M-T Lin C-Y cytoHubba: identifying hub objects and sub-networks from complex interactome BMC Syst Biol 2014 8 Suppl 4 S11 10.1186/1752-0509-8-S4-S11 25521941 PMC4290687 Chin C-H, Chen S-H, Wu H-H, Ho C-W, Ko M-T, Lin C-Y (2014) cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol 8(Suppl 4):S11 25521941 10.1186/1752-0509-8-S4-S11 PMC4290687

Donthu N Kumar S Mukherjee D Pandey N Lim WM How to conduct a bibliometric analysis: an overview and guidelines J Bus Res 2021 133 285 296 10.1016/j.jbusres.2021.04.070 Donthu N, Kumar S, Mukherjee D, Pandey N, Lim WM (2021) How to conduct a bibliometric analysis: an overview and guidelines. J Bus Res 133:285–296

Emerging Risk Factors Collaboration, Di Angelantonio E, Sarwar N, Perry P, Kaptoge S, Ray KK, et al. (2009) Major lipids, apolipoproteins, and risk of vascular disease. JAMA. 302:1993–2000 10.1001/jama.2009.1619 PMC3284229 19903920

Faubert B Solmonson A DeBerardinis RJ Metabolic reprogramming and cancer progression Science 2020 10.1126/science.aaw5473 32273439 PMC7227780 Faubert B, Solmonson A, DeBerardinis RJ (2020) Metabolic reprogramming and cancer progression. Science. 10.1126/science.aaw5473 32273439 10.1126/science.aaw5473 PMC7227780

Garfield E Journal impact factor: a brief review CMAJ Can Med Assoc J J Assoc Medicale Can 1999 161 979 980 PMC1230709 10551195 Garfield E (1999) Journal impact factor: a brief review. CMAJ Can Med Assoc J J Assoc Medicale Can 161:979–980 PMC1230709 10551195

Hall RE Horsfall DJ Stahl J Vivekanandan S Ricciardelli C Stapleton AMF Apolipoprotein-D: a novel cellular marker for HGPIN and prostate cancer Prostate 2004 58 103 108 10.1002/pros.10343 14716735 Hall RE, Horsfall DJ, Stahl J, Vivekanandan S, Ricciardelli C, Stapleton AMF et al (2004) Apolipoprotein-D: a novel cellular marker for HGPIN and prostate cancer. Prostate 58:103–108 14716735 10.1002/pros.10343

Hicks D Wouters P Waltman L de Rijcke S Rafols I Bibliometrics: the Leiden manifesto for research metrics Nature 2015 520 429 431 10.1038/520429a 25903611 Hicks D, Wouters P, Waltman L, de Rijcke S, Rafols I (2015) Bibliometrics: the Leiden manifesto for research metrics. Nature 520:429–431 25903611 10.1038/520429a

Hornburg D Wu S Moqri M Zhou X Contrepois K Bararpour N Dynamic lipidome alterations associated with human health, disease and ageing Nat Metab 2023 5 1578 1594 10.1038/s42255-023-00880-1 37697054 PMC10513930 Hornburg D, Wu S, Moqri M, Zhou X, Contrepois K, Bararpour N et al (2023) Dynamic lipidome alterations associated with human health, disease and ageing. Nat Metab 5:1578–1594 37697054 10.1038/s42255-023-00880-1 PMC10513930

Hu X Ding S Lu G Lin Z Liao L Xiao W Apolipoprotein C-III itself stimulates the Syk/cPLA2-induced inflammasome activation of macrophage to boost anti-tumor activity of CD8+ T cell Cancer Immunol Immunother CII 2023 72 4123 4144 10.1007/s00262-023-03547-8 37853273 PMC10992383 Hu X, Ding S, Lu G, Lin Z, Liao L, Xiao W et al (2023) Apolipoprotein C-III itself stimulates the Syk/cPLA2-induced inflammasome activation of macrophage to boost anti-tumor activity of CD8+ T cell. Cancer Immunol Immunother CII 72:4123–4144 37853273 10.1007/s00262-023-03547-8 PMC10992383

Huang J Sun W Wang Z Lv C Zhang T Zhang D FTO suppresses glycolysis and growth of papillary thyroid cancer via decreasing stability of APOE mRNA in an N6-methyladenosine-dependent manner J Exp Clin Cancer Res 2022 41 42 10.1186/s13046-022-02254-z 35090515 PMC8796435 Huang J, Sun W, Wang Z, Lv C, Zhang T, Zhang D et al (2022) FTO suppresses glycolysis and growth of papillary thyroid cancer via decreasing stability of APOE mRNA in an N6-methyladenosine-dependent manner. J Exp Clin Cancer Res 41:42 35090515 10.1186/s13046-022-02254-z PMC8796435

Ifere GO Desmond R Demark-Wahnefried W Nagy TR Apolipoprotein E gene polymorphism influences aggressive behavior in prostate cancer cells by deregulating cholesterol homeostasis Int J Oncol 2013 43 1002 1010 10.3892/ijo.2013.2057 23934233 PMC3829771 Ifere GO, Desmond R, Demark-Wahnefried W, Nagy TR (2013) Apolipoprotein E gene polymorphism influences aggressive behavior in prostate cancer cells by deregulating cholesterol homeostasis. Int J Oncol 43:1002–1010 23934233 10.3892/ijo.2013.2057 PMC3829771

Ihaka R Gentleman R R: A Language for Data Analysis and Graphics J Comput Graph Stat 1996 5 299 314 10.1080/10618600.1996.10474713 Ihaka R, Gentleman R (1996) R: A Language for Data Analysis and Graphics. J Comput Graph Stat 5:299–314

Jiang S Liu Y Zheng H Zhang L Zhao H Sang X Evolutionary patterns and research frontiers in neoadjuvant immunotherapy: a bibliometric analysis Int J Surg 2023 109 2774 2783 10.1097/JS9.0000000000000492 37216225 PMC10498839 Jiang S, Liu Y, Zheng H, Zhang L, Zhao H, Sang X et al (2023) Evolutionary patterns and research frontiers in neoadjuvant immunotherapy: a bibliometric analysis. Int J Surg 109:2774–2783 37216225 10.1097/JS9.0000000000000492 PMC10498839

Jin HR Wang J Wang ZJ Xi MJ Xia BH Deng K Lipid metabolic reprogramming in tumor microenvironment: from mechanisms to therapeutics J Hematol Oncol 2023 16 103 10.1186/s13045-023-01498-2 37700339 PMC10498649 Jin HR, Wang J, Wang ZJ, Xi MJ, Xia BH, Deng K et al (2023) Lipid metabolic reprogramming in tumor microenvironment: from mechanisms to therapeutics. J Hematol Oncol 16:103 37700339 10.1186/s13045-023-01498-2 PMC10498649

Kanehisa M Goto S Furumichi M Tanabe M Hirakawa M KEGG for representation and analysis of molecular networks involving diseases and drugs Nucleic Acids Res 2010 38 D355 360 10.1093/nar/gkp896 19880382 PMC2808910 Kanehisa M, Goto S, Furumichi M, Tanabe M, Hirakawa M (2010) KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Res 38:D355-360 19880382 10.1093/nar/gkp896 PMC2808910

Kastelein JJP van der Steeg WA Holme I Gaffney M Cater NB Barter P Lipids, apolipoproteins, and their ratios in relation to cardiovascular events with statin treatment Circulation 2008 117 3002 3009 10.1161/CIRCULATIONAHA.107.713438 18519851 Kastelein JJP, van der Steeg WA, Holme I, Gaffney M, Cater NB, Barter P et al (2008) Lipids, apolipoproteins, and their ratios in relation to cardiovascular events with statin treatment. Circulation 117:3002–3009 18519851 10.1161/CIRCULATIONAHA.107.713438

Ko H-L Wang Y-S Fong W-L Chi M-S Chi K-H Kao S-J Apolipoprotein C1 (APOC1) as a novel diagnostic and prognostic biomarker for lung cancer: a marker phase I trial Thorac Cancer 2014 5 500 508 10.1111/1759-7714.12117 26767044 PMC4704334 Ko H-L, Wang Y-S, Fong W-L, Chi M-S, Chi K-H, Kao S-J (2014) Apolipoprotein C1 (APOC1) as a novel diagnostic and prognostic biomarker for lung cancer: a marker phase I trial. Thorac Cancer 5:500–508 26767044 10.1111/1759-7714.12117 PMC4704334

Lengacher CA Reich RR Kip KE Paterson CL Park HY Ramesar S Moderating effects of genetic polymorphisms on improvements in cognitive impairment in breast cancer survivors participating in a 6-week mindfulness-based stress reduction program Biol Res Nurs 2015 17 393 404 10.1177/1099800415577633 25882604 PMC10131135 Lengacher CA, Reich RR, Kip KE, Paterson CL, Park HY, Ramesar S et al (2015) Moderating effects of genetic polymorphisms on improvements in cognitive impairment in breast cancer survivors participating in a 6-week mindfulness-based stress reduction program. Biol Res Nurs 17:393–404 25882604 10.1177/1099800415577633 PMC10131135

Levy JMM Towers CG Thorburn A Targeting autophagy in cancer Nat Rev Cancer 2017 17 528 542 10.1038/nrc.2017.53 28751651 PMC5975367 Levy JMM, Towers CG, Thorburn A (2017) Targeting autophagy in cancer. Nat Rev Cancer 17:528–542 28751651 10.1038/nrc.2017.53 PMC5975367

Li F, Han M, Gao X, Du X, Jiang C. (2024) APOA1 mRNA and serum APOA1 protein as diagnostic and prognostic biomarkers in gastric cancer. Transl Cancer Res [Internet]; [cited 2025 Feb 17];13. Available from: https://tcr.amegroups.org/article/view/86511 10.21037/tcr-23-1966 PMC11170536 38881912

Lim LC Looi ML Zakaria SZS Sagap I Rose IM Chin S-F Identification of differentially expressed proteins in the serum of colorectal cancer patients using 2D-DIGE proteomics analysis Pathol Oncol Res POR 2016 22 169 177 10.1007/s12253-015-9991-y 26463353 PMC4681751 Lim LC, Looi ML, Zakaria SZS, Sagap I, Rose IM, Chin S-F et al (2016) Identification of differentially expressed proteins in the serum of colorectal cancer patients using 2D-DIGE proteomics analysis. Pathol Oncol Res POR 22:169–177 26463353 10.1007/s12253-015-9991-y PMC4681751

Liu Z Yuan H Wang Y Li K Suo C Jin L Proteogenomic analysis identifies a causal association between plasma Apolipoprotein B levels and liver cancer risk J Proteome Res 2024 23 4055 4066 10.1021/acs.jproteome.4c00397 39091241 Liu Z, Yuan H, Wang Y, Li K, Suo C, Jin L et al (2024) Proteogenomic analysis identifies a causal association between plasma Apolipoprotein B levels and liver cancer risk. J Proteome Res 23:4055–4066 39091241 10.1021/acs.jproteome.4c00397

Liu Y-T Liu H-M Ren J-G Zhang W Wang X-X Yu Z-L Immune-featured stromal niches associate with response to neoadjuvant immunotherapy in oral squamous cell carcinoma Cell Rep Med 2025 6 102024 10.1016/j.xcrm.2025.102024 40107247 PMC11970382 Liu Y-T, Liu H-M, Ren J-G, Zhang W, Wang X-X, Yu Z-L et al (2025) Immune-featured stromal niches associate with response to neoadjuvant immunotherapy in oral squamous cell carcinoma. Cell Rep Med 6:102024 40107247 10.1016/j.xcrm.2025.102024 PMC11970382

Maglott D Ostell J Pruitt KD Tatusova T Entrez Gene: gene-centered information at NCBI Nucleic Acids Res 2007 35 D26 31 10.1093/nar/gkl993 17148475 PMC1761442 Maglott D, Ostell J, Pruitt KD, Tatusova T (2007) Entrez Gene: gene-centered information at NCBI. Nucleic Acids Res 35:D26-31 17148475 10.1093/nar/gkl993 PMC1761442

Martin-Perez M Urdiroz-Urricelqui U Bigas C Benitah SA The role of lipids in cancer progression and metastasis Cell Metab 2022 34 1675 1699 10.1016/j.cmet.2022.09.023 36261043 Martin-Perez M, Urdiroz-Urricelqui U, Bigas C, Benitah SA (2022) The role of lipids in cancer progression and metastasis. Cell Metab 34:1675–1699 36261043 10.1016/j.cmet.2022.09.023

McGinnis GJ Holden S Yu B Ransom C Guidarelli C De B Association of fall rate and functional status by APOE genotype in cancer survivors after exercise intervention Oncotarget 2022 13 1259 1270 10.18632/oncotarget.28310 36441715 PMC11623406 McGinnis GJ, Holden S, Yu B, Ransom C, Guidarelli C, De B et al (2022) Association of fall rate and functional status by APOE genotype in cancer survivors after exercise intervention. Oncotarget 13:1259–1270 36441715 10.18632/oncotarget.28310 PMC11623406

Miao G Zhuo D Han X Yao W Liu C Liu H From degenerative disease to malignant tumors: insight to the function of ApoE Biomed Pharmacother 2023 158 114127 10.1016/j.biopha.2022.114127 36516696 Miao G, Zhuo D, Han X, Yao W, Liu C, Liu H et al (2023) From degenerative disease to malignant tumors: insight to the function of ApoE. Biomed Pharmacother 158:114127 36516696 10.1016/j.biopha.2022.114127

Morry J Ngamcherdtrakul W Yantasee W Oxidative stress in cancer and fibrosis: opportunity for therapeutic intervention with antioxidant compounds, enzymes, and nanoparticles Redox Biol 2017 11 240 253 10.1016/j.redox.2016.12.011 28012439 PMC5198743 Morry J, Ngamcherdtrakul W, Yantasee W (2017) Oxidative stress in cancer and fibrosis: opportunity for therapeutic intervention with antioxidant compounds, enzymes, and nanoparticles. Redox Biol 11:240–253 28012439 10.1016/j.redox.2016.12.011 PMC5198743

Moysich KB Freudenheim JL Baker JA Ambrosone CB Bowman ED Schisterman EF Apolipoprotein E genetic polymorphism, serum lipoproteins, and breast cancer risk Mol Carcinog 2000 27 2 9 10.1002/(SICI)1098-2744(200001)27:1<2::AID-MC2>3.0.CO;2-W 10642431 Moysich KB, Freudenheim JL, Baker JA, Ambrosone CB, Bowman ED, Schisterman EF et al (2000) Apolipoprotein E genetic polymorphism, serum lipoproteins, and breast cancer risk. Mol Carcinog 27:2–9 10642431 10.1002/(sici)1098-2744(200001)27:1<2::aid-mc2>3.0.co;2-w

Osundeko O Tetlow L Bundred N Gowland E Radioimmunoassay for serum apolipoprotein D, an atypical apolipoprotein: validation and clinical application Ann Clin Biochem 1997 34 Pt 5 537 542 10.1177/000456329703400508 9293309 Osundeko O, Tetlow L, Bundred N, Gowland E (1997) Radioimmunoassay for serum apolipoprotein D, an atypical apolipoprotein: validation and clinical application. Ann Clin Biochem 34(Pt 5):537–542 9293309 10.1177/000456329703400508

Pan Y Ye Z Ling Y Kong L Wang C Chen G The apolipoprotein B and apolipoprotein A-I Ratio serves as a strong prognostic factor for the overall survival of patients with colorectal cancer Front Oncol 2022 12 1089688 10.3389/fonc.2022.1089688 36713523 PMC9880464 Pan Y, Ye Z, Ling Y, Kong L, Wang C, Chen G et al (2022) The apolipoprotein B and apolipoprotein A-I Ratio serves as a strong prognostic factor for the overall survival of patients with colorectal cancer. Front Oncol 12:1089688 36713523 10.3389/fonc.2022.1089688 PMC9880464

Park JY Lengacher CA Reich RR Alinat CB Ramesar S Le A Translational genomic research: the role of genetic polymorphisms in MBSR program among breast cancer survivors (MBSR [BC]) Transl Behav Med 2019 9 693 702 10.1093/tbm/iby061 30137607 PMC7184864 Park JY, Lengacher CA, Reich RR, Alinat CB, Ramesar S, Le A et al (2019) Translational genomic research: the role of genetic polymorphisms in MBSR program among breast cancer survivors (MBSR [BC]). Transl Behav Med 9:693–702 30137607 10.1093/tbm/iby061 PMC7184864

Peng X Cai Z Chen D Ye F Hong L Prognostic significance and immune characteristics of APOE in gastric cancer Aging 2023 15 13840 13853 10.18632/aging.205265 38054821 PMC10756126 Peng X, Cai Z, Chen D, Ye F, Hong L (2023) Prognostic significance and immune characteristics of APOE in gastric cancer. Aging 15:13840–13853 38054821 10.18632/aging.205265 PMC10756126

Porstmann T Griffiths B Chung Y-L Delpuech O Griffiths JR Downward J PKB/Akt induces transcription of enzymes involved in cholesterol and fatty acid biosynthesis via activation of SREBP Oncogene 2005 24 6465 6481 10.1038/sj.onc.1208802 16007182 Porstmann T, Griffiths B, Chung Y-L, Delpuech O, Griffiths JR, Downward J et al (2005) PKB/Akt induces transcription of enzymes involved in cholesterol and fatty acid biosynthesis via activation of SREBP. Oncogene 24:6465–6481 16007182 10.1038/sj.onc.1208802

Rosser CJ Ross S Chang M Dai Y Mengual L Zhang G Multiplex protein signature for the detection of bladder cancer in voided urine samples J Urol 2013 190 2257 2262 10.1016/j.juro.2013.06.011 23764080 PMC4013793 Rosser CJ, Ross S, Chang M, Dai Y, Mengual L, Zhang G et al (2013) Multiplex protein signature for the detection of bladder cancer in voided urine samples. J Urol 190:2257–2262 23764080 10.1016/j.juro.2013.06.011 PMC4013793

Saadat M Apolipoprotein E ( APOE ) polymorphisms and susceptibility to breast cancer: a meta-analysis Cancer Res Treat 2012 44 121 126 10.4143/crt.2012.44.2.121 22802750 PMC3394861 Saadat M (2012) Apolipoprotein E ( APOE ) polymorphisms and susceptibility to breast cancer: a meta-analysis. Cancer Res Treat 44:121–126 22802750 10.4143/crt.2012.44.2.121 PMC3394861

Shi X Xu J Wang J Cui M Gao Y Niu H Expression analysis of apolipoprotein E and its associated genes in gastric cancer Oncol Lett 2015 10 1309 1314 10.3892/ol.2015.3447 26622669 PMC4533697 Shi X, Xu J, Wang J, Cui M, Gao Y, Niu H et al (2015) Expression analysis of apolipoprotein E and its associated genes in gastric cancer. Oncol Lett 10:1309–1314 26622669 10.3892/ol.2015.3447 PMC4533697

Shi H Huang H Pu J Shi D Ning Y Dong Y Decreased pretherapy serum apolipoprotein A-I is associated with extent of metastasis and poor prognosis of non-small-cell lung cancer OncoTargets Ther 2018 11 6995 7003 10.2147/OTT.S170227 PMC6199218 30410356 Shi H, Huang H, Pu J, Shi D, Ning Y, Dong Y et al (2018) Decreased pretherapy serum apolipoprotein A-I is associated with extent of metastasis and poor prognosis of non-small-cell lung cancer. OncoTargets Ther 11:6995–7003 10.2147/OTT.S170227 PMC6199218 30410356

Shiang C, Wang B, Broom B, Pusztai L. (2011) ApoE and Its Receptors (LRP8, VLDLR) Function as Growth Signals for Triple Negative Breast Cancer and Represent a Novel Therapeutic Target. CANCER Res [Internet]. [cited 2025 Jan 4];71. Available from: https://aacrjournals.org/cancerres/article/71/24_Supplement/P3-17-01/568939/P3-17-01-ApoE-and-Its-Receptors-LRP8-VLDLR

Snaebjornsson MT Janaki-Raman S Schulze A Greasing the wheels of the cancer machine: the role of lipid metabolism in cancer Cell Metab 2020 31 62 76 10.1016/j.cmet.2019.11.010 31813823 Snaebjornsson MT, Janaki-Raman S, Schulze A (2020) Greasing the wheels of the cancer machine: the role of lipid metabolism in cancer. Cell Metab 31:62–76 31813823 10.1016/j.cmet.2019.11.010

Stelzer G Rosen N Plaschkes I Zimmerman S Twik M Fishilevich S The genecards suite: from gene data mining to disease genome sequence analyses Curr Protoc Bioinformatics 2016 54 1.30.1 1.30.33 10.1002/cpbi.5 27322403 Stelzer G, Rosen N, Plaschkes I, Zimmerman S, Twik M, Fishilevich S et al (2016) The genecards suite: from gene data mining to disease genome sequence analyses. Curr Protoc Bioinformatics 54:1.30.1-1.30.33 27322403 10.1002/cpbi.5

Su F Kozak KR Imaizumi S Gao F Amneus MW Grijalva V Apolipoprotein A-I (apoA-I) and apoA-I mimetic peptides inhibit tumor development in a mouse model of ovarian cancer Proc Natl Acad Sci U S A 2010 107 19997 20002 10.1073/pnas.1009010107 21041624 PMC2993420 Su F, Kozak KR, Imaizumi S, Gao F, Amneus MW, Grijalva V et al (2010) Apolipoprotein A-I (apoA-I) and apoA-I mimetic peptides inhibit tumor development in a mouse model of ovarian cancer. Proc Natl Acad Sci U S A 107:19997–20002 21041624 10.1073/pnas.1009010107 PMC2993420

Sugimachi K Yamaguchi R Eguchi H Ueda M Niida A Sakimura S 8q24 polymorphisms and diabetes mellitus regulate apolipoprotein A-IV in colorectal carcinogenesis Ann Surg Oncol 2016 23 546 551 10.1245/s10434-016-5374-1 27387680 Sugimachi K, Yamaguchi R, Eguchi H, Ueda M, Niida A, Sakimura S et al (2016) 8q24 polymorphisms and diabetes mellitus regulate apolipoprotein A-IV in colorectal carcinogenesis. Ann Surg Oncol 23:546–551 27387680 10.1245/s10434-016-5374-1

Tavazoie MF Pollack I Tanqueco R Ostendorf BN Reis BS Gonsalves FC LXR/ApoE activation restricts innate immune suppression in cancer Cell 2018 172 825 840.e18 10.1016/j.cell.2017.12.026 29336888 PMC5846344 Tavazoie MF, Pollack I, Tanqueco R, Ostendorf BN, Reis BS, Gonsalves FC et al (2018) LXR/ApoE activation restricts innate immune suppression in cancer. Cell 172:825-840.e18 29336888 10.1016/j.cell.2017.12.026 PMC5846344

Uen Y-H Liao C-C Lin J-C Pan Y-H Liu Y-C Chen Y-C Analysis of differentially expressed novel post-translational modifications of plasma apolipoprotein E in Taiwanese females with breast cancer J Proteomics 2015 126 252 262 10.1016/j.jprot.2015.05.038 26079612 Uen Y-H, Liao C-C, Lin J-C, Pan Y-H, Liu Y-C, Chen Y-C et al (2015) Analysis of differentially expressed novel post-translational modifications of plasma apolipoprotein E in Taiwanese females with breast cancer. J Proteomics 126:252–262 26079612 10.1016/j.jprot.2015.05.038

Van Eck NJ Waltman L Decker R Lenz H-J VOS: A New Method for Visualizing Similarities Between Objects Adv Data Anal [Internet] 2007 Springer, Berlin Heidelberg Berlin, Heidelberg 299 306 Van Eck NJ, Waltman L (2007) VOS: A New Method for Visualizing Similarities Between Objects. In: Decker R, Lenz H-J (eds) Adv Data Anal [Internet]. Berlin, Heidelberg, Springer, Berlin Heidelberg, pp 299–306

van Eck NJ Waltman L Software survey: VOSviewer, a computer program for bibliometric mapping Scientometrics 2010 84 523 538 10.1007/s11192-009-0146-3 20585380 PMC2883932 van Eck NJ, Waltman L (2010) Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 84:523–538 20585380 10.1007/s11192-009-0146-3 PMC2883932

Vargas T Moreno-Rubio J Herranz J Cejas P Molina S González-Vallinas M Genes associated with metabolic syndrome predict disease-free survival in stage II colorectal cancer patients. A novel link between metabolic dysregulation and colorectal cancer Mol Oncol 2014 8 1469 1481 10.1016/j.molonc.2014.05.015 25001263 PMC5528602 Vargas T, Moreno-Rubio J, Herranz J, Cejas P, Molina S, González-Vallinas M et al (2014) Genes associated with metabolic syndrome predict disease-free survival in stage II colorectal cancer patients. A novel link between metabolic dysregulation and colorectal cancer. Mol Oncol 8:1469–1481 25001263 10.1016/j.molonc.2014.05.015 PMC5528602

Wan S He Q-Y Yang Y Liu F Zhang X Guo X Sparc stabilizes ApoE to induce cholesterol-dependent invasion and sorafenib resistance in hepatocellular carcinoma Cancer Res 2024 84 1872 1888 10.1158/0008-5472.CAN-23-2889 38471084 Wan S, He Q-Y, Yang Y, Liu F, Zhang X, Guo X et al (2024) Sparc stabilizes ApoE to induce cholesterol-dependent invasion and sorafenib resistance in hepatocellular carcinoma. Cancer Res 84:1872–1888 38471084 10.1158/0008-5472.CAN-23-2889

Wang Y-B Zhou B-X Ling Y-B Xiong Z-Y Li R-X Zhong Y-S Decreased expression of ApoF associates with poor prognosis in human hepatocellular carcinoma Gastroenterol Rep 2019 7 354 360 10.1093/gastro/goz011 PMC6821331 31687155 Wang Y-B, Zhou B-X, Ling Y-B, Xiong Z-Y, Li R-X, Zhong Y-S et al (2019) Decreased expression of ApoF associates with poor prognosis in human hepatocellular carcinoma. Gastroenterol Rep 7:354–360 10.1093/gastro/goz011 PMC6821331 31687155

Wang Z Chen H Sun L Wang X Xu Y Tian S Uncovering the potential of APOD as a biomarker in gastric cancer: a retrospective and multi-center study Comput Struct Biotechnol J 2024 23 1051 1064 10.1016/j.csbj.2024.02.015 38455068 PMC10918487 Wang Z, Chen H, Sun L, Wang X, Xu Y, Tian S et al (2024) Uncovering the potential of APOD as a biomarker in gastric cancer: a retrospective and multi-center study. Comput Struct Biotechnol J 23:1051–1064 38455068 10.1016/j.csbj.2024.02.015 PMC10918487

Ward PS Thompson CB Metabolic reprogramming: a cancer hallmark even warburg did not anticipate Cancer Cell 2012 21 297 308 10.1016/j.ccr.2012.02.014 22439925 PMC3311998 Ward PS, Thompson CB (2012) Metabolic reprogramming: a cancer hallmark even warburg did not anticipate. Cancer Cell 21:297–308 22439925 10.1016/j.ccr.2012.02.014 PMC3311998

Wickham H. (2009) ggplot2: Elegant Graphics for Data Analysis [Internet]. New York, NY: Springer; [cited 2025 Feb 24]. Available from: 10.1007/978-0-387-98141-3

Wong LL Bruxvoort CG Cejda NI Delaney MR Otero JR Forsthoefel DJ Intestine-enriched apolipoprotein b orthologs are required for stem cell progeny differentiation and regeneration in planarians Nat Commun 2022 13 3803 10.1038/s41467-022-31385-2 35778403 PMC9249923 Wong LL, Bruxvoort CG, Cejda NI, Delaney MR, Otero JR, Forsthoefel DJ (2022) Intestine-enriched apolipoprotein b orthologs are required for stem cell progeny differentiation and regeneration in planarians. Nat Commun 13:3803 35778403 10.1038/s41467-022-31385-2 PMC9249923

Wu CT Cheng Y-H Chen F-N Chen D-R Wei MF Chang N-W Combined effects of peroxisome proliferator-activated receptor alpha and apolipoprotein E polymorphisms on risk of breast cancer in a Taiwanese population J Invest Med 2012 60 1209 1213 10.2310/JIM.0b013e31826be86a 23076161 Wu CT, Cheng Y-H, Chen F-N, Chen D-R, Wei MF, Chang N-W (2012) Combined effects of peroxisome proliferator-activated receptor alpha and apolipoprotein E polymorphisms on risk of breast cancer in a Taiwanese population. J Invest Med 60:1209–1213 10.2310/JIM.0b013e31826be86a 23076161

Yan S Pang M Wang J Chen R Liu H Xu X A comparative study of bibliometric analysis on old adults’ cognitive impairment based on Web of Science and CNKI via CiteSpace Health Econ Rev 2023 13 56 10.1186/s13561-023-00470-7 38041746 PMC10693164 Yan S, Pang M, Wang J, Chen R, Liu H, Xu X et al (2023) A comparative study of bibliometric analysis on old adults’ cognitive impairment based on Web of Science and CNKI via CiteSpace. Health Econ Rev 13:56 38041746 10.1186/s13561-023-00470-7 PMC10693164

Yang Y Liu X Yang D Li L Li S Lu S Interplay of CD36, autophagy, and lipid metabolism: insights into cancer progression Metabolism 2024 155 155905 10.1016/j.metabol.2024.155905 38548128 Yang Y, Liu X, Yang D, Li L, Li S, Lu S et al (2024a) Interplay of CD36, autophagy, and lipid metabolism: insights into cancer progression. Metabolism 155:155905 38548128 10.1016/j.metabol.2024.155905

Yang S Du J Wang W Zhou D Xi X APOC1 is a prognostic biomarker associated with M2 macrophages in ovarian cancer BMC Cancer 2024 24 364 10.1186/s12885-024-12105-z 38515073 PMC10956310 Yang S, Du J, Wang W, Zhou D, Xi X (2024b) APOC1 is a prognostic biomarker associated with M2 macrophages in ovarian cancer. BMC Cancer 24:364 38515073 10.1186/s12885-024-12105-z PMC10956310

Yeung AWK Comparison between Scopus, Web of Science, PubMed and publishers for mislabelled review papers Curr Sci 2019 116 1909 1914 10.18520/cs/v116/i11/1909-1914 Yeung AWK (2019) Comparison between Scopus, Web of Science, PubMed and publishers for mislabelled review papers. Curr Sci 116:1909–1914

Yi J Ren L Wu J Li W Zheng X Du G Apolipoprotein C1 (APOC1) as a novel diagnostic and prognostic biomarker for gastric cancer Ann Transl Med 2019 7 380 10.21037/atm.2019.07.59 31555694 PMC6736826 Yi J, Ren L, Wu J, Li W, Zheng X, Du G et al (2019) Apolipoprotein C1 (APOC1) as a novel diagnostic and prognostic biomarker for gastric cancer. Ann Transl Med 7:380 31555694 10.21037/atm.2019.07.59 PMC6736826

Yin Z-Y He S-M Zhang X-Y Yu X-C Sheng K-X Fu T Apolipoprotein B100 acts as a tumor suppressor in ovarian cancer via lipid/ER stress axis-induced blockade of autophagy Acta Pharmacol Sin 2025 10.1038/s41401-024-01470-x 39880927 PMC12032235 Yin Z-Y, He S-M, Zhang X-Y, Yu X-C, Sheng K-X, Fu T et al (2025) Apolipoprotein B100 acts as a tumor suppressor in ovarian cancer via lipid/ER stress axis-induced blockade of autophagy. Acta Pharmacol Sin. 10.1038/s41401-024-01470-x 39880927 10.1038/s41401-024-01470-x PMC12032235

Yiu CCP Sasano H Ono K Chow LWC Changes in protein expression after neoadjuvant use of aromatase inhibitors in primary breast cancer: a proteomic approach to search for potential biomarkers to predict response or resistance Expert Opin Investig Drugs 2010 19 Suppl 1 S79 89 10.1517/13543781003701011 20374034 Yiu CCP, Sasano H, Ono K, Chow LWC (2010) Changes in protein expression after neoadjuvant use of aromatase inhibitors in primary breast cancer: a proteomic approach to search for potential biomarkers to predict response or resistance. Expert Opin Investig Drugs 19(Suppl 1):S79-89 20374034 10.1517/13543781003701011

Yu G Wang L-G Han Y He Q-Y Clusterprofiler: an R package for comparing biological themes among gene clusters OMICS 2012 16 284 287 10.1089/omi.2011.0118 22455463 PMC3339379 Yu G, Wang L-G, Han Y, He Q-Y (2012) Clusterprofiler: an R package for comparing biological themes among gene clusters. OMICS 16:284–287 22455463 10.1089/omi.2011.0118 PMC3339379

Zamanian-Daryoush M Lindner D Tallant TC Wang Z Buffa J Klipfell E The cardioprotective protein apolipoprotein A1 promotes potent anti-tumorigenic effects J Biol Chem 2013 288 21237 21252 10.1074/jbc.M113.468967 23720750 PMC3774392 Zamanian-Daryoush M, Lindner D, Tallant TC, Wang Z, Buffa J, Klipfell E et al (2013) The cardioprotective protein apolipoprotein A1 promotes potent anti-tumorigenic effects. J Biol Chem 288:21237–21252 23720750 10.1074/jbc.M113.468967 PMC3774392

Zhang X Zhao X-W Liu D-B Han C-Z Du L-L Jing J-X Lipid levels in serum and cancerous tissues of colorectal cancer patients World J Gastroenterol 2014 20 8646 8652 10.3748/wjg.v20.i26.8646 25024621 PMC4093716 Zhang X, Zhao X-W, Liu D-B, Han C-Z, Du L-L, Jing J-X et al (2014) Lipid levels in serum and cancerous tissues of colorectal cancer patients. World J Gastroenterol 20:8646–8652 25024621 10.3748/wjg.v20.i26.8646 PMC4093716