Abstract
Epigenetics shapes all aspects of cancer occurrence and progression. Among them, chromatin methylation is a particularly important regulator of oncogenic processes. The dynamic shifts in methylation can influence the development or death of tumor cells. In the therapeutic context, effectively targeting and modulating DNA, RNA, and histone methylation brings promise for the early detection, prognostic assessment, and treatment of cancer. In particular, novel methylation inhibitors or modulators powerfully kill tumor cells or improve existing therapeutic strategies through different signaling pathways, thereby improving patient prognosis and survival. The discussion of the role of methylation in early cancer screening, therapeutic resistance in tumors, approaches to treating cancer, and cancer patient prognosis, while also providing data about the clinical application of methylation-focused strategies and interventions. These efforts are shaped by an overall framework focused on the mechanisms responsible for regulating methylation and targeting biomarkers. Together, this article provides insights into how methylation-driven approaches can be leveraged for early cancer detection, overcoming therapy resistance, and tailoring personalized treatments.
Keywords: Cancer, Epigenetics, Methylation, Treatment, Resistance
Highlights
Methylations (DNA/RNA/histone) are closely related to the occurrence, development, and inhibition of cancer.
Methylation is a powerful method for the early detection and diagnosis of cancer.
Methylation inhibitors and modulators offer new strategies for cancer treatment.
Methylation-related enzymes can regulate treatment resistance, mainly affecting Wnt/β-catenin, TKR, and JAK/STAT pathways.
Methylation of genes can be used as a biomarker to predict patient survival.
Introduction
Cancer poses a severe global public health challenge, ranking as the second leading cause of death and accounting for approximately one in six deaths worldwide [1]. The difficulty of early detection, coupled with the inherent proliferative and drug-resistant properties of tumor cells, contributes to persistently high cancer incidence and mortality rates. Despite significant advances in traditional treatments (surgery, radiotherapy, chemotherapy, and hormone therapy) [2, 3] and the transformative impact of immunotherapy and targeted therapies [1, 3], mortality rates remain unchanged for many patient populations. Consequently, there is an urgent need to develop innovative therapies or combination strategies to achieve long-term survival benefits for patients.
Epigenetic modifications refer to stable nucleic acid modifications that do not alter the underlying DNA sequence [4]. As a key chemical modification, methylation directly shapes cellular characteristics and guides differentiation by regulating gene expression and maintaining genomic stability [5]. Human understanding of methylation began with the discovery of 5-methylcytosine (5mC) in 1925 [6]. Subsequent decades of research identified major forms of RNA methylation [7] and histone methyltransferases [8]. In 1983, scientists first demonstrated the close association between DNA methylation and cancer, revealing that cancer genomes exhibit hypomethylation compared to normal cells [9, 10]. Subsequently, abnormal histone modifications [11] and RNA methylation [12] were also confirmed to contribute to tumorigenesis. Abnormal epigenetic alterations permeate the entire course of cancer development. Reports indicate that over 300 genes undergo epigenetic modifications across various cancers [13, 14], with these alterations closely linked to cellular proliferation, developmental abnormalities, invasion and metastasis, and treatment response [15]. Methylation-associated proteins serve as key regulators in tumor pathology, exhibiting both oncogenic and tumor-suppressing functions depending on the specific tumor context.
Given that methylation profoundly influences the entire process of cancer progression, investigating its biological functions and identifying key target genes are crucial for elucidating cancer pathogenesis. Screening and therapeutic strategies targeting methylation hold promise for improving patient outcomes [5]. This review focuses on the relevance of methylation to cancer detection, treatment, drug resistance, and prognostic assessment, outlining its current clinical applications and future prospects to provide scientific evidence for clinical decision-making.
Methylation and cancer
Methylation is a biochemical process wherein methyl groups are catalytically transferred from an active methyl compound to other macromolecules or chemicals, thereby forming methylated proteins and nucleic acids [16]. In biological systems, proteins involved in the methylation process include methyltransferases (‘writers’), demethylases (‘erasers’), and proteins with methylation-dependent binding activity (‘readers’). These proteins collaborate to shape various biological processes, including the regulation of gene expression, the control of protein function, and RNA processing [16]. As an essential nucleic acid and protein modification, methylation can suppress gene expression and is closely linked to the occurrence and development of diseases such as aging, Alzheimer’s disease, cancer, and necroptosis making it one of the most extensively studied epigenetic modifications currently [17–21]. DNA methylation, RNA methylation, and histone methylation are the most common types of methylation in biological systems [20].
DNA methylation
DNA methylation refers to the process catalyzed by methyltransferases such as DNMT1, DNMT3A, DNMT3B, and DNMT3L, wherein a methyl group (-CH3) is added to the 5′ position of cytosine within CpG islands (primarily located in transcriptional start sites or promoter regions). This leads to the formation of 5mC residues and significantly impacts tumor onset and development. Specifically, DNA hypomethylation can promote the transcriptional activation of oncogenes, whereas DNA hypermethylation can inhibit the expression of tumor suppressor genes [17, 22, 23]. These epigenetic changes can alter genomic stability, thereby serving as a cancer-related risk factor. TET dioxygenase and thymine DNA glycosylase mediate DNA demethylation and may also contribute to oncogenic progression [24, 25]. Although the mechanistic processes surrounding m6A methylation are still poorly understood, decreases in genomic m6A levels have been shown to promote tumor development in humans [26]. To date, three classes of DNA methylation readers have been established, including members of the methyl-CpG-binding domain (MBD), Kaiso, and set ring finger-associated (SRA) families [5] (Fig. 1). For further details, see reference [27].
Fig. 1.
Main types and mechanisms of methylation. A DNA methylation. The main methylation sites are continuous extensive CpG islands via DNA methyltransferases (DNMTs) and DNA demethylation (Ten-eleven translocation [TET] dioxygenases and thymine DNA glycosylase [TDG] are responsible for); Unmethylated and hemimethylated sites are also affected. In addition, 6-methyladenosine (m6A) has also been found to be methylated. B RNA methylation. Major sites contain more targetable nucleotides (A, G, C, or U) and methylated products (Cm, Gm, and Um), which stand for 2′-O methylations of indicated ribose. C Histone methylation. The main methylation sites are in the amino-terminal tails of histones H3 and H4, as well as the associated methyltransferase (blue), demethylase (orange), and reader (green)
Many tumor-related genes are characterized by hypermethylation or hypomethylation of CpG islands in their promoter regions, which respectively lead to the inhibition or enhancement of gene expression. DNA hypermethylation is a common feature of tumor suppressor genes such as CDKH2A, CDH1, CDH13, and APC in colorectal cancer (CRC), prostate cancer, lung cancer, and breast cancer [28]; CDKN2A in leukemia [29]; RASSF1 in breast, prostate, lung, hematological, and CRC [28]; SET9 and PAX1 in cervical cancer [30, 31]; and BRCA1 in ovarian and breast cancer [32, 33]. Conversely, hypomethylation of normally inactive regions of the DNA can support tumor development. For instance, in breast and ovarian cancers, for example, Sat2 and Satα DNA hypomethylation are associated with disease progression and poorer prognosis [34–36]. DNA hypomethylation can also lead to the re-expression of human papillomavirus (HPV) and Epstein-Barr virus (EBV), thereby triggering the onset or progression of lymphoma and cervical cancer [37, 38]. Similar effects have also been observed for MAGE and ALKBH1 in CRC [39, 40], MAGE in melanoma [41], MUC1 in ovarian cancer [42], and SNCG in gastric, ovarian, and breast cancers [43, 44]. Tumorigenesis may also stem from changes in the regulation of DNA methylation or the hypermethylation of one copy of a particular tumor suppressor gene while the other copy harbors a mutation or genomic loss [45, 46].
RNA methylation
RNA methylation refers to the chemical modification of RNA species (mainly tRNA and rRNA, the rest including mRNA, microRNA [miRNA], and lncRNA, etc.) at particular bases (m6A, N1-methyladenosine [m1A], 5-methylcytidine (m5C), N3-methylcytosine [m3C], N7-methylguanosine [m7G], 2′-O-methylation [2′-O-Me], pseudouridine [Ψ], and adenosine-to-inosine [A-to-I]) by methyltransferases such as METTL3, METTL14, and METTL16. Specific demethylases, including ALKBH5, FTO, IGF2BP1, and YTHDF, can demethylate these RNA species, reversing the effects of such modification [47–51]. RNA modifications can shape the expression of particular genes and the differentiation of specific cell populations by altering target genes and inducing oncogenic signaling pathway activity, functioning in a manner that supports or impairs the tumor cell development, migration, metabolic activity, invasion, stem cell-like activity, and therapeutic resistance [48, 49, 52–54] (Fig. 1). RNA-related species play a key role in the development of cancer, and an in-depth investigation of their mechanisms is of great significance for understanding RNA methylation, cancer diagnosis, and cancer treatment. miRNAs play fundamental regulatory roles in shaping cell activity, including development, differentiation, proliferation, apoptosis, and genome stability, through specific base pairing with target transcripts and/or protein molecules, as well as transcriptional and post-transcriptional regulation of their expression [55, 56].
miRNAs can contribute directly to tumorigenesis in different tissues by acting as tumor suppressors (such as miR-15a and miR-16-1) or oncogenes (such as clusters miR-17-92 and miR-155) [57, 58]. Additionally, miRNAs are considered markers of genetic susceptibility to cancer [59, 60]. In addition to their effects on cancer cell function, miRNAs can also modulate the function of other cells in the tumor microenvironment. Extracellular miRNAs can influence intercellular communication and cooperate with stromal cells and extracellular matrix components to create a suitable microenvironment for cancer cell growth and escape immune response [61]. LncRNAs have proven to be involved in every hallmark of cancer cells, from their intrinsic ability to proliferate and survive, to increased metabolism, to their relationship with the tumor microenvironment. Early evidence that lncRNAs in cancer comes from their transcriptional regulation by key carcinogenic or tumor-suppressing transcription factors (such as p53, MYC, estrogen receptor) or signaling cascades (such as the Notch pathway) [62–67]. These lncRNAs contribute to the functional output of carcinogenic or tumor suppressor responses. Carcinogenic lncRNAs include BOK-AS, CDKN2B-AS1, GAS5, HIF1A, HOTAIR, lincRNA-p21, LSINCT5, P21-AS, PTCSC3, TUG1, and UCA1 [68]. Tumor suppressor lncRNAs include BGL3, DILC, DLEU1, DLEU2, GAS5, MEG3, NBAT-1, and TERRA, etc. New lncRNAs are still being discovered [69]. Abnormal expression, mutations, and single-nucleotide polymorphisms (SNPs) of lncRNAs are associated with tumorigenesis and metastasis [70]. There are relatively few studies on the relevant mechanisms. For example, SNP rs11672691 interacts with HNRNPAB through PCAT19-long to activate, thereby promoting the growth and metastasis of prostate cancer [71]; SNP rs12517403 increases the risk of cervical cancer by reducing SP1 binding and lowering CARMN expression [72]; The occurrence of SNP rs7463708 mutation can more easily regulate the upregulation of PCAT1 expression and promote the proliferation of prostate cancer cells [73]; SNP rs6695584 mutation activation activates the expression of lncSLCC1, thereby driving the glycolysis pathway and accelerating the growth of rectal cancer tumor cells [74]; The SNP rs11655237 mutation leads to the reduction of LINC00673 (G), which inhibits the proliferation process of pancreatic cancer cells [75]. Identifying shared genetic mechanisms has brought new opportunities for the development of therapeutic targets and diagnostic tools. Future research should focus on functional validation and the investigation of environmental interactions to fully leverage these findings to advance cancer management. A more comprehensive overview of RNA methylation has been published previously [76].
The precise functions of the downstream targets of RNA methylation ultimately determine whether it plays a protective or counterproductive role in the context of tumor development and progression. Among them, the m6A modification mediated by METTL and the m5C modification mediated by NSUN have received more widespread attention. For instance, overexpression of the m6A methyltransferase METTL3 is evident in breast, colon, liver, and stomach cancers, wherein it can suppress SOCS2 or let-7 g expression to support proliferative tumor growth [77–80]. METTL3-mediated m6A modification upregulates NRXN3, promoting peritoneal metastasis of CRC [81]; Upregulation of NFE2L3 regulates the WNT signaling pathway, promoting the dryness and progression of lung adenocarcinoma cells [82]; Up-regulating IGF2BP2 and activating the PIK3CA/AKT pathway promote the malignant progression of esophageal cancer [83]; Upregulation of LINC00857 enhances the stemness and metastasis of ovarian cancer cells by activating the YAP-TEAD pathway [84]; Up-regulating TMOD4 promotes the growth and metastasis of liver cancer [85]. NSUN2 is an m5C methyltransferase with overexpression in breast and colorectal cancers that may function by suppressing miR-125b to inhibit GAB2 and other factors to support migratory and proliferative growth [86–88]. NSUN2-mediated m5C modification upregulates TRIM28 and promotes the progression of prostate cancer [89]; Up-regulating PKM2 promotes glycolysis and progression of hepatocellular carcinoma (HCC) [90]; Upregulation of KDM6B promotes bone metastasis of breast cancer [91]; Upregulation of SREBP2 promotes the progression of esophageal squamous cell carcinoma [92]; Up-regulating PGK1 activates the PI3K/AKT pathway, promoting the growth and invasion of gastric cancer [93]. Furthermore, the tRNA m1A methyltransferase TRMT6/TRMT61A/TRMT10C can promote HCC development through its ability to induce the PPARδ and c-Myc proto-oncogenes, similarly enhancing ovarian and cervical tumor growth [94, 95]. A-to-I RNA editing in glioblastoma can promote ADAR1 and ADAR3 upregulation, in turn leading to higher levels of GM2A expression and a reduction of the expression of GRIA2, which ultimately promotes migratory, invasive, and proliferative growth, while ADAR2 downregulation yields the opposite effect [96–98].
Histone methylation
The methylation of histones is essential for the control of transcriptional activity and DNA replication. Methylation is a fundamental regulatory mechanism that controls transcription and replication. Histone methyltransferases (HMTs) catalyze this process, including histone lysine methyltransferases and protein/histone arginine methyltransferases (PRMTs). They transfer the methyl group from S-adenosine methionine (SAM) to histones, yielding methylated histones and S-adenosine homocysteine (SAH). The removal of these methyl residues is mediated by HDMs, including members of the LSD (LSD1/2) and JmjC (JHDM1/2/3, JARID1, JMJD, PHF8, UTX/UTY, PHF8) families [99].
These epigenetic modifications predominantly occur on lysine and arginine residues of histone 3 and histone 4 (H3/H4), although other modifications are observed at lower levels. Arginine residues can be either monomethylated or demethylated. PRMTs can simultaneously transfer two methyl groups to the same nitrogen atom at the end of an arginine polypeptide to produce asymmetric methylarginine, or add one methyl group to each end of the nitrogen atom to generate symmetric dimethylarginine. In contrast, lysine residues can be methylated once, twice, or three times [20, 100]. Lysine and arginine methylation reader proteins include members of the ‘royal’ domain superfamily (Tudor, chromo, MBT, and PWWP) with plant homeodomain (PHD) zinc finger domains or Tudor and PHD zinc finger domains, respectively [5]. Abnormally regulated HMT and HDM activity or expression can lead to genome-wide shifts in histone methylation, which may impact tumor suppressor or oncogene expression, potentially contributing to tumor progression [20, 100] (Fig. 1). For a more in-depth discussion of histone methylation, see references [101, 102].
The ability of histone methylation to suppress or enhance gene expression depends on both the location and magnitude of such methylation. Specific functional studies have been performed on many different histone residues (including H3K4/H3K9/H3K17/H3K27/H3K36/H3K56/H3K79/H4K5/H4K20/H3R2/H3R8/H3R17/H3R26/H4R3). For instance, reduced levels of EZH2 can reduce H3K27me3 levels to induce the expression of miR-139-5p, ultimately protecting against pancreatic cancer onset. KRAS can reverse the effects of EZH2 and increase NFATc1 expression through reductions in H3K27me3 levels and concomitant increases in H3K4me3 levels, favoring pancreatic oncogenic progression [103, 104]. The absence of KMT3A can trigger significantly reduced H3K36me3 levels, thereby upregulating disheveled segment polarity protein 22, in turn enhancing Wnt/β-catenin signaling activity to support CRC development [105]. PRMT1 can catalyze the asymmetric demethylation of H4K3 to upregulate genes associated with the Notch pathway, thereby promoting esophageal cancer onset and progression [106]. The upregulation of G9a (also referred to as EHMT2) can trigger enhanced H3K9 and H3K27 methylation, leading to reduced expression of E-cadherin and epithelial-mesenchymal transition activity in PANC-1 pancreatic cancer cells [107, 108].
Methylation in the detection and diagnosis of cancer
Many cancers only present with symptoms following primary tumor metastasis, emphasizing the need to develop early screening strategies that can accurately detect and diagnose specific malignancies before they progress. Research focused on the mechanisms governing tumorigenesis has largely focused on measurable biomarkers detectable in biopsy samples (tissues or shed cells), systemic circulation (plasma or whole blood), secreted/excreted samples (urine, sputum, or stool), and associated lavage fluids [109]. Liquid biopsy strategies are increasingly becoming popular noninvasive approaches to cancer screening. One promising liquid biopsy strategy focuses on the analysis of cell-free DNA (cfDNA) in the systemic circulation, which includes circulating tumor-derived DNA (ctDNA), which may aid in the early detection and diagnosis of cancer [110]. Necrotic or apoptotic tumor cells can release ctDNA, which is also actively secreted in some instances, providing detailed information regarding the genomic makeup of both primary and secondary tumors [111]. Currently, unrestricted methylation-based approaches are the most widely utilized screening and early diagnostic strategies, as the abnormal methylation patterns that arise during early stages of tumorigenesis are generally tissue- and cancer-type-specific [112, 113]. Patterns of methylation are often highly prevalent across tumor tissue and found over large regions of the genome, in contrast with somatic mutations that are only present in particular tumor clones [113, 114]. Increasingly robust standardized approaches to capturing cfDNA in blood and other biofluids have enhanced the feasibility of analyzing DNA methylation levels for important locations in the genome (Fig. 2). Analyzing methylation status can provide an effective means of detecting and diagnosing tumors early during their development. Methylation-based research efforts can be broadly classified into genome-wide methylomics and site-specific methylation analyses [100]. In addition, exosomes are also promising detection modes. Exosomes are extracellular vesicles secreted into body fluids by living cells. Exosomes from tumor tissues contain DNA, RNA, proteins, lipids, and other substances that can reflect tumor information and provide basic analysis for tumor diagnosis, genetic analysis, and epigenetic analysis [115]. The advantage of exosome liquid biopsy is that it provides more comprehensive tumor information than ctDNA, and the tumor information contained in exosomes is more stable and not easily degraded [116]. However, the early application of exosomes in liquid biopsy for cancer diagnosis is a preliminary exploration stage, such as exosome DNA and RNA mutations, transcriptome analysis, and exosome protein analysis [115]. Current studies have found that double-stranded DNA in tumor-derived exosomes represents the entire genome and reflects the mutated state of tumors in melanoma [117], and exosome miRNA-10b-5p and exosome miRNA-21 have high diagnostic sensitivity and are independent risk factors for predicting poor prognosis of liver cancer [118, 119]. More experiments are needed to verify their effectiveness and determine the target of a comprehensive analysis.
Fig. 2.
The relationship between ctDNA and methylation in patients with cancer and methylation detection techniques. ctDNA are obtained from the patient’s tissue/shed cells), blood (whole blood or plasma), secretions/excreta (stool, urine, or sputum), and corresponding lavage fluid and analyzed to identify various DNA changes in the tumor (including gene mutations, gene fusions, copy number variations, and methylation). Further, modern research techniques are used to analyze methylation to screen and identify cancer, among which the cutting-edge research techniques for studying methylation can be divided into two categories (genome-wide methylomics and site-specific methylation detection)
DNA methylation is the most widely used epigenetic marker for the detection and diagnosis of cancer, in part because it persists well under typical experimental conditions and under routine storage, in addition to being common throughout the human genome (Fig. 2). The stability of DNA methylation, cancer-specific patterns of methylation, and the accessibility of cfDNA make it a particularly promising target for screening and diagnostic applications. In a screening capacity, while false negative or false positive results can occur, analyses of DNA methylation can afford greater specificity and sensitivity through the simultaneous analysis of multiple targets. Certain DNA methylation-related epigenetic biomarkers have been clinically implemented to date, including PAX1 in oral and cervical cancer, GSTP1 in prostate cancer, Twist1 in bladder cancer, ONECUT2 in urothelial carcinoma, SHOX2 in lung cancer, RNF180 in gastric cancer, and Septin9 in CRC. Over 30 diagnostic kits on the market have received approval from the Food and Drug Administration (FDA), Communate Europpene (CE), and National Medical Products Administration (NMPA) [120–146] (Table 1).
Table 1.
Approved methylation kits/biomarkers for cancer diagnosis/detection with performances
| Cancer | Gene target | Sample type | Sensitivity, % | Specificity, % | References |
|---|---|---|---|---|---|
| Bladder cancer | Twist1 | Urine | 88.20 | 86.80 | [112] |
| Bladder cancer | ZNF154 | Urine | 84.00 | 96.00 | [113] |
| Bladder cancer | OTX1/ONECUT2/TWIST1/FGFR3mut/TERTmut/HRAS | Urine | 93.00 | 86.00 | [114] |
| Bladder cancer | 15 proprietary markers (Bladder EpiCheck) | Urine | 68.20 | 88.00 | [115] |
| Cervical cancer | PAX1 | Exfoliated cell | 100 | 84.70 | [116] |
| Cervical cancer | PCDHGB7 | Exfoliated cell | 96.00 | 94.30 | [117] |
| Cervical cancer | PAX1/ZNF582 | Exfoliated cell | 88.24 | 91.89 | [118] |
| Cervical cancer | PAX1/JAM3 | Exfoliated cell | 74.10 | 95.90 | [119] |
| Cervical cancer | FAM19A4/hsa-mir124-2 | Brush self | 73.30 | 66.20 | [120] |
| Cervical cancer | POU4F3/positive hrHPV test | Exfoliated cell | 67.50 | 82.30 | [121] |
| Cervical cancer | ASTN1/DLX1/ITGA4/RXFP3/SOX17/ZNF671 | Exfoliated cell | 85.00 | 85.40 | [122] |
| Colorectal cancer | SDC2 | Faeces | 90.20 | 90.20 | [123] |
| Colorectal cancer | SDC2/TFPI2 | Faeces | 96.40 | 96.60 | [124] |
| Colorectal cancer | SDC2/SFRP2 | Faeces | 83.81 | 93.94 | [125] |
| Colorectal cancer | SDC2/NPY/FGF5/PDX1 | Faeces | 91.17 | 91.92 | [113] |
| Colorectal cancer | SDC2/ADHFE1/PPP2R5C | Faeces | 84.80 | 98.00 | [126] |
| Colorectal cancer | BMP3/NDRG4/FIT/KRASmut | Faeces | 91.94 | 87.08 | [113] |
| Colorectal cancer | Septin9 | Blood | 48.20 | 91.50 | [127] |
| Colorectal cancer | Septin9/SDC2/BCAT1 | Blood | 83.70 | 93.90 | [128] |
| Colorectal cancer | Septin9/BCAT1/IKZF1/BCAN/VAV3 | Blood | 87.3 | 91.10 | [113] |
| Lung cancer | SHOX2/RASSF1A | BALF | 88.24 | 81.25 | [129] |
| Lung cancer | SHOX2/PTGER4 | BALF | 67.00 | 90.00 | [130] |
| Lung cancer | SHOX2/RASSF1A/PTGER4 | Blood | 87.00 | 98.00 | [131] |
| Lung cancer | 6 proprietary markers (Lung EpiCheck) | Blood | 84.30 | 77.70 | [132] |
| Gastric cancer | RNF180/Septin9 | Blood | 62.20 | 84.80 | [133] |
| Gastric cancer | Reprimo/SDC2/TCF4 | Blood | 93.39 | 80.33 | [134] |
| Liver cancer | mSEPT9 | Blood | 91.00 | 81.00 | [135] |
| Liver cancer | BMPR1A/PLAC8 | Blood | 95.42 | 96.02 | [113] |
| Prostate cancer | GSTP1/RASSF1/APC | Tissue | 78.00 | 60.00 | [136] |
| Prostate cancer | GSTP1/SFRP2/IGFBP3/IGFBP7/APC/PTGS2 | Urine | 73.00 | 76.00 | [137] |
| Oral cancer | PAX1/ZNF582 | Exfoliated cell | 87.00 | 86.00 | [138] |
| Urothelial carcinoma | ONECUT2/VIM | Urine | 89.74 | 92.46 | [112] |
BALF bronchoalveolar lavage fluid
DNA methylation is a key determinant of transcriptional activity and is closely tied to the process of oncogenesis. Therefore, studies of the methylation status of particular genes are quite common, revealing that changes in DNA methylation can be leveraged to detect tumor development independent of age, sex, lesion location, or other factors. These diagnostic approaches offer the advantages of being sensitive, specific, noninvasive, easy to implement, and compatible with early detection, such that they are more convenient than MRI- or CT-based screening protocols.
Methylation and the treatment of cancer
Aberrant epigenetic modifications and regulatory activity can profoundly shape the behaviors of tumor cells and the surrounding tumor microenvironment (TME), highlighting the promising potential of targeting these factors in the context of cancer therapy. Cellular reprogramming involves irregular epigenomic changes that modulate cellular signaling pathways and tumor-associated gene expression, thereby creating conditions conducive to tumor growth and metastasis [147]. Given that writer, reader, and eraser proteins are the key mediators of epigenetic changes, the development of novel drugs targeting these proteins represents a promising avenue for cancer treatment, which is currently being actively investigated in numerous preclinical and clinical trials. Below, we provide a discussion of epigenetic inhibitory compounds that have already undergone clinical testing for their ability to treat cancer.
DNA methylation inhibitors
DNMT inhibitors represent the most extensively developed class of epigenetic inhibitory drugs to date, having attracted substantial interest and undergone testing in several clinical trials. The nucleoside analogs 5-azacytidine (ZCyt, Azacytidine), 5-aza-2′-deoxycytidines (ZdCyd), and 5-fluoro-2′-deoxycytidine (FdC, Decitabine) can be incorporated into RNA and DNA, thereby interfering with DNA, RNA, and protein biosynthesis [148]. When cells are treated with these compounds at higher doses, toxic compounds are generated that disrupt de novo thymidylate synthesis and lead to increased cytotoxicity [149]. These compounds are also capable of irreversibly binding to and inactivating DNMT (5mC), yielding a mutagenic or toxic complex [149]. These drugs have received FDA approval to treat myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML), achieving respective complete response rates as high as 54% and 100%. Furthermore, favorable effects have also been reported when these drugs are used at low doses in patients with chronic myeloid leukemia (CML) and hemoglobinopathy [150].
The high susceptibility of these inhibitors to deaminase-mediated degradation led to the development of guadecitabine (SG-110), which consists of decitabine linked to deoxyguanosine via a phosphodiester bond [151]. The resultant compound possesses a distinct dinucleotide structure that protects against drug removal through deamination of decitabine, thereby affording superior biostability. When used for treatment, the gradual release of the active metabolite of guadecitabine, decitabine, can initiate associated inhibitory processes [152]. This drug has received approval as a first-line salvage therapy for AML and MDS, although randomized controlled trials have reported that it affords similar survival benefits to those associated with azacytidine and decitabine in randomized controlled trials [153]. While the single-agent efficacy of DNMT inhibitors in hematological malignancies can be quite high, their efficacy in solid tumors remains very limited. This discrepancy is attributable to the distinct features of solid tumors, including metabolic instability, poor cellular nutritional status, and limited binding to cellular DNA. The development of modified cytidine analogs with enhanced stability or the combination of drugs with other therapeutic agents may provide an opportunity to achieve improved efficacy while avoiding the toxic side effects associated with high-dose cytidine analog administration (common adverse events include bone marrow suppression, gastrointestinal reactions, fatigue, weakness, and allergic reactions) [154, 155]. Consequently, numerous studies have explored innovative DNMT inhibitors or combination treatment strategies (Table 2).
Table 2.
Single targeting methylations used in cancer
| Inhibitor target | Sensitizer | Cancer | Phase, Years | Clinical trial number |
|---|---|---|---|---|
| DNA methyltransferases | ||||
| DNMT inhibitor | fluoro-2-deoxycytidine (FdC)/Tetrahydrouridine | Advanced solid tumours | II, 2006 | NCT00359606 |
| DNMT inhibitor | Azacitidine (ZCyt) | Acute myeloid leukemia and myelodysplastic syndrome | II, 2008 | NCT00666497 |
| DNMT inhibitor | 5-fluoro-2-deoxycytidine/Tetrahydrouridine | Lung, breast, bladder, and head and neck cancer | II, 2009 | NCT00978250 |
| DNMT inhibitor | 5-fluoro-2-deoxycytidine/Tetrahydrouridine | Advanced solid tumours | II, 2012 | NCT01534598 |
| DNMT inhibitor | 4′-thio-2′-deoxycytidine | Advanced solid tumours | I, 2015 | NCT02423057 |
| DNMT inhibitor | Guadecitabine (SG-110)/DLI | Acute myeloid leukemia | II, 2016 | NCT02684162 |
| DNMT inhibitor | 5-aza-4′-thio-2′-deoxycytidine | Advanced solid tumours | I, 2017 | NCT03366116 |
| DNMT inhibitor | Decitabine (5-aza-2′-deoxycytidine [ZdCyd]) | Acute myeloid leukemia with complex or monosomal karyotype | II, 2017 | NCT03080766 |
| DNMT inhibitor | Guadecitabine | Paraganglioma, gastrointestinal stromal tumours, phaeochromocytoma, hereditary leiomyomatosis, and renal cell carcinoma | II, 2017 | NCT03165721 |
| DNMT inhibitor | Guadecitabine | Acute myeloid leukemia | III, 2017 | NCT02920008 |
| DNMT inhibitor | Decitabine | BAP1-associated malignancies | II, 2023 | NCT05960773 |
| RNA methyltransferases | ||||
| FTO inhibitor | Meclofenamate | Advanced solid tumours with brain metastasis | NA, 2015 | NCT02429570 |
| FTO inhibitor | Bisantrene | Acute myeloid leukemia | II, 2019 | NCT03820908 |
| METTL3 inhibitor | STC-15 | Advanced solid tumours | I, 2022 | NCT05584111 |
| Histone methyltransferases (lysine demethylasear/ginine methyltransferase/H3K27 methyltransferase/H3K79 methyltransferase/H3K36 methyltransferase) | ||||
| LSD1 inhibitor | Seclidemstat (SP-2577) | Advanced solid tumours | I, 2019 | NCT03895684 |
| LSD1 inhibitor | INCB059872 | Ewing sarcoma | I, 2018 | NCT03514407 |
| LSD1 inhibitor | INCB059872 | Acute myeloid leukemia, small cell lung cancer, myelofibrosis, Ewing sarcoma and poorly differentiated neuroendocrine tumours | I/II, 2016 | NCT02712905 |
| LSD1 inhibitor | Bomedemstat (IMG-7289/MK-3543)/ATRA | Acute myeloid leukemia | I, 2016 | NCT02842827 |
| LSD1 inhibitor | Pulrodemstat Celgene (CC-90011) |
Non-Hodgkin lymphoma and advanced solid tumours |
I, 2016 | NCT02875223 |
| LSD1 inhibitor | Phenelzine | Prostate cancer | II, 2014 | NCT02217709 |
| LSD1 inhibitor | GSK2879552/ATRA | Acute myeloid leukemia | I, 2014 | NCT02177812 |
| LSD1 inhibitor | GSK2879552 | Small cell lung cancer | 1, 2013 | NCT02034123 |
| LSD1-HDAC6 inhibitor | JBI-802 | Advanced solid tumours | I/II, 2022 | NCT05268666 |
| PRMT5 inhibitor | GSK3326595 | Early stage breast cancer | II, 2020 | NCT04676516 |
| PRMT5 inhibitor | AZD3470 | MTAP deletion advanced solid tumours | I/IIa, 2023 | NCT06130553 |
| PRMT5 inhibitor | AZD3470 | Haematologic malignancies | I/II, 2023 | NCT06137144 |
| PRMT5 inhibitor | SCR-6920 | Advanced solid tumours | I, 2022 | NCT05528055 |
| PRMT5 inhibitor | PF-06939999 (PRMT5-IN-3) | Non-small cell lung cancer, head and neck squamous cell cancer, esophageal cancer, endometrial cancer, cervical cancer, and bladder cancer | I, 2019 | NCT03854227 |
| PRMT5 inhibitor | TNG462 | MTAP deletion advanced solid tumours | I/II, 2023 | NCT05732831 |
| PRMT5 inhibitor | TNG908 | MTAP deletion advanced solid tumours | I/II, 2022 | NCT05275478 |
| PRMT5 inhibitor | PRT543 | B-cell non-Hodgkin lymphoma and advanced solid tumours | I, 2019 | NCT03886831 |
| PRMT5 inhibitor | PRT811 | Advanced solid tumors, central nervous system lymphoma, and recurrent high-grade gliomas | I, 2019 | NCT04089449 |
| PRMT5 inhibitor | JNJ-64619178 | B-cell non-Hodgkin lymphoma and advanced solid tumours | I, 2018 | NCT03573310 |
| PRMT5 inhibitor | AMG 193 | MTAP deletion advanced thoracic tumors | Ib, 2024 | NCT06333951 |
| PRMT5-MTA inhibitor | MRTX1719 | MTAP deletion advanced solid tumours | I/II, 2022 | NCT05245500 |
| EZH2 inhibitor | Tazemetostat (EPZ-6438) | B-cell lymphoma with EZH2 mutation | II, 2018 | NCT03456726 |
| EZH2 inhibitor | Tazemetostat | Malignant mesothelioma with BAP1 mutant | II, 2016 | NCT02860286 |
| EZH2 inhibitor | Tazemetostat | Follicular lymphoma with EZH2 mutation | II, 2022 | NCT05467943 |
| EZH2 inhibitor | Tazemetostat | Follicular lymphoma with EZH2 mutation | Observational, 2022 | NCT05228158 |
| EZH2 inhibitor | Tazemetostat | Follicular lymphoma | II, 2023 | NCT06068881 |
| EZH2 inhibitor | Tazemetostat | Follicular lymphoma | Ib/III, 2019 | NCT04224493 |
| EZH2 inhibitor | Tazemetostat | Metastatic malignant peripheral nerve sheath tumors | II, 2021 | NCT04917042 |
| EZH2 inhibitor | Tazemetostat | INI1-negative tumors and synovial sarcoma | II, 2015 | NCT02601950 |
| EZH2 inhibitor | Tazemetostat | INI1-negative tumors and synovial sarcoma | I, 2015 | NCT02601937 |
| EZH2 inhibitor | Tazemetostat | Diffuse large B-cell lymphoma and advanced solid tumours | I/II, 2013 | NCT01897571 |
| EZH2 inhibitor | Tazemetostat | Advanced solid tumours | Expanded Access, 2019 | NCT03874455 |
| EZH2 inhibitor | Tazemetostat | Epithelioid Sarcoma | Expanded Access, 2020 | NCT04225429 |
| EZH2 inhibitor | Tazemetostat | Advanced solid tumors, non-hodgkin lymphoma, and histiocytic disorders With EZH2, SMARCB1, or SMARCA4 mutations | II, 2017 | NCT03213665 |
| EZH2 inhibitor | Tazemetostat | B-cell lymphoma and advanced solid tumours | I, 2016 | NCT03010982 |
| EZH2 inhibitor | CPI-1205 | B-cell lymphoma | II, 2015 | NCT02395601 |
| EZH2 inhibitor | CPI-0209 | Advanced solid tumors and lymphomas | I/II, 2019 | NCT04104776 |
| EZH2 inhibitor | AXT-1003 | Non-hodgkin lymphomas | I, 2023 | NCT05965505 |
| EZH2 inhibitor | SHR2554 | Mature lymphoid neoplasms | I, 2018 | NCT03603951 |
| EZH2 inhibitor | PF-06821497 | Small cell lung cancer,castration-resistant prostate cancer, and follicular lymphoma | I, 2018 | NCT03460977 |
| EZH2 inhibitor | GSK2816126 | Diffuse large B-Cell lymphoma, transformed follicular lymphoma, non-Hodgkin’s lymphomas, solid tumors, and multiple myeloma | I, 2014 | NCT02082977 |
| EZH2-1 inhibitor | Valemetostat Tosylate (DS-3201b) | Peripheral T-cell lymphoma | II, 2021 | NCT04703192 |
| EZH2-1 inhibitor | Valemetostat Tosylate | T-cell leukemia/lymphoma | II, 2019 | NCT04102150 |
| EZH2-1 inhibitor | Valemetostat Tosylate | B-cell lymphoma | II, 2021 | NCT04842877 |
| EZH2-1 inhibitor | HH2853 | Non-hodgkin’s lymphomas and advanced solid tumors | I/II, 2020 | NCT04390737 |
| EED inhibitor | MAK683 | Diffuse large B-cell lymphoma, nasopharyngeal carcinoma, and other advanced solid tumors | I/II, 2016 | NCT02900651 |
| EED inhibitor | ORIC-944 | Metastatic prostate cancer | I/Ib, 2022 | NCT05413421 |
| EED inhibitor | APG-5918 | Nasopharyngeal carcinoma, castrate-resistant prostate cancer, gastric cancer, ovarian clear cell carcinoma, non-hodgkin lymphomama, B-cell lymphoma, mesothelioma sarcoma, and epithelioid sarcoma | I, 2022 | NCT05415098 |
| DOT1L inhibitor | Pinometostat (EPZ-5676) | Paediatric MLL-rearranged leukemias | Ib, 2014 | NCT02141828 |
| DOT1L inhibitor | Pinometostat | Advanced hematologic malignancies with 11q23-rearrangement | I, 2012 | NCT01684150 |
| NSD2 inhibitor | KTX-1001 | Multiple myeloma | I, 2022 | NCT05651932 |
| SETD2 inhibitor | EZM0414 | Multiple myeloma and diffuse large B-Cell lymphoma | I/Ib, 2021 | NCT05121103 |
Trial information is taken from ClinicalTrials. gov
DNMT DNA methyltransferase, BAP1 BRCA1-associated protein-1, DLI donor lymphocyte infusion, FTO fat mass and obesity, METTL3 methyltransferase like 3, LSD1 Lysine-specific demethylase 1, ATRT all-trans retinoic acid, PRMT5 protein arginine methyltransferase 5, MTA methylthioadenosine, MTAP methylthioadenosine phosphorylase, EZH1/2 enhancer of zeste homolog 1/2, EED embryonic ectoderm development, DOT1L histone 3 lysine 79 methyltransferase, NSD 2 nuclear receptor-binding SET domain-containing 2, SETD 2 SET domain containing 2
Histone methylation inhibitors
Similar to DNA, histones are subject to extensive epigenetic modification at many residues. Methylation and acetylation are the predominant classes of histone modification [156], and histone regulatory proteins are among the most commonly dysregulated proteins in many cancers, providing a wealth of therapeutic targets, many of which can impact histone methylation. These targets include specific histone lysine methyltransferases (EZH1/2 and H3K79 methyltransferases), PRMT5, LSD1, and less common targets such as SETD2, NSD2, and EED (Table 2). Among these targets, inhibitors of EZH proteins are the most extensively studied [157, 158].
EZH is a subunit of polycomb repressor complex 2 (PRC2) that inhibits Hox gene expression [159, 160]. Proteins in the polycomb group (PcG) serve as suppressors of transcriptional activity through their ability to regulate chromatin remodeling [161]. PRC2 can promote chromatin compression and inhibit transcription through the catalysis of H3K27 methylation [162]. H3K27 can undergo acetylation (H3K27ac) or mono-, di-, or trimethylation (H3K27me1/2/3), with PcG activity being characterized primarily by the H3K27me2 and H3K27me3 modification of gene promoters. H3K27me1 and H3K27ac are generally believed to facilitate transcription [163]. PRC2 is comprised of four subunits, including EZH1/2, EED, SUZ12, and RbAp46/48, with the first three of these proteins functioning to mediate the inhibition of H2K27 methylation-inducing genes, while also shaping the onset of a range of diseases through the effects of particular cofactors (AEBP2, C10or12, C17orf96, JARID2, PCL1-3, and RBBP4/7) [164]. In animal model studies, the loss of EZH2 has been demonstrated to impair development and lead to death, with deceased mice exhibiting normal EZH1 expression, thus supporting a vital role for EZH2 in the epigenetic functions of PRC2 [165, 166]. However, some reports have demonstrated that EZH1 may also function as a weak epigenetic regulator when low levels of EZH2 expression are evident [167]. EZH2 is also an important regulator of the DNA damage repair and cell cycle progression [168, 169].
The EZH2 protein, comprising over 700 amino acids, is encoded by a gene on chromosome 7q35-36 [170]. The final protein consists of multiple domains, including the HMT-activating SET domain [170]. In the context of oncogenesis, the effects of EZH proteins can be both PRC-dependent and PRC-independent. For instance, H2AK-119ub1, H3K27me3, and PRC1 can together form a complex that disrupts chromatin remodeling and inhibits the transcription of downstream genes, leading to their inactivation of silencing. The acceleration of ribosomal activity and translational regulation, as well as the phosphorylation of particular residues (such as Y705 of STAT3), can also promote the development of tumors, including prostate cancer, gastric cancer, and glioblastoma [170, 171]. During the chromatin remodeling process, EZH2 is involved in a regulatory relationship with the SWI/SNF polyprotein complex, with high EZH2 activity levels inhibiting PRC2-regulated target gene expression in stem/progenitor cells [172, 173]. This finding has been reported in many tumor types, including endometrial, ovarian, and malignant rhabdomyoid tumors [174, 175]. Conversely, low levels of EZH2 activity can lead to SWI/SNF activation and PRC2 target gene upregulation, contributing to cellular differentiation and dormancy [172, 173]. EZH1/2 can stably interact with p85β and USP7 or ERβ components within the nucleus to support H3K27me3 modification and global transcriptional expression in a manner conducive to CRC and breast cancer development [176, 177]. Major EZH inhibitors currently under investigation include 3-deazaneplanocin A (DZNep), which is an SAH hydrolase inhibitor that functions by upregulating SAH and suppressing SAM-dependent HMT activity, ultimately suppressing the growth and progression of tumors [178]. In 2020, the FDA approved tazemetostat (EPZ-6438) for the treatment of patients with epithelioid sarcoma and follicular lymphoma, and it has reportedly achieved objective response rates of 15–69% and a progression-free survival interval of 5.5–13 months [179, 180].
Low molecular weight ligands capable of binding to H3K27me3 pockets of EED have also been reported to influence cancer-related outcomes. As a WD40-containing protein, EED can bind to H3K27me3 residues via an aromatic cage known as an H3K27me3 pocket, leading to an allosteric increase in PRC2 activity that can propagate H3K27 methylation [181, 182]. Inhibitors of EED can suppress the methyltransferase activity of PRC2 methyltransferase containing EZH1/EZH2, thereby suppressing the proliferation of tumor cell lines harboring mutations in EZH2, including lines resistant to SAM-competitive EZH2 inhibitors [183]. EED inhibitors can also trigger rapid proteasomal degradation of EED, EZH2, and SUZ12, thereby selectively inhibiting the proliferation of PRC2-dependent tumor cells [184, 185]. Thus, EED inhibitors such as MAK683 may be promising anticancer agents.
To date, over 9 human PRMTs have been identified that are capable of catalyzing methyl group transfer from SAM to the arginine residues of target substrate proteins [186]. These PRMTs are broadly classified into the type I (asymmetric dimethylarginine, ADMA), type II (symmetric dimethylarginine, SDMA), and type III (monomethylarginine) categories [186]. As a type II arginine methyltransferase, PRMT5 can bind to MEP50, forming a stable functional complex that supports cancer cell proliferation [187, 188]. The loss of the 2-methylthioadenosine metabolism regulator methylthioadenosine phosphorylase (MTAP) in somatic cells can disrupt SDMA functionality, reversing the proliferation of cancer cells [187]. The highly conserved HMT DOT1L catalyzes H3K79 methylation to regulate the activation of transcription and elongation [189]. In patients harboring MLL-rearrangement (MLL-r), fusion proteins lead to the aberrant chromatin localization of DOT1L-containing multi-subunit complexes, resulting in ectopic H3K79 methylation and enhanced expression of leukemia-related genes [190, 191]. Pinometostat is a DOT1L inhibitor that can selectively suppress H3K79 methylation, suppressing the transcriptional activation of MLL-r target genes, including MEIS1 and HOXA9, to alter tumor cell development [192].
Lysine demethylases are classified based on their sequences and mechanistic functions into the LSD and JmjC-containing lysine demethylase subfamilies [193]. LSD1 and LSD2 are members of the larger flavin adenine dinucleotide (FAD)-dependent amine oxidase superfamily, whereas JmjC lysine demethylases are FeII/2-oxoglutarate (2-OG)-dependent enzymes responsible for catalyzing mono-, di-, and trimethyllysine demethylation [193]. LSD1 utilizes FAD as a cofactor and binds to CoREST, HDAC, C-terminal binding, and nucleosome remodeling proteins to facilitate the demethylation of particular histone H3 residues, including H3K4me1/2 and H3K9me1/2, thereby activating or inhibiting particular cancer-related genes [194]. Molecularly distinct inhibitors of LSD1 can simultaneously interact with multiple regions of this complex, thereby synergistically inhibiting the transcriptional activation of tumor suppressor genes through combination treatment [195]. A limited number of NSD-2 and SETD-2 inhibitors, which act on H3K36, have also been discovered and have made their way into clinical trials.
Inhibitors of RNA methylation
To date, there has only been relatively limited success in the development and clinical application of RNA methylation inhibitors. FTO and METTL3 inhibitors are the only small molecules that have been widely utilized in research settings. FTO is a non-heme FeII/α-ketoglutaric acid (α-KG)-dependent dioxygenase AlkB protein family member responsible for the in vitro oxidation and demethylation of m3T and m3U in single-stranded DNA and RNA sequences [196, 197]. The AlkB family proteins ABH1, ABH2, and ABH3 can reportedly oxidize demethylated N-methylated DNA/RNA clips [198, 199]. FTO is also capable of removing m6A residues from nuclear RNA and mRNA transcripts via α-KG and Fe II-dependent mechanisms [200]. Consequently, inhibitors of these processes have shown promise as antitumor agents in preclinical settings. Bisantrene is an FTO inhibitor that has recently been evaluated in clinical trials for the treatment of hematologic malignancies, potentially due to suboptimal efficacy outcomes in solid tumors [201–203]. Meclofenamate, another FTO inhibitor, exhibited significant clinical efficacy when used to treat MGMT-methylated glioblastoma or brain metastases [204]. This may be attributed to the fact that this compound is a nonsteroidal anti-inflammatory drug capable of interfering with microtubule growth, which is crucial for glioblastoma cell communication, thereby compromising the morphological and functional integrity of the cellular network in glioblastoma [204].
The 580-amino-acid METTL3 protein consists of a methyltransferase domain and a zinc finger domain, both of which are essential for its enzymatic activity [205]. MT-A70 is a key methyltransferase subunit capable of forming a methyltransferase complex (MTC) with METTL14 or other binding partners, producing a heterodimeric core component of this multi-protein complex that can guide the transcription of m6A-modified RNAs [206, 207]. This can enable selective m6A deposition at m6A sites enriched in mRNA coding sequences and 3′-untranslated regions to influence cancer outcomes [208]. In preclinical studies, the small molecule inhibitor STM2457 of METTL3 has demonstrated effectiveness in anti-tumor treatment and synergy with other therapeutic approaches. It can suppress the growth and progression of non-small cell lung cancer (NSCLC) [209]; Inhibit the DNA repair mechanism and thereby increase cisplatin sensitivity in ovarian cancer [210]; Inhibit the CXCL5/CCL5 pathway and increase immune infiltration, thereby synergizing with PD-1 to enhance the anti-tumor effect [211]; Down-regulating MCL1 and MYC thereby increased the venetoclax-induced apoptosis of leukemia cells [212]; And inhibit the proliferation of esophageal squamous cell carcinoma by activating the DNA damage response through the ATM-Chk2 axis [213]. Another METTL3 inhibitor, STC-15, has also been advanced into clinical trials for the management of solid tumors (Table 2). Given the early promise of these RNA methylation-related inhibitory drugs, further efforts to develop innovative drugs and combination regimens are crucial to achieving better therapeutic efficacy in patients with cancer.
Epigenetic diet
Furthermore, the new term “epigenetic diet” refers to a class of bioactive dietary compounds, such as isothiocyanates in broccoli, genistein in soybeans, and resveratrol in red grapes, which are common components in foods [214]. These components can alter the epigenome and bring about beneficial health effects. Tumor progression can be inhibited by modulating epigenetic modification enzymes such as DNA methyltransferases and histone deacetylases, as well as certain non-coding RNAs [214–216]. Current research focuses mainly on preclinical studies. For example, green tea polyphenols (dietary DNMTi) plus sulforaphane (dietary histone deacetylase inhibitor) or soybean isoflavone (genistein, dietary HDACi) enable ERα reactivation to upregulate p21/KLOTHO or enhance the efficacy of the estrogen antagonist tamoxifen, thereby exhibiting potential for the treatment and prevention of ERα-negative human breast cancer [217, 218]; Large amounts of short-chain fatty acids in dietary fiber (mainly propionic acid and butyric acid, dietary HDACi) can inhibit the expression of tumor suppressor genes such as MYC, FOS, and JUN, thereby inhibiting the proliferation of colorectal cancer cells [219]; The anthocyanin delphinidin (dietary DNMTi) activates the Nrf2-ARE pathway and can be used as a potential chemoprophylaxis agent for skin cancer [220]; Anthocyanin-rich bilberry extract induces acute lymphocyte apoptosis through redox-sensitive epigenetic modifications (DNA and histone methylation-related factors) [221]; A diet deficient in methyl groups and folic acid can significantly reduce the level of S-adenosylmethionine in the livers of male rats and mice, leading to the formation of liver cancer [222]; Curcumin alters the expression profile of miRNAs both in vivo and in vitro, and the formation of m6A is regulated by the binding of miRNAs to mRNA substrates through METTL3, thereby influencing cancer activity [223]; Cyanidin 3-glucoside upregulates the expression of lncRNA MALAT1 and nRNA tubulin γ 1, increases the level of miR-125b, and thereby regulates the cell cycle of liver cancer cells to inhibit the occurrence of liver cancer [224]; Delphinidin may inhibit the occurrence of breast cancer through the HOTAIR/miR-34a axis and exert anti-cancer effects [225]; Pomegranate polyphenolics (ellagitannins and anthocyanins) prevent colon tumor occurrence by inhibiting miR-126/VCAM-1 and miR-126/PI3K/AKT/mTOR signaling [226]. Only one clinical trial has reported that black raspberries can demethylate tumor suppressor genes and regulate other biomarkers of human colon and rectal tumor development [227]. Although epigenetic diets hold broad prospects in cancer prevention and treatment, it is still necessary to address individual differences, verify effects in long-term large-sample human clinical trials, and optimize dosage and combination regimens. In conclusion, epigenetic diets offer new insights and strategies for cancer prevention and treatment. In the future, they are expected to become an important component of precision medicine, working in synergy with traditional treatment methods to improve the survival rate and quality of life of cancer patients.
Combination therapies
Despite the development of novel therapies predicated on a refined understanding of the epigenetic mechanisms governing tumorigenesis, the heterogeneous and dynamic nature of tumors often precipitates therapeutic resistance. This resistance typically emerges from alterations in signaling pathways, changes in drug target expression, mutations, or other adaptive mechanisms [228, 229]. Consequently, overcoming such resistance to achieve superior long-term survival thus requires a comprehensive characterization of the functional interactions between different drug combinations, thereby enabling the rational design and selection of small-molecule agents. In many prior studies, epigenetic therapies have demonstrated reciprocal sensitizing effects when combined with other forms of treatment. To date, several clinical trials have explored various combinations of epigenetic drugs with radiotherapy, chemotherapy, targeted therapy, and immunotherapy approaches (Table 3). Data from ClinicalTrials.gov showed that combination therapy was significantly associated with improved overall response rate in cancer patients, increasing it by more than 10%. In particular, the combination with immunotherapy has demonstrated superior survival benefits. However, many clinical trials fail, withdraw, or terminate. The primary factors contributing to these setbacks in combinatorial strategies include severe toxicity, premature progression, and early occurrence of treatment-related deaths.
Table 3.
Combinations targeting methylations used in cancer
| Inhibitor target | Sensitizer | Cancer | Phase, Years | Clinical trial number |
|---|---|---|---|---|
| DNMT/PD1 inhibitors | Guadecitabine/Nivolumab | Metastatic colorectal cancer | Ib/II, 2018 | NCT03576963 |
| DNMT/PD1 inhibitors | Guadecitabine/Pembrolizumab | Recurrent ovarian, primary peritoneal, and fallopian tube cancer | II, 2016 | NCT02901899 |
| DNMT/PD1 inhibitors | Guadecitabine/Pembrolizumab | Non-small cell lung cancer and castrate-resistant prostate cancer | I, 2017 | NCT02998567 |
| DNMT/PD1 inhibitors | Decitabine/Pembrolizumab | Non-small cell lung cancers, esophageal carcinomas, and pleural mesotheliomas | I/II, 2017 | NCT03233724 |
| DNMT/PD1 inhibitors | Decitabine/Nivolumab | Non-small cell lung cancer | II, 2016 | NCT02664181 |
| DNMT/PD1 inhibitors | Decitabine/Nivolumab | Mucosal melanoma | I/II, 2016 | NCT05089370 |
| DNMT/PD1 inhibitors | Decitabine/Camrelizumab | Classical hodgkin lymphoma | II/III, 2020 | NCT04510610 |
| DNMT/PD1 inhibitors | Decitabine/Camrelizumab | Hodgkin lymphoma | II, 2017 | NCT03250962 |
| DNMT/PD1 inhibitors | Decitabine/Camrelizumab | Acute myeloid leukemia | II, 2020 | NCT04353479 |
| DNMT/PD1 inhibitors | Decitabin/Anti-PD1 | Relapsed/refractory malignancies | I/II, 2016 | NCT02961101 |
| DNMT/PD1 inhibitors | Azacitidine/Pembrolizumab | Non-small cell lung cancer | II, 2015 | NCT02546986 |
| DNMT/PD1 inhibitors | Azacitidine/Pembrolizumab | Metastatic melanoma | II, 2017 | NCT02816021 |
| DNMT/PDL1-TGFβ inhibitors | Azacitidine/Bintrafusp Alfa | Epithelial malignancies (excluding lung and renal cell carcinomas) and pulmonary metastases | I/II, 2020 | NCT04648826 |
| LSD1/PD1 inhibitors | Seclidemstat/Pembrolizumab | Ovarian and endometrial cancer | I, 2020 | NCT04611139 |
| LSD1/PD1 inhibitors | INCB059872/Nivolumab | Small cell lung cancer | I/II, 2016 | NCT02712905 |
| LSD1/PD1 inhibitors | CC-90011/Nivolumab | Advanced solid tumours | II, 2020 | NCT04350463 |
| PRMT5/PD1 inhibitors | AMG 193/Pembrolizumab | MTAP deletion advanced thoracic tumors | Ib, 2024 | NCT06333951 |
| EZH2/PD1 inhibitors | Tazemetostat/Pembrolizumab | Advanced non-small cell lung cancer | Ib/II, 2022 | NCT05467748 |
| EZH2/PD1 inhibitors | Tazemetostat/Pembrolizumab | Urothelial carcinoma (stage III/IV bladder Cancer AJCC v8) | I/II, 2019 | NCT03854474 |
| EZH2/PD1 inhibitors | XNW5004/Pembrolizumab | Head and neck squamous cell carcinoma, urothelial carcinoma, prostate cancer, small-cell lung cancer, non-smallcell lung cancer, cervical cancer, and other solid tumors | Ib/II, 2023 | NCT06022757 |
| EZH2-1/PD1 inhibitors | Valemetostat/Pembrolizumab | HPV-negative recurrent/metastatic head and neck squamous cell carcinoma | Ib/II, 2023 | NCT05879484 |
| DNMT/PDL1 inhibitors | Guadecitabine/Atezolizumab | Acute myeloid leukemia with myelodysplasia-related changes | Ib/II, 2016 | NCT02935361 |
| DNMT/PDL1 inhibitors | Guadecitabine/Atezolizumab | Advanced urothelial carcinoma | I/II, 2017 | NCT03179943 |
| DNMT/PDL1 inhibitors | Guadecitabine/Durvalumab | Advanced renal cancer | Ib/II, 2017 | NCT03308396 |
| DNMT/PDL1 inhibitors | Guadecitabine/Durvalumab | Hepatocellular carcinoma, pancreatic adenocarcinoma and cholangiocarcinoma | Ib, 2018 | NCT03257761 |
| LSD1/PDL1 inhibitors | Bomedemstat/Atezolizumab | Extensive-stage small cell lung cancer | I/II, 2021 | NCT05191797 |
| LSD1/PDL1 inhibitors | Iadademstat/Atezolizumab or Durvalumab | Extensive-stage small cell lung cancer | II, 2024 | NCT06287775 |
| EZH2/PDL1 inhibitors | Tazemetostat/Atezolizumab | Diffuse large B-cell lymphoma | I, 2014 | NCT02220842 |
| EZH2/PDL1 inhibitors | Tazemetostat/Durvalumab | Advanced solid tumors, adult solid tumor, colorectal carcinoma, soft-tissue sarcoma, and pancreatic adenocarcinoma | II, 2021 | NCT04705818 |
| DNMT/PDL1 or CTLA4 | Azacitidine/Durvalumab or Tremelimumab | Head and neck cancer | I/II, 2017 | NCT03019003 |
| DNMT/CTLA4 inhibitors | Guadecitabine/Ipilimumab | Metastatic melanoma | I, 2015 | NCT02608437 |
| DNMT/CTLA4 inhibitors | Decitabine/Ipilimumab | Acute myeloid leukemia | I, 2017 | NCT02890329 |
| EZH2/CTLA4 inhibitors | CPI-1205/Ipilimumab | Advanced solid tumours | I/II, 2017 | NCT03525795 |
| EZH2-1/PD1 inhibitors | Valemetostat/Ipilimumab | Metastatic aggressive variant prostate, urothelial, and renal cell carcinomas | I, 2020 | NCT04388852 |
| EZH2/PDL1-TGFβ inhibitors | SHR2554/SHR1701 | Advanced solid tumors and B-cell lymphomas | I/II, 2020 | NCT04407741 |
| DNMT inhibitor/Chemotherapy | Guadecitabine/Cladribine/Idarubicin | Untreated adult acute myeloid leukemia | II, 2014 | NCT02096055 |
| DNMT inhibitor/Chemotherapy | Guadecitabine/Azacitidine/Cytarabine | Acute myeloid leukemia | II, 2017 | NCT03164057 |
| DNMT inhibitor/Chemotherapy | Azacitidine/Lenalidomide | Acute myeloid leukemia and myelodysplastic syndromes | II, 2011 | NCT01442714 |
| FTO inhibitor/Chemotherapy | Bisantrene/Fludarabine/Clofarabine | Acute myeloid leukemia | II, 2021 | NCT04989335 |
| LSD1 inhibitor/Chemotherapy | Seclidemstat/Cyclophosphamide/Topotecan | Ewing sarcomas | I, 2018 | NCT03600649 |
| LSD1 inhibitor/Chemotherapy | Bomedemstat/Venetoclax | Acute myeloid leukemia | I, 2022 | NCT05597306 |
| LSD1 inhibitor/Chemotherapy | Tranylcypromine/cytarabine/ATRA | Non-M3 Acute myeloid leukemia | I/II, 2016 | NCT02717884 |
| LSD1 inhibitor/Chemotherapy | Phenelzine Sulfate/Abraxane | Breast cancer | Ib, 2018 | NCT03505528 |
| LSD1 inhibitor/Chemotherapy | Phenelzine Sulfate/Docetaxel | Prostate cancer | II, 2010 | NCT01253642 |
| LSD1 inhibitor/Chemotherapy | Iadademstat/Paclitaxel | Small cell lung cancer and extrapulmonary high grade neuroendocrine carcinomas | II, 2022 | NCT05420636 |
| PRMT5 inhibitor/Chemotherapy | AMG 193/Docetaxel | MTAP deletion advanced solid tumours | I/II, 2021 | NCT05094336 |
| PRMT5 inhibitor/Chemotherapy | AMG 193/Carboplatin/Paclitaxel or Pemetrexed | MTAP deletion advanced thoracic tumors | Ib, 2024 | NCT06333951 |
| EZH2 inhibitor/Chemotherapy | CPI-0209/Carboplatin | Platinum sensitive recurrent ovarian cancer | I, 2023 | NCT05942300 |
| EZH2-EZH1 inhibitor/Chemotherapy | Valemetostat/Irinotecan | Small cell lung cancer | I/II, 2019 | NCT03879798 |
| DOT1L inhibitor/Chemotherapy | Pinometostat/Daunorubicin/Cytarabine | Acute myeloid leukemia with MLL rearrangement | Ib/II, 2019 | NCT03724084 |
| DNMT/HDAC inhibitors | Azacitidine/Vorinostat | Diffuse large B-cell lymphoma | I/II, 2010 | NCT01120834 |
| DNMT/HDAC inhibitors | Guadecitabine/Belinostat | Metastatic primary central chondrosarcoma | II, 2020 | NCT04340843 |
| DNMT/LSD1 inhibitors | Azacitidine/GSK2879552 | Acute myeloid leukemia and myeloproliferative-neoplasms | II, 2017 | NCT02929498 |
| DNMT/LSD1 inhibitors | Azacitidine/Seclidemstat | chronic myelomonocytic leukemia | I/II, 2021 | NCT04734990 |
| DNMT/LSD1 inhibitors | Azacitidine/INCB059872/ATRA | Acute myeloid leukemia | I/II, 2016 | NCT02712905 |
| DNMT/LSD1 inhibitors | Azacitidine/Iadademstat | Acute myeloid leukemia | Ib, 2024 | NCT06357182 |
| DNMT/PARP inhibitors | Decitabine/talazoparib | Acute myeloid leukemia | I/II, 2016 | NCT02878785 |
| DNMT/Tim-3 inhibitors | Azacitidine/Decitabine/TQB2618 | Acute myeloid leukemia and myeloproliferative-neoplasms | I, 2022 | NCT05426798 |
| DNMT/DOT1L inhibitors | Pinometostat and Azacitidine | Acute myeloid leukemia with 11q23-rearrangement | Ib/II, 2018 | NCT03701295 |
| PRMT5/KRASG12C inhibitors | AMG 193/Sotorasib | MTAP deletion advanced thoracic tumors | Ib, 2024 | NCT06333951 |
| EZH2-EZH1/CD20 inhibitors | Valemetostat/Rituximab/Lenalidomide | Follicular lymphoma | I/II, 2023 | NCT05683171 |
| LSD1/FLT3-AXL inhibitors | Iadademstat/Paclitaxel | Acute myeloid leukemia | I, 2022 | NCT05546580 |
| LSD1/BCL-2 inhibitors | Iadademstat/Venetoclax | Acute myeloid leukemia | Ib, 2024 | NCT06357182 |
| EZH2-EZH1 inhibitor/ADC | Valemetostat/Trastuzumab Deruxtecan | HER2 low breast cancer | Ib, 2022 | NCT05633979 |
| EZH2-EZH1 inhibitor/ADC | Valemetostat Tosylate/Trastuzumab Deruxtecan | Advanced solid tumours | Ib, 2024 | NCT06244485 |
| DNMT inhibitor/Androgen receptor | Decitabine/Enzalutamide | Metastatic castrate-resistant prostate cancer | Ib, 2021 | NCT05037500 |
| LSD1 inhibitor/Androgen receptor | CC-90011/Abiraterone | Metastatic castrate-resistant prostate cancer | I, 2020 | NCT04628988 |
| EZH2 inhibitor/Androgen receptor | Tazemetostat/Abiraterone or Enzalutamide | Metastatic castrate-resistant prostate cancer | Ib/II, 2019 | NCT04179864 |
| EZH2 inhibitor/Androgen receptor | CPI-1205/Abiraterone or Enzalutamide | Metastatic castrate-resistant prostate cancer | Ib/II, 2018 | NCT03480646 |
| DNMT inhibitor/NEDD8-activating enzyme | Azacitidine/Pevonedistat | Myeloproliferative-neoplasms | II, 2017 | NCT03238248 |
| EZH2-1 inhibitor/CELMoD | Valemetostat/CC-99282 | Non-hodgkin lymphomas | I/II, 2019 | NCT03930953 |
| DNMT/PDL1/CTLA4 | Azacitidine/Nivolumab/Ipilimumab | Acute myeloid leukemia | I/II, 2017 | NCT03019003 |
| DNMT/PDL1/CTLA4 | Guadecitabine/Nivolumab/Ipilimumab | Melanoma and non-small cell lung cancer | II, 2020 | NCT04250246 |
| DNMT inhibitor/PD1 inhibitor/Radiotherapy | Decitabine/Pembrolizumab/Hypofractionated Index Site Radiation | Pediatric and young adult patients with relapsed and refractory solid tumors or lymphoma | I, 2018 | NCT03445858 |
| DNMT inhibitorinhibitor/PDL1 inhibitor/Vaccine | Guadecitabine/Atezolizumab/CDX-1401 | Recurrent ovarian, primary peritoneal, and fallopian tube cancer | II, 2017 | NCT03206047 |
| DNMT/PD1/HDAC inhibitors | Decitabine/Camrelizumab/Chidamide | Relapsed non-hodgkin’s lymphoma | I/II, 2020 | NCT04337606 |
| DNMT/PD1/HDAC inhibitors | Decitabine/Camrelizumab/Chidamide | Classical hodgkin lymphoma | II, 2020 | NCT04233294 |
| DNMT/PD1/HDAC inhibitors | Decitabine/Camrelizumab/Chidamide | Classical hodgkin lymphoma | II, 2020 | NCT04514081 |
| DNMT/PD1/HDAC inhibitors | Decitabine/Anti-PD1/Chidamide | Classical hodgkin lymphoma | II, 2024 | NCT06393361 |
| DNMT/ICIs/HDAC inhibitors | Decitabine/Anti-PD1/Anti-PDL1/Anti-CTLA4/Chidamide | Non-hodgkin lymphoma and advanced solid tumors | I/II, 2022 | NCT05320640 |
| DNMT/PD1/HDAC inhibitors | Azacitidine/Nivolumab/Entinostat | Non-small cell lung cancer | II, 2013 | NCT01928576 |
| DNMT/PD1/HDAC inhibitors | Guadecitabine/Pembrolizumab/Mocetinostat | Advanced non-small cell lung cancer | I, 2017 | NCT03220477 |
| DNMT/PD1/IDO1 inhibitors | Azacitidine/Pembrolizumab/Epacadostat | Advanced solid tumours | I/II, 2017 | NCT02959437 |
| LSD1/PD1/IDO1 inhibitors | INCB059872/Pembrolizumab/Epacadostat | Advanced solid tumours | I/II, 2017 | NCT02959437 |
| EZH2-1/PDL1/VEGF inhibitors | Valemetostat/Atezolizumab/Bevacizumab | Hepatocellular Carcinoma | Ib/II, 2024 | NCT06294548 |
| DNMT inhibitor/PD1 inhibitor/Chemotherapy | Decitabine/Camrelizumab/Gemcitabine/Vinorelbine/Doxorubicine | Primary mediastinal large B-cell lymphoma | I/II, 2017 | NCT03346642 |
| DNMT inhibitor/PD1 inhibitor/Chemotherapy | Decitabine/Pembrolizumab/Paclitaxel | Metastatic triple-negative breast cancer | I, 2023 | NCT05673200 |
| DNMT inhibitor/PD1 inhibitor/Chemotherapy | Azacitidine/Visilizumab/Homoharringtonine/Cytarabine | Acute myeloid leukemia | III, 2020 | NCT04722952 |
| DNMT inhibitor/LSD1 inhibitor/Chemotherapy | Azacitidine/CC-90011/Venetoclax | Acute myeloid leukemia | I/II, 2021 | NCT04748848 |
| DNMT inhibitor/FTO inhibitor/Chemotherapy | Decitabine/Bisantrene/Cytarabine | Acute myeloid leukemia and myelodysplastic syndromes | I, 2022 | NCT05456269 |
| DNMT/PD1/Tim3 inhibitors | Azacitidine/Decitabine/Spartalizumab/Sabatolimab | Acute myeloid leukemia and high-risk myeloproliferative-neoplasms | Ib, 2017 | NCT03066648 |
| DNMT/PD1/Tim3 inhibitors | Decitabine/PDR001/MBG453 | Advanced malignancies | I-Ib/II, 2015 | NCT02608268 |
| DNMT inhibitor/HDAC inhibitor/CART | Decitabine/Chidamide/CART 19/20 | B-cell non-hodgkin’s lymphoma | I/II, 2020 | NCT04553393 |
| EZH2 inhibitor/CD20 inhibitor/Chemotherapy | Tazemetostat/Rituximab/Bendamustine | Follicular lymphoma | I/II, 2022 | NCT05551936 |
| EZH2/BRAF/MEK inhibitors | Tazemetostat/Dabrafenib/Trametinib | Metastatic melanoma | I/II, 2020 | NCT04557956 |
| DNMT/PDL1/HDAC inhibitors/Chemotherapy | Azacitidine/Durvalumab/Romidepsin/Nab-Paclitaxel | Pancreatic cancer | I/II, 2020 | NCT04257448 |
Trial information is taken from ClinicalTrials. gov
DNMT DNA methyltransferase, LSD1 Lysine-specific demethylase 1, PRMT5 protein arginine methyltransferase 5, MTAP methylthioadenosine phosphorylase, EZH1/2 enhancer of zeste homolog 1/2, DOT1L histone 3 lysine 79 methyltransferase, PD1 programmed death 1, PDL1 programmed cell death ligand 1, CTLA-4 cytotoxic T-lymphocyte antigen 4, TGFβ transforming growth factor-β, PARP poly adenosine diphosphate ribose polymerase, ADC antibody–drug conjugate, NEDD8 neural precursor cell expressed developmentally downregulated 8, CELMoD cereblon E3 ligase modulator, ICIs immune checkpoint inhibitors, HDAC histone deacetylase, IDO1 indoleamine 2,3-dioxygenase 1, VEGF vascular endothelial growth factor, Tim3 T cell immunoglobulin and mucin domain-3, CART chimeric antigen receptor T-cell immunotherapy, MEK methyl ethyl ketone
Immunotherapy
Epigenetic dysregulation profoundly impacts both tumor and immune cell populations within the TME, enabling tumor cells to evade immune-mediated detection and clearance [230, 231]. In immune cells, epigenetic alterations can modulate the expression of key effector cytokines and immune checkpoint molecules, affect MHC-mediated tumor-associated antigen presentation, and overall cancer immune surveillance [232]. Furthermore, malignant cells and other immune cells can exhibit epigenetic adaptations in response to immunotherapeutic interventions, rendering them resistant to immune checkpoint inhibitor-mediated destruction [233]. Consequently, agents capable of inhibiting or modulating methylation hold the potential to enhance T-cells cytotoxicity within the TME while simultaneously disrupting other epigenetic processes conducive to tumor growth, thereby disrupting cancer progression [233]. Inhibitors of DNMTs, LSD1, and EZH2 can suppress endogenous retrovirus (ERV) activity, activating anti-cellular antiviral RNA detection pathways and triggering interferon (IFN)-α production. This leads to interferon-stimulated gene (ISG) upregulation and the induction of proinflammatory chemokines, such as CCL5, CXCL9, and CXCL10 [20, 234, 235]. These inhibitors can also trigger the enhancement of MHC-I-mediated antigen presentation by increasing cancer-testis antigen (CTA) production, upregulating PD-L1, and downregulating Lag3, Tim3, and TIGIT to activate CD8+T cells, triggering their invasion and cytotoxic functions [20, 233]. Additionally, EZH2 inhibitors can also affect regulatory T cells (Treg), natural killer (NK) cells, and myeloid-derived suppressor cells (MDSCs) to enhance T cell proliferation and cytokine expression, thus amplifying the overall immune response [20, 233]. Moreover, EZH2 inhibitors can reverse EZH2-mediated repression and reduce the differentiation of CD4+Th1/Th2 cells, increasing the expression of effector cytokines in CD4+T cells [233]. Inhibitors of FTO and METTL3 induce the upregulation of PD-1 and PD-L1, respectively, in addition to promoting CD8+T cell activation and enhancing immunotherapeutic efficacy [236, 237]. METTL3 inhibitors can also promote increased production of IFN-γ, CXCL9, and CXCL10, leading to enhanced T cell-mediated cytotoxicity [237] (Fig. 3).
Fig. 3.
Combination therapies with targeting methylation. At the moment, many clinical trials of DNA methyltransferase inhibitors (DNMTi), zeste homolog 2 enhancer inhibitors (EZH2i), lysine-specific demethylase 1 inhibitors (LSD1i), fat mass and obesity-associated protein inhibitors (FTOi), and methyltransferase like inhibitors (METTL3i) with Immunotherapy (programmed death-1 [PD-1]/programmed death-ligand 1 [PD-L1], cytotoxic T lymphocyte associate protein-4 [CTLA-4], lymphocyte activation gene 3 [LAG-3], and T cell immunoglobulin domain and mucin domain-3 [Tim-3], radiotherapy, targeted drugs (Anti-CD20 and B-cell lymphoma-2 inhibitors [BCL-2i]), and other epigenetic drugs (histone deacetylase inhibitors [HDACi]) were carried out vigorously. The combination of methylation-related drugs mainly affects the tumor microenvironment (TME). The mechanisms include: increasing and activating CD8+ and CD4+ T cells; Activate antigen processing and presentation mechanisms; and Up-regulated inflammatory genes and pathways that control tumor cell secretion of interferon (IFN), cytokines, and chemokines. Methylation can also manipulate the differentiation and function of tumor-infiltrating immune cells, including T cells, natural killer (NK) cells, macrophages, and bone marrow cells. There are also regulatory T cells (Tregs) that inhibit T cell proliferation and cells, which can be regulated by therapy. It is worth noting that radiotherapy immunity and DNMTi have a synergistic effect against tumors
Radiotherapy
Although radiotherapy exerts limited systemic control over tumor growth, it is highly effective for local disease control. Therefore, innovative combinations of radiotherapy and other therapeutic strategies are vital for achieving systemic control. Clinical trials have demonstrated the utility of epigenetic-modifying drugs as radiosensitizing agents capable of interfering with the cell cycle, impairing DNA damage repair, and inducing oxidative stress, ultimately leading to improved radiotherapy outcomes [238]. These combinations can also enhance tumor antigen release, resulting in superior immunogenicity, and highlighting the potential for combining radiotherapy, epigenetic drugs, and immunotherapy in clinical settings.
Irradiation of tumor cells induces double-stranded DNA break, releases tumor-specific peptide, and activates the cGAS/STING pathway. This results in the production of IFN-α and the subsequent induction of proinflammatory chemokines, including CXCL10 and CXCL16 [239]. Elevated IFN and CTA levels also trigger ISG upregulation, enhancing MHC-I-mediated antigen presentation and PD-L1 expression to promote antitumor immunity [240, 241]. Furthermore, radiotherapy can trigger the production of cancer-specific peptides that can enhance DC-mediated antigen presentation and immune response induction [242]. Radiotherapy can also modulate MDSCs and M1 tumor-associated macrophages (TAMs); inhibition of MDSC leads to increased NK cell numbers and IFN-γ production, while M1 TAMs support CD8+T cell activation, leading to increased antitumor immunity [240–242] (Fig. 3). Radiotherapy-induced double-stranded DNA damage can trigger the MAPK-dependent activation of DNA repair genes, including XRCC1 and ERCC1, within tumor cells. As METTL3 inhibitors suppress MAPK signaling and FTO inhibitors suppress the expression of ERCC1, they possess the potential to modulate radiotherapy outcomes [243–245]. Disruption of the m6A modification of circCUX1 with METTL3 inhibitors can lead to increased secretion of IL-1 and IL-18, potentially contributing to enhanced radiosensitivity [246, 247]. Radiation may also trigger reductions in H3K27me3 levels, which are associated with chromatin condensation and impact the repair of double-stranded DNA breaks. The JMJD3 inhibitor GSKJ4 can increase tumor cell radiosensitivity, whereas the HDM UTX may counteract it [248]. At present, research focused on combining histone and RNA methylation-related modulators with radiotherapy is restricted to preclinical settings, highlighting an opportunity for future clinical development.
Targeted therapy
CD20, a non-glycosylated transmembrane protein of the MS4A family, is expressed by both normal and malignant B cells [249]. CD20 reportedly closely with other tetrameric molecules, including CD81, CD82, and CD53, to form supramolecular complexes [250]. It is capable of physically interacting with MHC-II, CD40, BCR, and CBP, enabling macrophage recognition and altering immune cell activity [251]. The loss of CD20 from CD20+T cell populations may impair T cell-mediated immunity [252]. In addition to regulating Bcl-2, a key apoptosis-related protein, CD20 can be regulated by epigenetic modulators, including HDACs, DNMTs, and EZH2 [208]. DNMT and EZH2 inhibitors are capable of stimulating the expression of CD20 at both the mRNA and the cell surface level, thereby increasing sensitivity to anti-CD20 antibody treatment [208]. Since Bcl-2 is an inhibitor of apoptosis and CD20 can promote the expression of Bcl-2 and other targets, combination treatment with Bcl-2 inhibitors can activate apoptotic pathways to mediate tumor cell killing [253]. Inhibitors of Bcl-2, EZH2, and LSD1 can exert synergistic effects on programmed tumor cell death [254, 255] (Fig. 3).
Epigenetic therapy
The combinatorial use of two or more epigenetic drugs can prolong or broaden therapeutic efficacy and/or reduce associated side effects by allowing for dose reductions. However, the underlying mechanisms of action must be carefully considered to achieve synergistic efficacy (Fig. 3). The combination of DNMT inhibitors with HDAC or LSD1 inhibitors is an area of active research interest. HDACs are enzymes that remove acetyl groups from histones and other proteins [256], and HDAC inhibitors can readily access the nucleus to markedly alter chromatin structure, gene expression, and functional outcomes such as apoptosis or proliferation [257, 258]. Since DNA methylation and histone modifications are two predominant epigenetic mechanisms controlling gene silencing, combinatorial strategies with synergistic efficacy are important. LSD inhibitors are capable of inducing global DNA demethylation or inhibiting LSD1-driven H3K4me2 and H3K4me1 demethylation to establish active chromatin structures [259]. Combination treatment with DNMT inhibitors reportedly yields synergistic effects on abnormally silenced genes via H3K4me2 and H3K4me1 enrichment [259]. Further research aimed at balancing efficacy against adverse events will be vital to achieve more sophisticated and effective synergistic epigenetic treatment outcomes.
Chemotherapy and hormone therapy
In the absence of such intervention, the silencing of the androgen receptor gene, coupled with its functional blockade by hormone therapy, results in increased DNMT activity and expression in tumor cells, leading to impaired antitumor immunity. Thus, combining DNMT inhibitors with hormone therapy can effectively generate antitumor immunity, particularly in patients with prostate cancer. Since methylation inhibitors generally function in a genome-wide manner, they may simultaneously demethylate both tumor suppressor genes and oncogenes, potentially upregulating both and thereby limiting antitumor efficacy. Efforts to improve the gene specificity of these inhibitors may provide a means to enhance antitumor activity while reducing the incidence of adverse events.
Normal cells acquiring advantageous genetic or epigenetic alterations that modify gene expression can undergo clonal selection if these changes confer growth advantages through the silencing or activation of particular genes [260, 261]. Interactions between these processes can lead to chromatin changes, and tumor cell therapeutic sensitivity can be further fueled by genetic and epigenetic pathways under conditions of chemotherapy or hormone treatments [260]. Increased DNMT expression and activity also coincide with upregulation of truncated androgen receptor subtype, which is conducive to the development of a hormone-resistant phenotype [262]. The use of a DNMT inhibitor can markedly reduce hormone resistance resulting from prolonged hormone treatment. In the absence of such intervention, the silencing of the androgen receptor gene, coupled with its functional blockage by hormone therapy, results in increased DNMT activity and expression in tumor cells, leading to impaired antitumor immunity [263, 264]. Thus, combining DNMT inhibitors with hormone therapy can effectively generate antitumor immunity, particularly in patients with prostate cancer. Since inhibitors of methylation generally function in a genome-wide manner, they may simultaneously demethylate both tumor suppressor genes and oncogenes, potentially upregulating both and thereby limiting the antitumor efficacy. Efforts to improve the gene specificity of these methylation inhibitors may provide a means to enhance antitumor activity while reducing the incidence of adverse events.
Methylation, therapeutic resistance, and prognostic outcomes
Therapeutic resistance
Given that therapeutic resistance emerges through a range of complex and dynamic mechanisms, curative drug efficacy remains unsatisfactory for many cancer patients. Methylation studies provide an opportunity to analyze therapeutic resistance, facilitating the reliable selection of treatment regimens suitable for specific patient populations and better guiding clinical decision-making. This section guides the mechanistic link between methylation and therapeutic resistance, with several mechanisms established to date, including ATP-binding cassette (ABC) transporter overexpression, cancer stem cell (CSC) properties, autophagy, apoptosis, cell cycle arrest, gene mutation, DNA damage repair, epithelial-mesenchymal transition (EMT), and TME-related processes (Fig. 4).
Fig. 4.
Mechanisms of treatment resistance. Methylation can modulate various mechanisms to demonstrate therapeutic resistance (including drug resistance and radiological resistance). Including the ability to maintain or enhance the activity and function of cancer stem cells (CSCs); Reduce apoptosis and autophagy, and overexpression of ABC transporter protein, affecting drug delivery; Hypoxia can induce abnormal angiogenesis and change the function of surrounding stromal cells to indirectly regulate the stem cell of cancer cells and thus affect the tumor microenvironment. Can regulate DNA repair pathways or develop genetic mutations that allow them to evade anti-tumor therapy; Epithelial mesenchymal transformation (EMT) is driven to cause the dry features of tumor progression and metastasis; The cell cycle is affected, causing it to become dormant and thus resistant to treatment. It is worth noting that the main mechanisms causing radiotherapy resistance are non-homologous end joining (NHEJ) and homologous recombination (HR), G2/M stagnation and a large number of cells accumulation in S phase, CSCs production, self-inhibiting reactive oxygen species (ROS) production, TME changes, and hypoxia. Most of these processes are due to epigenetic regulation resulting in changes in metabolism or signaling pathway transduction
Drug resistance
Crosstalk between DNMTs and TET family members is closely associated with drug resistance in tumors. CSCs are key mediators of chemoresistance. Through the regulatory effects of DMNT1, BEX1/RUNX3 expression in the brain can activate Wnt/β-catenin signaling and maintain liver stem cell self-renewal, facilitating resistance to sorafenib and chemotherapy [265]. DNMT1 is capable of activating the cell cycle suppressors p21 and p27 via interactions with high mannose type CD133, maintaining glioma stem cell self-renewal and temozolomide resistance while reducing DNA damage [266]. DNA methylation also plays a role in resistance to targeted therapy. DNMT1-mediated miR-34a inhibition and Notch pathway activation can influence pancreatic cancer cell sorafenib sensitivity [267]. The CSC-related DNMT3b/OCT4 genes can promote HCC sorafenib resistance via the activation IL-6/STAT3 axis [268]. TET2 loss and the associated changes in mitogen-activated protein kinase (MEK) activity can mediate EGFR-TKI resistance in NSCLC cells via non-blocking transcriptional inhibition of TNF/NF-κB signaling, primarily through its ability to maintain CSC functionality [269]. DNMT1/UHRF1 complex-dependent promoter methylation suppresses MUC17 expression and NF-κB activity, leading to gefitinib or osimertinib resistance in NSCLC [270] (Fig. 5).
Fig. 5.
Methylation-induced chemotherapy or targeted drug resistance. Drug resistance is attributed to CSCs activity, the occurrence of apoptotic autophagy, EMTs, inhibition of DNA damage repair, and alterations in signal transduction pathways, all of which are regulated by methylation-related enzymes. Maintain the activity and function of CSCs: activation of the Wnt/β-catenin (DNMT1/3B, METTL3, FTO, ALKBH5, and LSD1/2), Notch (DNMT1 and METTL3), tyrosine kinase receptor (TKR) (DNMT3A, SETDB1, and EZH2), NF-κB (DNMT1, TET2, LSD1/2), JAK/STAT (FTO), and IGF-IR (DNMT3B and FTO) pathways; The activation of EMTs: LSD1/2 and PAD4; The inhibition of DNA damage repair: METTL3, KIAA1429, and PRMT5; The activation of autophagy: METTL3, YTHDF1, PRMT5, and PAD4. Other: chemotherapy can directly affect the function of RNA methylase and epithelial growth factor receptor tyrosine kinase inhibitors (EGFR-TKI) can regulate TKR and NF-κB pathways, thereby causing tumor cells to resist chemotherapy and targeted drugs
m6A modification represents the predominant form of RNA methylation implicated in tumor cells' drug resistance [271]. For instance, in breast cancer cells, METTL3 reportedly induces doxorubicin resistance via the miR-221-3p/HIPK2/Che-1, EGF/RAD51, and MALAT1/E2F1/AGR2 axes [272–274]. YTHDC1 can reverse METTL3-mediated EGF and RAD51 upregulation, thereby increasing doxorubicin resistance in breast cancer cells [273]. FTO can activate STAT3 signaling within breast cancer cells, further supporting doxorubicin resistance [275]. ALKBH5 functions as an inhibitor of Wnt signaling through its ability to upregulate Wnt inhibitory factor 1 (WIF-1) rather than function in β-catenin expression, reducing pancreatic ductal adenocarcinoma cell sensitivity to gemcitabine [276]. As an m6A methyltransferase, METTL3 can promote the upregulation of cytidine deaminases or the inhibition of the miR-3163/USP44 axis in pancreatic cancer, giving rise to p56-mediated gemcitabine resistance [277, 278]. SEC62 can activate Wnt/β-catenin signaling to maintain the stem-like functions of CRC cells, contributing to 5-FU resistance in a manner mediated by METTL3 [279]. c-Myc can promote YTHDF1 expression in CRC and inhibit 5-FU responsiveness [280]. Zinc finger CCCH domain-containing protein 13 (ZC3H13) and PHD finger protein 10 (PHF10) are important regulators of pancreatic cancer cells’ gemcitabine susceptibility through their effects on the repair of DNA damage [281].
Both the METTL3-associated sustained activity of the miR-146a/Notch signaling axis and the YTHDF2-mediated suppression of TUSC7 contribute to erlotinib resistance in lung adenocarcinoma cells [282]. METTL3 can promote malignant melanoma cell resistance to the BFAF (V600E) kinase inhibitor PLX4032 through EGFR upregulation and subsequent activation of the RAF/MEK/ERK pathway [283]. METTL3 upregulation can itself promote higher levels of c-MET expression, enhancing signaling via the c-MET-HGF axis and increasing the ability of NSCLC cells expressing high levels of c-MET to tolerate crizotinib [284]. In HCC, METTL3-dependent m6A methylation can modulate circRNA-SORE to facilitate competitive Wnt/β-catenin pathway activation and induce resistance to sorafenib, while also activating CGCs and lenvatinib resistance via the respective FZD10/β-catenin/YAP1 and FZD10/β-catenin/c-Jun/MEK/EPR axes [285, 286]. YTHDF1-dependent METTL3 downregulation can result in the degradation of FOXO3, which is observed in the context of autophagy-mediated sorafenib resistance in HCC [287]. Nilotinib resistance in leukemia is related to FTO overexpression-related m6A hypomethylation [288] (Fig. 5). For further details regarding the association between RNA methylation and drug resistance, refer to the review published by Zhuang et al. [237].
Methylases and demethylases are the primary proteins linking histone methylation to drug resistance. EZH2 hypermethylation can trigger the activation of miR-137/c-Myc cell survival pathways and the PI3K/AKT pathway, promoting gefitinib resistance in NSCLC and cisplatin resistance in ovarian cancer [289, 290]. The hypermethylation of EZH2 can also suppress MEIS1 expression through the activity of the ELN1-AS1/EZH2/DNMT3A axis, leading to oxaliplatin resistance in CRC cells [291]. SETDB1 is capable of inducing the methylation of PELP1/AKT, promoting tamoxifen resistance in breast cancer [292]. Leukemia stem cells are a major cause of resistance to TKIs, and G9A is capable of reducing the expression of the tumor suppressor gene SOX6, thus maintaining leukemia stem cell survival and self-renewal [293]. MTAP-deficient cancer cells exhibit reduced MAT2A/PRMT5/RIOK1 axis activity, whereas class I PRMTs can enhance the ability of NSCLC cells or MTAP-deficient ovarian cancer cells to resist the PARP inhibitor BMN-673 [294, 295]. PRMT5 is capable of inducing abnormal splicing of the DNA repair factor TIP60/KAT5, leading to defective homologous recombination repair that sensitizes acute leukemia cells to the inhibition of PARP [296]. LSD1 regulates therapeutic resistance in many cancer cells through changes in H3K4 and H3K9 methylation levels, primarily by maintaining CSC function; for instance, LSD1 overexpression increases breast cancer stem cell potential [297]. CD44, SOX2, and OCT4 in gastric cancer can promote resistance to oxaliplatin. Wnt/β-catenin pathway activation in thyroid cancer can induce chemotherapy resistance [298]. The enhancement of p65 demethylation with CD13 assistance can activate NF-κB and suppress the expression of spirgle1 and APC, promoting Lgr5 + cell proliferation and ultimately conferring sorafenib resistance to HCC cells [299, 300]. LSD2 may function similarly, as when overexpressed, it upregulates NANOG, SOX2, LSD1, KDM4B, and KDM5B mRNA expression, promoting CSC-like characteristics and drug resistance [301]. LSD2 also plays a role in DNA damage repair and apoptosis, contributing to the resistance of ovarian, pancreatic cancer cells, and multiple myeloma to chemotherapy or proteasome inhibitors [302–304]. LSD1 can also induce chemoresistance in breast cancer by controlling EMT induction via interactions with protein kinase C-θ (PKC-θ) and BRCA1 [305, 306]. Furthermore, LSD1 can interact with lncRNAs to regulate drug resistance; for instance, it interacts with FTH1P3 or HAS2 antisense RNA 1 (HAS2-AS1) to suppress the TIMP3 and EphB3, contributing to increased gefitinib and chemotherapy resistance in lung cancer cells [307, 308]. LSD1 is also capable of activating the LINC01134/SP1/p62 axis, increasing resistance to oxaliplatin in HCC [309]. PRMTs are also related to therapeutic resistance. Hypoxia can induce the methylation of the PRMT5-mediated autophagic regulator ULK1, promoting tumor cell carboplatin resistance [310]. PRMT5 and MEP50 interaction-mediated hnRNPA1 methylation can stimulate internal ribosome entry site (IRES)-mediated cyclin D1 and c-Myc translation, helping glioblastoma cells better resist mTOR inhibitor treatment [311]. For further details regarding the association between histone methylation and therapeutic resistance, see the article published by Wang et al. [312] (Fig. 5).
Radiotherapy resistance
The formation of double-stranded DNA damage and the consequent inhibition of repair processes are major causes of radioresistance. Many studies of the responses of DNA damage repair (DDR) activity to methylation-related enzymes have thus been conducted. DNMT1 can promote the overexpression of DSTN and activate Wnt/β-catenin signaling to promote radioresistance in CRC cells. It can also promote miR-24 downregulation and alter H2AX expression, impairing the non-homologous end joining (NHEJ) process [313, 314]. DNMT3A silencing in rhabdomyosarcoma can trigger senescence through the upregulation of p16 and p21. Depleting DNMT3B results in severe DNA damage and the impairment of DNA repair mediators, including reductions in ATM, DNA-PKcs, and Rad51 levels [315]. Conversely, DNMT3A and DNMT3B overexpression in nasopharyngeal carcinoma cells may render them resistant to radiotherapy through the promotion of p53 and p21 methylation [316]. Positive correlations between DNMT3B and hTERT promoter methylation levels have been reported, promoting EZH2 expression and gold histone methylation while also affecting small-cell lung cancer radioresistance [317]. BRCA1 and DAPK1 demethylation leads to the increased expression of these genes in cervical cancer, altering DNA damage repair and reducing apoptosis following irradiation, thereby giving rise to radioresistance [318] (Fig. 6).
Fig. 6.
Methylation-induced radiotherapy resistance. Radiotherapy resistance is attributed to the inhibition of DNA damage repair and the promotion of important signal transduction pathways by methylation-related enzymes. Regulate the DNA damage repair system (inhibit its repair or promote cell cycle dormancy, and the change of cell functional status affects its repair): DNMT3A, METTL3, ALKBH5, and EZH2; The activation of the relevant signal pathways: Wnt/β-catenin (DNMT1, DNMT3B, METTL3, and FTO), JAK/STAT3 (KDM4B), mitogen-activated protein kinase (MAPK) (METTL3), and PI3K-AKT/S6 (METTL3 and YTHDC1)
m6A modification is the most prominent type of RNA methylation in the context of radioresistance. METTL3-mediated m6A methylation can activate SOX2 and Wnt/β-catenin signaling to regulate glioma and GSC cell radiosensitivity by maintaining a dedifferentiated state [319]. It can also suppress MAPK signaling to render pancreatic cancer cells more radioresistant [320] and reduce radiosensitivity by promoting circCUX1/Caspase-1 pathway-mediated IL-1β and IL-18 production, thus reducing the programmed death of tumor cells [246]. METTL3 can increase osteosarcoma resistance to ionizing radiation through the recruitment of YTHDC1 and the homologous recombination factors BRCA1 and RAD51 [321]. TRDMT1 (DNMT2) is capable of inducing m5C modifications, increasing the recruitment of the RAD52/RAD51 or the expression of TET1/FMRP, altering homologous recombination repair activity and timing, and thereby improving the resistance of osteosarcoma cells to ionizing radiation [322, 323]. Inhibition of ALKBH5 expression in GSC results in the downregulation of DNA damage repair-related factors (Rad51, XRCC2, BRCA2, EXO1, CH1, and γ-H2AX), leading to inhibition of homologous recombination, rapid repair, and enhanced radioresistance. FOXM1 expression can also be modulated to regulate cell cycle activity and radioresistance [324]. FTO can influence Wnt/β-catenin signaling to promote ERCC1, rendering cervical squamous cell carcinoma cells more resistant to chemoradiotherapy [325]. YTHDC2 can enhance IGF1R expression in nasopharyngeal carcinoma, thereby triggering PI3K-AKT/S6 pathway activation and inhibiting the apoptotic death of tumor cells in a manner that can promote resistance to radiotherapy [326] (Fig. 6).
Histone methylation is closely related to radiation-induced autophagic, which protects NSCLC cells. H4K20me3 modifications can facilitate GABARAPL1 upregulation and enhanced autophagy, promoting radioresistance that can be overcome by EZH2 inhibition [327]. In addition, radiation may trigger the methylation of histone H3 at the promoter of the CSC marker aldehyde dehydrogenase 1A1 (ALDH1A1), stimulating its transcription and rendering cancer cells resistant to radiotherapy [328]. Knocking down JMJD1A can affect RNF8 levels and the expression of various DNA damage repair factors, impairing double-stranded DNA break repair in prostate cancer cells [329]. KDM4B can activate JAK/STAT3 signaling through its ability to bind to cAMP-response element binding protein (CREB), reducing DNA damage repair and the ionizing radiation sensitivity of CRC [330] (Fig. 6).
Immunotherapy resistance
DNMT activity may be a potential mechanism underpinning immunotherapeutic resistance in melanoma. Hypermethylation can limit the efficacy of immune checkpoint blockade through the inhibition of endogenous IFN responses necessary for tumor cell recognition. Conversely, global hypomethylation can lead to immunosuppressive changes in EMT activity, inflammatory gene expression, and PD-L1 expression [331] (Fig. 7). However, the drivers of these contrasting methylation states remain unclear.
Fig. 7.
Methylation-induced immunotherapy resistance. Immunotherapy resistance is attributed to changes in signaling pathway transduction and immune microenvironment by methylation-related enzymes. Inhibit the adaptive immunity of cells against tumor cells: Toll-like receptor (TLR) signaling pathway (DNMT), mitogen-activated protein kinase (MAPK) signaling pathway (DNMT), cGAS/STING axis (DNMT and PRMT), IFN-γ/STAT1/IRF1 axis (YTHDF2), CD163/Nrf2 axis (PRMT), hypoxia, and Wnt/β-catenin pathway (FTO); Changes in the immune microenvironment: ALKBH5 can promote the secretion of lactic acid and increase the immunosuppressive effect of MDSC and Treg cells; Inhibit the expression of PD-L1 on the cell surface: DNMT, METTL, and YTHDF2
Most research on m6A modifications in this context has been focused on resistance to PD-1/PD-L1 checkpoint inhibitors [332]. METTL3 can support NSCLC cell evasion of CD8+T cell cytotoxicity through YTHDC1-mediated activation of the circIGF2BP3/PKP3/PD-L1 pathway [333]. It can also be regulated by JNK to increase the resistance of bladder cancer cells to CD8+T cell cytotoxicity [334]. METTL3/METTL14 can also suppress IFN-γ/STAT1/IRF1 signaling in a YTHDF2-dependent manner, suppressing chemokine and cytokine expression to enhance CRC and melanoma resistance to anti-PD-1 treatment [335]. ALKBH5 deletion can reduce MCT4/SLC16A3 expression to alter lactic acid levels within the TME, thus modifying Treg and MDSC recruitment and enhancing the efficacy of anti-PD-1 treatment in malignant melanoma [336]. FTO overexpression leads to the Wnt/β-catenin pathway-mediated upregulation of SOX10 and the inhibition of the IFN-γ-induced melanoma cells' death, contributing to immunotherapy resistance [337] (Fig. 7).
Relatively little research focused on histone methylation and immunoresistance. PRMT1 can methylate cGAS to inhibit cGAS/STING pathway activity, supporting tumor cell immune escape [338]. Methylation-mediated stabilization of KEAP1 by PRMT5 can lead to NRF2/HMOX1 pathway suppression and reduced ferroptosis, rendering triple-negative breast cancer cells more resistant to immune-mediated clearance [339] (Fig. 7).
Predictive and prognostic evaluation
The primary objective of clinical oncology is the improvement of patient survival. Maximizing the efficacy of novel therapeutic strategies necessitates the identification of biomarkers capable of predicting clinical outcomes, enabling the selection of patients most likely to benefit from a given treatment. Methylation analyses in patients undergoing epigenetic therapy provide an opportunity to determine the clinical responses of these drugs. Genome-wide methylation analyses from various tissues may represent another important area for future study, potentially opening new avenues for treatment. The ability of certain methylation patterns to predict cancer patient treatment outcomes and overall prognosis is summarized in Table 4 [340–421].
Table 4.
Methylation biomarkers used for prediction and prognostic evaluation
| Cancer | Methylation biomarkers | Country | Therapy | Simple No | Methylation situation | Prognosis | Survival outcome | References |
|---|---|---|---|---|---|---|---|---|
| Bladder cancer | CDH11 | China | Surgery | 146 | Methylation vs. Unmethylation | Poor prognosis | HR (95% CI) of OS: 6.852 (3.461–16.177), p = 0.008 | [340] |
| Bladder cancer | MSH6/THBS1 | Spain | Surgery/Bacillus Calmette-Guerin (BCG) | 101 | Methylation vs. Unmethylation | Favorable prognosis | HR (95% CI) of PFS: 0.226 (0.074–0.692), p = 0.009 | [341] |
| Bladder cancer | RASSF1A | Korea | Surgery/Adjuvant radiotherapy | 301 | Methylation vs. Unmethylation | Poor prognosis | HR (95% CI) of PFS: 8.559 (1.547–47.364), p = 0.014 | [342] |
| Bladder cancer | PCDH8 | China | Surgery | 233 | Methylation vs. Unmethylation | Poor prognosis | HR (95% CI) of PFS: 2.523 (1.654–7.431), p = 0.0036; HR (95% CI) of OS: 3.017 (1.542–8.251), p = 0.002 | [343] |
| Bladder cancer | PMF-1 | Spain | Surgery/BCG | 77 | Unmethylation vs. Methylation | Favorable prognosis | HR (95% CI) of PFS: 2.91 (1.18–7.19), p = 0.020; HR (95% CI) of RFS: 2.03 (1.03–4.02), p = 0.020 | [344] |
| Bladder cancer | PRAC | Korea | Surgery | 136 | Hypermethylation vs. Hypomethylation | Poor prognosis | HR (95% CI) of PFS: 9.531 (1.172–77.497), p = 0.035; HR (95% CI) of DFS: 2.652 (1.241–5.667), p = 0.012 | [345] |
| Bladder cancer | RB1/PYCARD | Spain | Surgery/BCG | 101 | Methylation vs. Unmethylation | Favorable prognosis | HR (95% CI) of PFS: 0.149 (0.032–0.687), p = 0.015 | [341] |
| Bladder cancer | RUNX3 | Korea | Surgery/BCG or Chemotherapy | 186 | Methylation vs. Unmethylation | Poor prognosis | HR (95% CI) of DFS: 1.709 (1.010–2.890), p = 0.046; HR (95% CI) of PFS: 5.126 (1.049–25.050), p = 0.043; HR (95% CI) of OS: 1.959 (1.129–0.398), p = 0.017 | [346] |
| Bladder cancer | SFRP | America | Surgery | 355 | Methylation vs. Unmethylation | Poor prognosis | HR (95% CI) of OS: 1.78 (1.08–2.92), p = 0.042 | [347] |
| Bladder cancer | SYNPO2 | Spain | Surgery/BCG | 170 | Methylation vs. Unmethylation | Poor prognosis | HR (95% CI) of DFS: 2.541 (1.207–5.350), p = 0.014; HR (95% CI) of PFS: 11.227 (1.530–82.361), p = 0.017 | [348] |
| Breast cancer | CDH13 | China | Chemotherapy/Endocrine therapy or radiotherapy | 238 | Methylation vs. Unmethylation | Favorable prognosis | HR (95% CI) of DFS: 0.163 (0.051–0.520), p = 0.002; HR (95% CI) of OS: 0.374 (0.110–1.276), p = 0.067 | [349] |
| Breast cancer | BRCA1 | Multinational | Surgery | 3,205 | Methylation vs. Unmethylation | Poor prognosis | HR (95% CI) of DFS: 1.38 (1.04–1.84), p = 0.005; HR (95% CI) of OS: 3.92 (1.49–10.32), p = 0.000 | [350] |
| Breast cancer | HOXD13 | China | Surgery | 196 | Methylation vs. Unmethylation | Poor prognosis | HR (95% CI) of OS: 1.889 (1.151–3.099), p = 0.012 | [351] |
| Breast cancer | SFRP1 | Germany | Surgery | 130 | Methylation vs. Week/no methylation | Poor prognosis | HR (95% CI) of OS: 1.565 (1.001–3.657), p = 0.047 | [352] |
| Breast cancer | SALL2 | China | Endocrine therapy | 238 | Hypermethylation vs. Hypomethylation | Poor prognosis | HR (95% CI) of DFS: 1.939 (1.167–3.222), p = 0.011; HR (95% CI) of OS: 2.220 (1.251–3.940), p = 0.006 | [353] |
| Breast cancer | PAX2 | Iran | Surgery/Adjuvant anti-hormone therapy | 72 | Hypermethylation vs. Hypomethylation | Poor prognosis | HR (95% CI) of DFS: 2.161 (1.041–4.486), p = 0.039; HR (95% CI) of OS: 0.887 (0.357–2.207), p = 0.797 | [354] |
| Breast cancer | ESR1 | China | Surgery/Adjuvant chemotherapy | 43 | Methylation vs. Unmethylation | Poor prognosis |
Median PFS: 3.0 months and 6.0 months; Median OS: 14.0 months and 20.5 months |
[355] |
| Breast cancer | MDR1 | India | Surgery | 100 | Methylation vs. Unmethylation | Poor prognosis | HR (95% CI) of OS: 8.40 (1.00–65.50), p = 0.040 | [356] |
| Breast cancer | LDH-C4 | China | Surgery | 136 | Methylation vs. Unmethylation | Poor prognosis | HR (95% CI) of OS: 2.304 (1.115–3.457), p = 0.015 | [357] |
| Breast cancer | CST6 | Austria | Chemotherapym Targeted therapy, or Hormone therapy | 62 | Methylation vs. Unmethylation | Poor prognosis | HR (95% CI) of OS: 7.99 (2.33–26.84), p < 0.000 | [358] |
| Breast cancer | RASSF1 | Austria | Chemotherapym Targeted therapy, or Hormone therapy | 62 | Methylation vs. Unmethylation | Poor prognosis | HR (95% CI) of OS: 3.17 (1.10–9.17), p = 0.033 | [358] |
| Breast cancer | H3K9me3 | China | Untreated | 917 | Methylation vs. Unmethylation | Favorable prognosis | HR (95% CI) of PFS: 0.70 (0.49,-0.99), p = 0.046; HR (95% CI) of OS: 0.77 (0.49–1.20), p = 0.244 | [359] |
| Cervical cancer | CDH1/CDH13 | Austria | Surgery | 93 | Methylation vs. Unmethylation | Poor prognosis | HR (95% CI) of DFS: 2.50 (1.30–4.60), p = 0.005; HR (95% CI) of OS: 2.50 (1.30–4.80), p = 0.005 | [360] |
| Cervical cancer | HHEX | China | Untreated | 271 | Hypermethylation vs. Hypomethylation | Favorable prognosis | HR (95% CI) of OS: 0.778 (0.635–0.953), p = 0.015 | [361] |
| Cervical cancer | ITGA5 | China | Untreated | 271 | Hypermethylation vs. Hypomethylation | Poor prognosis | HR (95% CI) of OS: 1.012 (1.002–1.022), p = 0.019 | [361] |
| Cervical cancer | PAX1 | China | Raidotherapy/Chemotherapy | 125 | Hypomethylation vs. Hypermethylation | Favorable prognosis | HR (95% CI) of OS: 4.433 (1.131–17.380), p = 0.033 | [362] |
| Cervical cancer | RASSF1A | India | Surgery | 110 | Methylation vs. Unmethylation | Poor prognosis | HR (95% CI) of OS: 2.83 (1.11–7.22), p = 0.029 | [363] |
| Cervical cancer | RASSF2A | Spain | Surgery | 152 | Methylation vs. Unmethylation | Poor prognosis | HR (95% CI) of DFS: 6.20 (1.20–32.40), p = 0.03; HR (95% CI) of OS: 6.00 (1.50–24.50), p = 0.012 | [364] |
| Cervical cancer | S1PR4 | China | Untreated | 271 | Hypermethylation vs. Hypomethylation | Favorable prognosis | HR (95% CI) of OS: 0.787 (0.657–0.944), p = 0.010 | [361] |
| Cervical cancer | SOX1 | China | Surgery | 205 | Methylation vs. Unmethylation | Favorable prognosis | 5-year OS rate: 93.35% vs. 68.29% (p = 0.048) | [365] |
| Cervical cancer | VIM | Korea | Surgery | 54 | Methylation vs. Unmethylation | Favorable prognosis | 5-year DFS rate: 91.70% vs. 68.90% (p = 0.036) | [366] |
| Cervical cancer | ZNF582 | China | Raidotherapy/Chemotherapy | 219 | Methylation vs. Unmethylation | Poor prognosis | HR (95% CI) of DFS: 2.826 (1.125–7.099), p = 0.030; HR (95% CI) of OS: 2.039 (0.700–5.938), p = 0.191 | [367] |
| Cervical cancer | CENPK | China | Surgery | 154 | Methylation vs. Unmethylation | Poor prognosis | HR (95% CI) of OS: 1.413 (1.078–1.853), p = 0.012 | [368] |
| Cervical cancer | H3K4me3 | Germany | Surgery | 250 | Methylation vs. Unmethylation | Poor prognosis | HR (95% CI) of PFS: 2.278 (1.084–4.790), p = 0.030; HR (95% CI) of OS: 1.503 (0.853–2.651), p = 0.159 | [369] |
| Colorectal cancer | BCAT1/IKZF1 | Australia | Surgery | 175 | Methylation vs. Unmethylation | Poor prognosis | HR (95% CI) of PFS: 9.69 (2.50–37.59) | [370] |
| Colorectal cancer | HLTF | Germany | Surgery | 311 | Methylation vs. Unmethylation | Poor prognosis | HR (95% CI) of OS: 2.4 (1.4–4.0), p = 0.001 | [371] |
| Colorectal cancer | HPP1 | Germany | Surgery | 311 | Methylation vs. Unmethylation | Poor prognosis | HR (95% CI) of OS: 2.2 (1.4–3.4), p = 0.000 | [371] |
| Colorectal cancer | LINE-1 | America | Surgery/Adjuvant chemotherapy | 643 | Hypermethylation vs. Hypomethylation | Favorable prognosis | HR (95% CI) of OS: 0.80 (0.64–0.89), p = 0.020 | [372] |
| Colorectal cancer | p16 | Multinational |
Radiotherapy or Chemotherapy |
3,968 | Hypermethylation vs. Hypomethylation | Poor prognosis | HR (95% CI) of OS: 1.64 (1.33–2.02); HR (95% CI) of DFS: 1.91 (1.16–3.15) | [373] |
| Colorectal cancer | RASSF1A | China, Greece, and Denmark | Multiple | 448 | Methylation vs. Unmethylation | Poor prognosis | HR (95% CI) of OS: 1.91 (1.16–3.15) | [374] |
| Colorectal cancer | SFRP | China | Surgery | 307 | Hypermethylation vs. Hypomethylation | Favorable prognosis | HR (95% CI) of OS: 0.333 (0.159–0.694), p = 0.032 | [375] |
| Colorectal cancer | SHISA3 | China | Surgery/Adjuvant chemotherapy | 127 | Hypermethylation vs. Hypomethylation | Poor prognosis | HR (95% CI) of OS: 2.90 (1.50–5.80), p = 0.002; HR (95% CI) of DFS: 4.00 (1.60–10.20), p = 0.003 | [376] |
| Colorectal cancer | TFAP2E | Korea | Surgery | 193 | Hypermethylation vs. Hypomethylation | Favorable prognosis | HR (95% CI) of OS: 2.24 (1.10–4.56), p < 0.025; HR (95% CI) of DFS: 2.44 (1.12–5.32), p = 0.025 | [377] |
| Colorectal cancer | MGMT | Spain | Surgery/Adjuvant chemotherapy or radiotherapy | 123 | Hypomethylation vs. Hypermethylation | Favorable prognosis | HR (95% CI) of OS: 2.809 (1.269–6.214), p = 0.011; HR (95% CI) of DFS: 1.55 (0.81–2.99), p = 0.182 | [378] |
| Colorectal cancer | ERCC1 | Egypt | Chemotherapy | 80 | Hypomethylation vs. Hypermethylation | Favorable prognosis | HR (95% CI) of OS: 2.809 (1.269–6.214), p = 0.011; HR (95% CI) of EFS: 3.480 (1.703–7.115), p = 0.001 | [379] |
| Colorectal Cancer | PTEN | America | Targeted therapy with or without chemotherapy | 76 | Methylation vs. Unmethylation | Favorable prognosis | HR of OS: 0.496, p = 0.012; HR of PFS: 0.900, p = 0.734 | [380] |
| Colorectal cancer | H3K4me2 | Japan | Surgery | 54 | Methylation vs. Unmethylation | Favorable prognosis | HR of OS: 0.338 (0.146–0.783), p = 0.001 | [381] |
| Lung cancer | CDH1 | America | Surgery | 132 | Methylation vs. Unmethylation | Poor prognosis | HR (95% CI) of OS: 5.07 (1.32–19.40), p = 0.018 | [382] |
| Lung cancer | CXCL12 | Japan | Surgery | 236 | Methylation vs. Unmethylation | Poor prognosis | HR (95% CI) of OS: 1.681 (1.107–2.551), p = 0.015 | [383] |
| Lung cancer | DAPK | America | Surgery | 132 | Methylation vs. Unmethylation | Poor prognosis | HR (95% CI) of OS: 1.93 (1.06–3.53), p < 0.001 | [382] |
| Lung cancer | KMT2C | Greece | Surgery or Chemotherapy | 139 | Unmethylation vs. Methylation | Favorable prognosis |
Surgery: HR (95% CI) of PFS: 0.239 (0.099–0.575), p = 0.001; HR (95% CI) of OS: 0.324 (0.135–0.865), p = 0.023 Chemotherapy: HR (95% CI) of PFS: 0.431 (0.239–0.779), p = 0.005; HR (95% CI) of OS: 0.306 (0.173–0.541), p < 0.001 |
[384] |
| Lung cancer | MGMT | Germany | Multiple | 859 | Methylation vs. Unmethylation | Poor prognosis | HR (95% CI) of OS: 2.05 (1.41–2.98), p = 0.000 | [385] |
| Lung cancer | NPTX1 | China | Surgery | 188 | Methylation vs. Unmethylation | Poor prognosis | HR (95% CI) of OS: 1.974 (1.107–3.517), p = 0.021 | [386] |
| Lung cancer | PTGER2 | Japan | Surgery | 133 | Methylation vs. Unmethylation | Favorable prognosis | HR (95% CI) of OS: 0.481 (0.263–0.878), p = 0.017 | [387] |
| Lung cancer | RASSF1 | America | Surgery | 132 | Methylation vs. Unmethylation | Poor prognosis | HR (95% CI) of OS: 2.71 (1.05–7.00), p = 0.040 | [382] |
| Lung cancer | TFPI-2 | China | Surgery | 133 | Methylation vs. Unmethylation | Poor prognosis | HR (95% CI) of OS: 1.922 (1.149–3.214), p = 0.013 | [388] |
| Lung cancer | TGFBI | Korea | Chemotherapy | 138 | Methylation vs. Unmethylation | Poor prognosis | HR (95% CI) of DFS: 2.88 (1.19–6.99), p = 0.019 | [389] |
| Lung cancer | miR-34b/miR-34c/miR-124–3 | Korea | Surgery/Adjuvant therapy | 157 | Methylation vs. Unmethylation | Poor prognosis | HR (95% CI) of OS: 4.44 (2.15–9.18), p < 0.000 | [390] |
| Lung cancer | H3K27me3 | China | Complete ablative surgical | 71 | Hypermethylation vs. Hypomethylation | Poor prognosis | HR (95% CI) of OS: 6.04 (1.42–25.70), p = 0.015 | [391] |
| Esophageal carcinoma | FOXF2 | China | Surgery | 135 | Methylation vs. Unmethylation | Poor prognosis | HR (95% CI) of OS: 2.385 (1.360–4.182), p < 0.002 | [392] |
| Esophageal carcinoma | IGF-1/IGF-1R/IGFBP3 | China | Untreated | 264 | Methylation vs. Unmethylation | Poor prognosis | HR (95% CI) of OS: 5.821 (2.922–11.600), p < 0.001 | [393] |
| Esophageal carcinoma | p16 | Thailand | Untreated | 213 | Methylation vs. Unmethylation | Poor prognosis | HR (95% CI) of OS: 2.82 (1.13–5.06), p = 0.027 | [394] |
| Esophageal carcinoma | TP53 | Thailand | Untreated | 213 | Methylation vs. Unmethylation | Poor prognosis | HR (95% CI) of OS: 2.95 (1.34–5.47), p = 0.007 | [394] |
| Endometrial cancer | CDH1 | China | Surgery | 152 | Methylation vs. Unmethylation | Poor prognosis | 5-year OS rate: 50.00% vs. 78.80% (p = 0.021) | [395] |
| Endometrial cancer | RASSF1A | Korea | Surgery | 70 | Hypermethylation vs. Hypomethylation | Poor prognosis | 5-year DFS rate: 77.80% vs. 97.00% (p = 0.039) | [396] |
| Gastric cancer | MAGI2 | China | Surgery/Adjuvant chemotherapy | 70 | Methylation vs. Unmethylation | Poor prognosis | HR (95% CI) of PFS: 2.245 (1.154–4.369), p = 0.012 | [397] |
| Gastric cancer | TRAF2 | China | Surgery | 129 | Hypomethylation vs. Hypermethylation | Favorable prognosis | HR (95% CI) of OS: 18.827 (3.103–114.222), p = 0.001 | [398] |
| Glioblastoma | HOXA 9/10 | Italy | Surgery/Adjuvant chemoradiotherapy | 63 | Hypermethylation vs. Hypomethylation/Unmethylation | Favorable prognosis |
Median survival 16.8 and 5.9 months, HR of OS: 0.256, P = 0.004 |
[399] |
| Glioblastoma | MGMT | Multinational | Radiotherapy or Chemotherapy | 2,593 | Methylation vs. Unmethylation | Favorable prognosis | HR (95% CI) of OS: 0.48 (0.35–0.65), p < 0.001; HR (95% CI) of PFS: 0.43 (0.32–0.56), p < 0.001 | [400] |
| Head and neck cancer | PDCD1 | Germany | Surgery | 527 | Methylation vs. Unmethylation | Poor prognosis | HR (95% CI) of OS: 2.14 (1.19–3.84), p = 0.011 | [401] |
| Head and neck cancer | SEPT | Germany | Surgery/Adjuvant radioyherapy or chemoradiotherapy | 219 | Methylation vs. Unmethylation | Poor prognosis | HR (95% CI) of DFS: 2.72 (1.30–5.68), p = 0.008 | [402] |
| Liver cancer | SOCS3 | China | Transarterial chemoembolization (TACE) | 246 | Hypermethylation vs. Hypomethylation | Poor prognosis | HR (95% CI) of OS: 3.44 (2.57–6.31), p < 0.001 | [403] |
| Liver cancer | TFPI2 | China | Surgery | 198 | Hypermethylation vs. Hypomethylation | Poor prognosis | HR (95% CI) of OS: 2.718 (1.590–4.647), p = 0.000; HR (95% CI) of DFS: 2.522 (1.726–3.684), p = 0.000 | [404] |
| Lymphoma | MGMT | Japan | Chemotherapy | 116 | Methylation vs. Unmethylation | Favorable prognosis | 5-year OS rate: 65.10% vs. 47.80% (p = 0.036) | [405] |
| Lymphoma | PCDH10 | China | Targeted therapy/Chemotherapy | 107 | Methylation vs. Unmethylation | Poor prognosis | HR (95% CI) of OS: 1.829 (1.059–3.158), p = 0.030; HR (95% CI) of PFS: 2.977 (1.245–7.119), p = 0.014 | [406] |
| Melanoma | LKB1 | China | Untreated | 107 | Methylation vs. Unmethylation | Poor prognosis | HR (95% CI) of OS: 3.688 (1.124–12.100), p = 0.031 | [407] |
| Melanoma | PTEN | Korean | Untreated | 158 | Hypermethylation vs. Hypomethylation | Poor prognosis | HR (95% CI) of OS: 3.76 (1.20 to 11.10), p = 0.017 | [408] |
| Myeloid neoplasms | ANGPT2 | Italy | Untreated | 88 | Hypomethylation vs. Hypermethylation | Favorable prognosis | HR (95% CI) of OS: 2.435 (1.137–5.215), p = 0.022 | [409] |
| Myeloid neoplasms | DLX5 | China | Untreated | 270 | Methylation vs. Unmethylation | Poor prognosis | HR (95% CI) of OS: 2.345 (1.113–4.942), p = 0.025 | [410] |
| Myeloid neoplasms | ITGBL | China | Untreated | 160 | Methylation vs. Unmethylation | Poor prognosis | HR (95% CI) of OS: 4.045 (1.287–12.711), p = 0.017 | [411] |
| Nasopharyngeal carcinoma | RAB37 | China | Radioyherapy/Chemotherapy | 110 | Hypermethylation vs. Hypomethylation | Poor prognosis | HR (95% CI) of OS: 4.265 (1.291–14.10), p = 0.017; HR (95% CI) of DMFS: 8.316 (1.119–61.774), p = 0.038 | [412] |
| Nasopharyngeal carcinoma | TIPE3 | China | Radioyherapy/Chemotherapy | 441 | Hypermethylation vs. Hypomethylation | Poor prognosis | HR (95% CI) of OS: 2.99 (1.59–5.64), p = 0.001; HR (95% CI) of DFS: 2.56 (1.35–4.85), p = 0.004 | [413] |
| Ovarian cancer | BRCA1 | Multinational | Surgery/Adjuvant chemotherapy | 2,636 | Methylation vs. Unmethylation | Poor prognosis | HR (95% CI) of PFS: 1.26 (1.02–1.56), p = 0.030; HR (95% CI) of OS: 1.11 (0.88–1.41), p = 0.035 | [414] |
| Ovarian cancer | OPCML | China | Surgery | 102 | Methylation vs. Unmethylation | Poor prognosis | HR (95% CI) of OS: 13.55 (1.85–98.97), p = 0.010 | [415] |
| Prostate cancer | GSTP1 | Multinational | Surgery | 1,045 | Hypermethylation vs. Hypomethylation/Unmethylation | Poor prognosis | HR (95% CI) of PFS: 2.57 (1.30–5.10), p = 0.007; HR (95% CI) of OS: 2.73 (2.05–3.62), p < 0.000 | [416] |
| Prostate cancer | RASSF1/DAPK1 | Lithuania | Surgery | 149 | Methylation vs. Unmethylation | Poor prognosis | HR (95% CI) of OS: 2.20 (1.06–4.54), p = 0.027 | [417] |
| Renal cancer | CDO1 | Netherlands | Surgery with or without (neo) adjuvant therapy | 365 | Hypermethylation vs. Hypomethylation | Poor prognosis | HR (95% CI) of OS: 1.66 (1.13–2.44), p = 0.006 | [418] |
| Renal cancer | PCDH17 | China | Surgery | 191 | Methylation vs. Unmethylation | Poor prognosis | HR (95% CI) of OS: 2.876 (1.347–8.065), p = 0.010; HR (95% CI) of PFS: 3.014 (1.235–7.463), p = 0.003 | [419] |
| Thyroid carcinoma | HOXD10 | China | Surgery | 152 | Methylation vs. Unmethylation | Poor prognosis | HR (95% CI) of OS: 3.779 (1.283–11.128), p = 0.016 | [420] |
| Thyroid carcinoma | RASSF1A | Multinational | Multiple | 498 | Methylation vs. Unmethylation | Poor prognosis | HR (95% CI) of DFS: 2.63 (1.28–5.38), p < 0.011; HR (95% CI) of OS: 1.26 (0.45–3.62), p < 0.664 | [421] |
HR hazard ratio, CI confidence interval, OS overall survival, PFS progression-free survival, DFS disease-free survival, EFS event-free survival, DMFS distant metastasis-free survival
Currently, liquid biopsy programs focused on ctDNA/cfDNA methylation are a major focus of clinical attention. The short half-life of ctDNA makes it directly related to the tumor cells' status, reflecting the instantaneous changes in treatment response and tumor load, achieving accurate tumor staging, and predicting recurrence. Detection involves single-target and multi-target assays. SEPT9 methylation reflects postoperative recovery in CRC, with reduced levels closely related to disease remission [422]. Preoperative SEPT9 methylation levels correlate linearly with tumor diameter and accurately reflect distant metastasis or recurrence [423]. Methylation levels of EYA4, GRIA4, ITGA4, MAP3K14-AS1, and MSC reflect tumor size dynamics in CRC patients, indicating poor or good treatment response [424]. The combined BCAT/IKZF1 methylation test has higher sensitivity than routine CEA for diagnosing postoperative recurrence of stage II/III CRC [425]. Methylation markers of 10 liver cancer-related genes are correlated highly with tumor load, treatment response, and clinical stage, offering good monitoring and prognostic value [426]. MGMT hypermethylation in cfDNA predicts response and time to progression in glioblastoma multiforme or metastatic CRC patients treated with cytotoxic alkylating agents [427]. Diffuse breast cancer has a poor prognosis and is difficult to identify by imaging techniques. A study identified and optimized a specific and highly expressed methylation tag EFC#93 in diffuse breast cancer, containing 5 associated CpG patterns, whose elevated level is closely related to metastatic breast cancer and can diagnose poor prognosis 12 months before metastasis [428]. In metastatic breast cancer, methylation of WNT5A, KLK10, MSH2, GATA3, and SOX17 is associated with poorer prognosis, disease progression, or death, with higher methylation scores indicating non-response to treatment [429]. Dynamic methylation changes of HOXA9 have been used to predict the therapeutic effect of novel therapies for ovarian cancer, filling the gap in predictive biological indicators of therapeutic effect [430].
Methylated biomarkers and ctDNA methylated liquid biopsy are widely used in clinical diagnosis and prognosis; some have entered clinical trials or been approved for clinical practice, showing good application prospects. However, challenges remain for large-scale clinical implementation. First, when single-test reliability is insufficient, combining screening programs can improve the detection effect. Second, costs must be considered to reduce the financial burden on patients. Clinically, strict inspection procedures and quality management norms are necessary to eliminate batch differences and human errors as much as possible, thereby improving result reproducibility.
Conclusions and perspectives
Recent years have witnessed increasingly comprehensive investigations into the roles that epigenetic modifications-including DNA, RNA, and histone methylation oncogenesis, driven by advances in genomics and molecular biology. The widely accepted results of these studies have major implications for drug development, clinical management, and patient care. Small-molecule drugs modulating these modifications have been developed, providing unprecedented opportunities for diagnosis, treatment, and prognostic evaluation. Specific genes shape cancer-related processes, including growth, development, differentiation, and immunomodulation. Tumor cell-derived DNA enters the blood through secretion, apoptosis, and other mechanisms; the resultant ctDNA may harbor the DNA methylation profiles of the parental malignant cells. The expression and methylation of specific genes in cancers have fueled the development of methylation-focused tests for detecting latent cancer cells. These tests are ideally situated to predict tumor development or monitor patients in a convenient, minimally invasive manner. Future methylation-focused strategies may further improve screening, stratified treatment, and follow-up strategies.
Tumor cell proliferation coincides with genomic abnormalities and chromatin modifications. The mechanisms underlying these dysregulated processes enable tumor cells to evade detection or killing, with epigenetic changes, supporting these evasive behaviors. Formulating personalized treatment options remains challenging, partly due to therapeutic resistance. The processes controlling resistance are dynamic; many epigenetic changes exist pre-treatment or evolve during treatment, yielding various phenotypes. Since epigenetic modifications are reversible, they can readily alter tumor cells and shape prognosis. However, the complex, changeable nature of the epigenetic code represents a persistent barrier to a comprehensive understanding of the molecular mechanisms linking epigenetic changes to cancer treatment outcomes. While epigenetic therapeutics offer unprecedented options, they also increase the complexity of personalized care. There is a pressing need to further study resistance mechanisms, particularly the roles of CSCs, the TME, and specific signaling pathways. As many epigenetic drugs are under development, their continued entry into clinical trials is expected, and combination therapies may provide superior patient outcomes when single-agent benefits are inadequate.
A review of contemporary clinical trials highlights that choosing suitable treatment plans poses significant clinical challenges, which need to be explored in detail and even overcome. Experimental failure is one such challenge. A University of Tokyo study found that adult T-cell leukemia/lymphoma treated with the histone methylation inhibitor valemetostat experienced progression or recurrence after an average of 4 months [431]. Multiple substitutions of EZH2 (Y111) and EZH2 (Y661) were detected in relapsed somatic cells, restoring the pre-treatment H3K27me3 pattern and reversing the effect of valemetostat [432]. In addition, decreased TET2 mutation and high expression of DNMT3A were detected; cells developed resistance even without the EZH2 mutation [432]. Furthermore, quiescent leukemia stem cells cannot be eradicated, making methylation therapy difficult to be effective. In some clinical trials of combination therapy, failures resulted from drug interactions or poor patient tolerance. In the phase Ib clinical trial of azacitidine plus vorinostat for the treatment of relapsed/refractory diffuse large B-cell lymphoma (NCT01120834), 5 cases had poor dose tolerance, 17 cases showed disease progression, and the trial ended prematurely. Uncontrollable toxicity is also a huge challenge, with myelosuppression being the most common adverse event of methylation inhibitors. In the clinical trial of azacitidine plus R-CHOP in the treatment of diffuse large B-cell lymphoma, all patients presented with grade 3 or 4 neutropenia and required increased blood transfusion support. Severe gastrointestinal toxicity also causes treatment interruptions. After the initiation of phase I clinical trial of 4′-thio-2′-deoxycytidine (TdCyd) for the treatment of advanced solid tumors (NCT02423057), more than 50% of the patients experienced pulmonary infection, with one death leading to study suspension. Wang et al. found that DNMT3A inhibitors could increase infarction area and inflammation in stroke patients [433]. To promote innovative treatment plans, broader exploration is needed to overcome these challenges. Future clinical trials require more detailed analysis, strict operational control, and consideration of individual differences. For methylation inhibitors, while ensuring the therapeutic effect, drug dosage and regimen must be adjusted, and synergistic treatment sought to reduce drug toxicity while ensuring efficacy. Foreseeable adverse events should be managed with advanced intervention or supportive care.
In summary, epigenetic inheritance is closely tied to the onset, proliferation, metastatic progression, and therapeutic resistance of tumor cells. Targeting specific epigenetic processes can synergize with conventional radiotherapy, chemotherapy, and immunotherapy strategies to help kill target tumor cells. Given the poor prognostic implications of therapeutic resistance, it is vital that a more detailed understanding of the mechanisms that govern drug resistance be established and that approaches to targeting epigenetic regulatory mechanisms be devised more reliably to achieve better patient outcomes. Large-scale clinical trials in the future will inevitably help advance this field and improve patient outcomes. The integration of methylation therapy into precision oncology remains a great challenge, and the application of multi-omics fusion in the methylation model of personalized medicine is needed in the future. With the development of science and technology, the reuse of simple and effective programmable gene editing tools by the CRISPR-Cas system has greatly promoted basic and applied research in many fields, laying the foundation for the development of targeted gene therapy and various biotechnology applications, and may promote joint strategies with methylation therapy. With the attention of the computer field, the combination of medicine and industry has become a prominent trend, which has significantly promoted the diagnosis and treatment of cancer. AI is increasingly integrated with multimodal data, including imaging omics and pathomics, to identify primary tumors and methylation levels, more favorably guiding personalized cancer treatment and prognostic prediction.
Acknowledgements
All figures are drawn by the BioRender (https://BioRender.com) platform and have been published with permission.
Abbreviations
- 5mC
5-Methylcytosine
- CpG
Cytosine-phosphate-guanine
- m6A
N6-methyladenosine
- METTL3
Methyltransferase like 3
- SNPs
Single-nucleotide polymorphisms
- LSD1
Lysine-specific histone demethylase 1
- HDM
Histone demethylase
- DNMT
DNA methyltransferase
- MBD
Methyl-CpG-binding domain
- SRA
Kaiso, and set the ring finger-associated
- CRC
Colorectal cancer
- HPV
Human papillomavirus
- EBV
Epstein-Barr virus
- m1A
N1-methyladenosine
- m5C
5-Methylcytidine
- m3C
N3-methylcytosine
- m7G
N7-methylguanosine
- 2′-O-Me
2′-O-methylation
- Ψ
Pseudouridine
- A-to-I
Adenosine-to-inosine
- HCC
Hepatocellular carcinoma
- HMTs
Histone methyltransferases
- PRMTs
Protein/histone arginine methyltransferases
- SAM
S-adenosine methionine
- SAH
S-adenosine homocysteine
- H3/H4
Histone 3 and histone 4
- PHD
Plant homeodomain
- cfDNA
Cell-free DNA
- ctDNA
Circulating tumor-derived DNA
- FDA
The Food and Drug Administration
- CE
The communate Europpene
- NMPA
The National Medical Products Administration
- TME
Tumor microenvironment
- ZCyt
5-Azacytidine
- ZdCyd
5-Aza-2′-deoxycytidines
- FdC
5-Fluoro-2′-deoxycytidine
- MDS
Myelodysplastic syndrome
- AML
Acute myeloid leukemia
- CML
Chronic myeloid leukemia
- SG-110
Guadecitabine
- PRC2
Polycomb repressor complex 2
- PcG
Polycomb group
- DZNep
3-Deazaneplanocin A
- EPZ-6438
Tazemetostat
- ADMA
Asymmetric dimethylarginine
- SDMA
Symmetric dimethylarginine
- MTAP
Methyl-thioadenosine phosphorylase
- MLL-r
MLL-rearrangement
- FAD
Flavin adenine dinucleotide
- 2-OG
2-Oxoglutarate
- α-KG
α-Ketoglutaric acid
- MTC
Methyltransferase complex
- NSCLC
Non-small cell lung cancer
- ORR
Overall response rate
- ISG
Interferon-stimulated gene
- CTA
Cancer-testis antigen
- Treg
Regulatory T
- NK
Natural killer
- MDSCs
Myeloid-derived suppressor cells
- TAMs
Tumor-associated macrophages
- ABC
ATP-binding cassette
- CSC
Cancer stem cell
- EMT
Epithelial-mesenchymal transition
- IRES
Internal ribosome entry site
- NHEJ
Non-homologous end joining
- ALDH1A1
Aldehyde dehydrogenase 1A1
- CREB
CAMP-response element binding protein
- BALF
Bronchoalveolar lavage fluid
- BAP1
BRCA1-associated protein-1
- DLI
Donor lymphocyte infusion
- FTO
Fat mass and obesity
- ATRT
All-trans retinoic acid
- PRMT5
Protein arginine methyltransferase 5
- MTA
Methylthioadenosine
- MTAP
Methylthioadenosine phosphorylase
- EZH1/2
Enhancer of zeste homolog 1/2
- EED
Embryonic ectoderm development
- DOT1L
Histone 3 lysine 79 methyltransferase
- NSD 2
Nuclear receptor-binding SET domain-containing 2
- SETD 2
SET domain containing 2
- PD-1
Programmed death-
- PD-L1
Programmed death-ligand 1
- CTLA-4
Cytotoxic T lymphocyte-associated protein-4
- TGFβ
Transforming growth factor-β
- PARP
Poly adenosine diphosphate ribose polymerase
- ADC
Antibody–drug conjugate
- NEDD8
Neural precursor cell expressed developmentally downregulated 8
- CELMoD
Cereblon E3 ligase modulator
- ICIs
Immune checkpoint inhibitors
- HDAC
Histone deacetylase
- IDO1
Indoleamine 2,3-dioxygenase 1
- VEGF
Vascular endothelial growth factor
- Tim-3
T cell immunoglobulin domain and mucin domain-3
- CART
Chimeric antigen receptor T-cell immunotherapy
- MEK
Mitogen-activated protein kinase
- HR
Hazard ratio
- CI
Confidence interval
- OS
Overall survival
- PFS
Progression-free survival
- DFS
Disease-free survival
- EFS
Event-free survival
- DMFS
Distant metastasis-free survival
- TET
Ten-eleven translocation
- TDG
Thymine DNA glycosylase
- DNMTi
DNA methyltransferase inhibitors
- EZH2i
Zeste homolog 2 enhancer inhibitors
- LSD1i
Lysine-specific demethylase 1 inhibitors
- FTOi
Fat mass and obesity-associated protein inhibitors
- METTL3i
Methytransferase-like inhibitors
- LAG-3
Lymphocyte activation gene 3
- BCL-2i
B-cell lymphoma-2 inhibitors
- HDACi
Histone deacetylase inhibitors
- IFN
Interferon
- HR
Homologous recombination
- ROS
Reactive oxygen species
- EGFR-TKI
Epithelial growth factor receptor tyrosine kinase inhibitors
- MAPK
Mitogen-activated protein kinase
- TLR
Toll-like receptor
Author contributions
Hong Zhu: Conceptualization, methodology, software, resources, data curation, writing—original draft, and writing—review and editing, supervision, project administration, and funding acquisition. Youwen Zhu: Conceptualization, methodology, validation, formal analysis, investigation, writing-original draft, and writing-review and editing, and visualization. Kun Liu: Conceptualization, methodology, validation, formal analysis, investigation, writing—original draft, and writing—review and editing. All authors have read and approved the manuscript.
Funding
This work was partly supported by the Changsha Natural Science Foundation of Hunan Province of China (Grant/Award Number: kq2208376 to H. Zhu).
Data availability
All authors had full access to all of the data in this study and took complete responsibility for the integrity of the data and accuracy of the data analysis. The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.
Declarations
Ethics approval and consent to participate
This article is based on previously conducted studies and does not contain any new studies with human participants or animals performed by any of the authors; it does not require the approval of the independent ethics committee.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
All authors had full access to all of the data in this study and took complete responsibility for the integrity of the data and accuracy of the data analysis. The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.