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. 2026 Jan 22;65(4):397–406. doi: 10.1002/mc.70080

The 5‐Methylcytosine RNA Modification in Hepatitis B Virus‐Negative Hepatocellular Carcinoma: Insights From Long‐Read Nanopore Sequencing

Tianhan Sun 1,2,3, Liying Zhou 4, XiaoQing Li 5, Ryan Xiao 6, Gaoyuan Sun 2, Lili Zhang 2, Yifei Li 2, Wei Huang 3, Yayu Li 2, Lu Kuai 2, Xuanmei Luo 2, Hongyuan Cui 1,✉, Meng Chen 7,✉
PMCID: PMC12973161  PMID: 41570162

ABSTRACT

5‐Methylcytosine (m5C) RNA modification contributes to tumor initiation and progression. However, its transcriptome‐wide distribution patterns and biological implications in hepatitis B virus (HBV)‐negative hepatocellular carcinoma (HCC) remain poorly understood. Therefore, this study employs long‐read Nanopore direct RNA sequencing to systematically elucidate the mechanisms of m5C‐mediated epigenetic reprogramming in HBV‐negative HCC. Paired tumor and adjacent normal tissues from three HBV‐negative HCC patients were collected for Nanopore sequencing. Transcriptome‐wide m5C sites were profiled using the CHEUI tool, followed by a comprehensive comparison between tumor and adjacent normal tissue groups regarding the number of m5C sites, their genomic distribution characteristics, and the expression levels of m5C regulators. Finally, an integrated analysis of transcriptomic and methylation data was conducted to identify m5C‐related prognostic indicators in HCC. Tumor tissues exhibited a global increase in m5C sites abundance, with differential modifications enriched on chromosomes 1–3. Genes harboring m5C modifications were significantly enriched in immune and inflammatory pathways, suggesting a potential role for this epitranscriptomic mark in remodeling the tumor immune microenvironment. Consistent upregulation of m5C regulators, including NSUN family members and ALYREF, at both gene and isoform levels, correlated with increased methylation activity. Elevated m5C coupled with decreased CES3 expression were associated with poorer overall survival. Additionally, TMEM234 showed prognostic significance despite unchanged bulk expression in public datasets. m5C modifications are globally altered in HBV‐negative HCC and may contribute to post‐transcriptional regulation and aberrant expression. These findings highlight the potential of m5C as both a prognostic biomarker and a therapeutic target in HBV‐negative HCC.

Keywords: hepatocellular carcinoma, nanopore sequencing, RNA 5‐methylcytidine methylation

Abbreviations

COAD

Immunotherapy in colonic adenocarcinoma

DEGs

Differentially expressed genes

DETs

Differentially expressed transcripts

DRS

Direct RNA sequencing

GO

Gene Ontology

GTEx

Genotype‐Tissue Expression

HBV

Hepatitis B virus

HCC

Hepatocellular carcinoma

lncRNAs

Non‐coding RNAs

m5C

5‐methylcytosine

TCGA

The Cancer Genome Atlas

TIICs

Tumor‐infiltrating immune cells

TME

Tumor microenvironment

TTS

Termination transcript site

UTR

Untranslated region

1. Introduction

Hepatocellular carcinoma (HCC) is the most common primary liver cancer, accounting for approximately 75%–80% of cases worldwide. It ranks as the sixth most common malignancy and the third leading cause of cancer‐related death globally [1, 2]. The high mortality rate of HCC is primarily attributed to the challenges of early detection, frequent late‐stage diagnosis, and limited treatment options for advanced disease, underscoring the need for novel biomarkers and therapeutic targets [3].

5‐methylcytosine (m5C) has emerged as a pivotal regulator of mRNA stability, nuclear export, and translation, thereby influencing protein synthesis and cellular metabolism [4, 5, 6, 7, 8, 9]. m5C modification is catalyzed by specific methyltransferases, notably NSUN2, and is recognized by the export adaptor ALYREF, which facilitates the nucleus‐cytoplasm transport of mRNA. Dysregulation of m5C or its regulator proteins has been implicated in a variety of human diseases, including cancers and neurological disorders [5, 10]. The involvement of m5C in disease processes highlights its potential as both a diagnostic and prognostic biomarker as well as a therapeutic target [7, 11].

Although m5C participates in numerous cellular pathways, its precise role remains incompletely defined, particularly in disease‐specific contexts. The dynamic interactions among its “writers,” “readers,” and “erasers” indicate a highly coordinated epitranscriptomic network that fine‐tunes gene expression. Systematic characterization of m5C in defined pathological settings, such as HCC, could yield mechanistic insights into tumorigenesis and inform therapeutic development.

The relationship between m5C and HCC is complex, encompassing tumor progression, immune modulation, and prognosis. Previous studies have demonstrated distinct m5C patterns between tumor and adjacent non‐tumor tissues, suggesting their contribution to HCC pathogenesis. m5C modifications are linked to metabolic pathways essential for tumor survival and proliferation [12, 13], and overexpression of m5C regulatory genes such as NSUN4 and ALYREF correlated with poor prognosis [14]. Furthermore, m5C modification of long non‐coding RNAs (lncRNAs) and mRNAs differs significantly between tumor and normal tissues, emphasizing its role in transcriptional and translational control [12, 13, 15].

In addition, m5C regulators have been associated with immune infiltration within the tumor microenvironment (TME). For example, DNMT1 expression correlates with poor prognosis and increased immune cell infiltration, suggesting a role in immune evasion [16]. High m5C‐related lncRNAs expression has been linked to elevated levels of regulatory T cells, neutrophils, and M2 macrophages, contributing to immune suppressive phenotypes [17]. Prognostic models incorporating m5C regulators demonstrate strong predictive performance for patient outcomes [18]. Furthermore, m5C‐based scoring system have emerged as an independent prognostic factor that stratify patient by immune landscape and therapeutic responsiveness [19]. High m5C scores are generally associated with immune evasion, reduced immunotherapy sensitivity, and increased chemotherapy responsiveness, suggesting m5C as a promising clinical biomarker for therapeutic guidance [20].

Nanopore sequencing provides distinct advantages in transcriptome profiling due to its ability to generate ultra‐long reads and directly detects base modifications. Nanopore direct RNA sequencing (DRS) analyzes native RNA molecules, bypassing reverse transcription or PCR amplification. This approach preserves base modifications and enables single‐molecule measurement of ionic current signals. Deviations in this signal reflect chemical alterations to the RNA, forming the basis for direct epitranscriptome detection [21]. This real‑time, single‐molecule sequencing platform enhances assembly accuracy, isoform resolution, and structural‑variant identification. The utility of Nanopore sequencing in cancer epigenomics has been validated in multiple studies, underscoring its suitability for mapping RNA modifications such as m5C [22, 23].

HBV‐negative HCC represents a clinically important yet understudied subgroup characterized by unique etiological features and molecular signatures. In this study, we systematically profiled the transcriptome‐wide m5C landscape in HBV‐negative HCC using Nanopore long‐read RNA sequencing and performed integrative analyses to uncover the functional and clinical significance of m5C modifications.

2. Methods

2.1. Patients

Tumor tissues and adjacent normal tissues were obtained from three patients with hepatitis B virus (HBV)‐negative HCC who underwent liver lobectomy at the Cancer Hospital, Chinese Academy of Medical Sciences. None of the patient received chemotherapy or neoadjuvant therapy before surgery. Pathological diagnosis was confirmed independently by two pathologists. The study protocol was approved by the Institutional Review Board of the Cancer Hospital, Chinese Academy of Medical Sciences. Written informed consent was obtained from all participants or their legal representatives.

2.2. Nanopore Direct RNA Sequencing

Total RNA was extracted from tumor and matched adjacent normal tissues using the TRIzol Reagent (Thermo Fisher Scientific). mRNA was purified with Dynabeads mRNA Purification Kit (Invitrogen). Libraries were prepared using the Direct‐RNA Sequencing Kit SQK‐RNA002 (Oxford Nanopore Technologies) following the manufacturer's instructions and sequenced on R9.4.1 flow cells with a GridION platform (ONT).

2.3. Detection of Differential m5C Modifications

After sequencing, raw fasta5 files were base‐called using Guppy (v3.3.0) (https://community.nanoporetech.com/downloads) to obtain base sequences [24]. Reads was aligned to the human genome reference (GRCh38) using Minimap2 (v2.26) (https://github.com/lh3/minimap2) [25]. The signal data were then resquiggled to match aligned sequences with Nanopolish (https://nanopolish.readthedocs.io/en/latest/) [26].

m5C sites and their modification probabilities were identified with CHEUI (Methylation (CH3) Estimation Using Ionic Current) (https://github.com/comprna/CHEUI), a two‐stage deep‐learning framework for methylation detection. Model 1 of CHEUI estimated the probability of m5C at each cytosine per read. While Model 2 determined site‐level stoichiometry across the transcriptome. Differential methylated sites were defined by difference value (|Δm5C | ) > 0.1 and p < 0.1 between tumor and normal tissues.

Sites with an m5C probability < 0.5 were excluded. Genes and transcripts harboring m5C sites were annotated using BEDTools (v2.31.1) [27], with Gencode (v35) [28] as the reference. The genomic distribution of m5C sites was visualized with RIdeogram (v0.2.2) [29], and sequence motifs were generated using the R package ggseqlogo (v0.2) [30]. Functional analysis was performed with R package clusterProfiler (v4.16.0) [31], validated with two independent bioinformatics platforms, DAVID Functional Annotation Tools (https://davidbioinformatics.nih.gov/tools.jsp), Metascape (http://metascape.org), and the top ten GO terms were plotted in ggplot2 (v3.5.2) [32] for visualization.

2.4. Differential Gene Expression

Long reads were aligned to the human genome GRCh38 using Minimap2 [25], and gene expression was quantified with NanoCount (v1.1.0. post2) (https://aslide.github.io/NanoCount) [33]. R package DESeq. 2 (v1.48.1) [34] was used to identify differentially expressed genes (DEGs) and transcripts (DETs), defined by log2 fold change |logFC | > 1 and adjusted p < 0.05. Heatmaps of m5C‐regulator expression were generated using the R package pheatmap (v1.0.13) [35]. DEGs‐DETs were intersected with differentially methylated genes to identify dysregulated m5C‐ related transcripts using the R package VennDiagram (v1.7.3) [36].

2.5. Data‐Mining of TCGA and GTEx Database

Public RNA sequencing data were analyzed using GEPIA 2 (http://gepia2.cancer-pku.cn/#index), which integrates The Cancer Genome Atlas (TCGA) and Genotype‐Tissue Expression (GTEx) databases. Gene expression profiles and survival analysis were performed via the GEPIA 2 platform to validate candidate markers.

3. Results

3.1. Global Increase of m5C Modification in Tumor Tissues

Nanopore direct‐RNA sequencing was performed on paired tumor and adjacent normal tissues to investigate differences in m5C modifications. A marked global elevation in m5C sites was observed in tumor tissues compared with adjacent normal tissues (Figure 1A). The m5C sites were predominantly located on chromosome 1, showing a pronounced enrichment in both tumor and normal tissues (Figure 1B). Significant differences in m5C site distribution were detected across chromosomes 1, 2, and 3 (Figure 1C). The sequence logos demonstrated a similar motif enrichment pattern for m5C in both tumor and normal tissues (Figure 1D), indicating conserved sequence preference despite global alternations in methylation frequency.

Figure 1.

Figure 1

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Characteristics of m5C sites in tumor and adjacent normal tissues. (A) The number of m5C sites in each group (filtered by m5C value > 0.5). (B) Genomic distribution of differential m5C modifications. The downregulated site is defined as a site has lower m5C ratio in tumor than that in normal tissue, while upregulated site is vice versa. Downregulated sites in tumor tissues are shown as green circles; upregulated sites are shown as orange triangles. The red‐to‐blue scale represents the relative m5C differentiation ratio. (C) Distribution ratio of m5C sites across each chromosome in tumor (red) and normal (blue) tissues. (D) Sequence logos showing motif enrichment of m5C sites in each group.

3.2. Functional Distribution of Differential m5C Modification in HCC

To explore the biological implications of mRNA m5C modification, we identified differential methylated sites using the thresholds of p < 0.1 and |Δm5C | > 0.1. From this analysis, we identified 471 sites with higher m5C levels in normal tissue than in tumor tissue, and 463 sites with higher levels in tumors (Figure 2A). The genomic localization of these differential sites revealed a significant enrichment in the termination transcript site (TTS) and exon regions (Figure 2B), suggesting that m5C may influence translational efficiency. In Figure 2C, the left panel shows the log2 ratio of observed‐to‐expected (obs/exp) modifications, while the right panel displays the log10 p value for enrichment. The highest Log₂ ratio were observed in miRNA regions, indicating strong enrichment of differential m5C modifications, whereas intergenic and intronic regions were depleted. Furthermore, substantial and statistically significant enrichment was observed in Exons and TTS, underscoring their prominent association with m5C modification. Most differentially methylated genes were protein‐coding genes (Figure 2D), underscoring the post‐transcriptional regulation role of m5C in mRNA processing.

Figure 2.

Figure 2

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Differential m5C modifications between tumor and normal tissues. (A) Number of sites with higher m5C levels in tumor or normal tissues across each chromosome. (B) Distribution of differential m5C sites within the functional gene regions. (C) Distribution preference and statistical significance of differential m5C sites across genomic functional regions. (D) Transcripts categories exhibiting differential m5C modifications.

3.3. m5C‐Mediated Regulation of Core Oncogenic and Immune Pathways

Hierarchical clustering analysis of gene expression patterns distinguished tumor from normal tissue groups (Figure 3A). Functional enrichment analysis was first performed using the clusterProfiler. Gene Ontology (GO) enrichment analysis of differential m5C‐modified genes revealed predominant involvement in cell proliferation, survival dysregulation, and gene expression regulation (Figure 3B). To validate these findings, we performed additional enrichment analysis on the same gene set with DAVID and Metascape. The key immune and inflammatory pathways remained significantly enriched, confirming the robustness of the result. Altered m5C methylation may therefore promote uncontrolled cell growth and survival in HCC.

Figure 3.

Figure 3

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Clustering and Gene Ontology (GO) enrichment analysis of differential m5C modifications. N1‐N3 represent adjacent normal samples from patient 1‐3; T1‐T3 represent tumor tissues from the same patients. (A) Hierarchical clustering of gene expression in tumor and normal tissues. (B) GO enrichment analysis of differentially m5C‐modified mRNAs. (C) Heatmap of m5C regulators expression at the gene level. (D) Heatmap of m5C regulators expression at the isoform level.

Additionally, enrichment of immune and inflammatory pathways was observed, particularly in inflammatory response and cytokine‐cytokine receptor interaction pathways. This finding suggests that m5C modifications influence immune evasion and inflammatory remodeling within the TME (Figure 3B).

We next examined the expression of key m5C regulators, including “writers” (e.g., DNMT1 and NSUN family), “readers” (e.g., ALYREF, YTHDF2, and RAD52); and “erasers” (e.g., TET2, TET3, and ALKBH1). Most regulators were consistently upregulated in tumor tissues compared with normal controls (Figure 3C).

Taking advantage of Nanopore sequencing's capacity to detect full‐length isoforms, we further compared isoform‐level expression of these regulators. Heatmap analysis revealed elevated isoform expression in tumors (Figure 3D). Collectively, these findings demonstrate concurrent upregulation of m5C regulators at both gene and isoform levels, suggesting that enhanced m5C activity contributes to epigenetic reprogramming in HCC.

3.4. CES3 Identified as a Potential Prognostic Biomarker

An integrated analysis combing transcriptomic and methylation profile was performed to identify m5C related prognostic indicators in HCC. Ten genes exhibited concurrent m5C and expression alterations (Figure 4A, Supporting Information: Supplementary Table 1). We performed a systematic survival analysis for each candidate gene using the TCGA‐LIHC cohort (n = 439). Among them, only CES3 expression showed a significant association with patient overall survival (Figure 4B), where lower CES3 levels correlated with a poorer prognosis (p < 0.05). In our samples, CES3 showed elevated m5C modification but decreased mRNA expression in tumors. The specific location of this differential m5C site on the CES3 transcript is depicted (Figure 4C). CES3 expression combining TCGA and GTEx dataset was analyzed to further validate the translational relevance of this finding. It was confirmed that CES3 expression was significantly lower in HCC tumors than in normal controls (Figure 4D). Lower CES3 expression was associated with worse overall survival, indicating its potential as a prognostic biomarker. These results imply that aberrant m5C modification may suppress CES3 expression, contributing to tumor progression.

Figure 4.

Figure 4

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Integrated analysis of aberrantly m5C‐modified genes and differentially expressed genes (DEGs). (A) Venn diagram showing overlap between aberrantly m5C‐modified genes and DEGs. (B) Kaplan‐Meier survival curve for CES3. (C) Distribution of m5C sites within the CES3 gene. (D) Expression of CES3 in HCC tumor and normal tissues from the TCGA and GTEx datasets.

3.5. Isoform‐Level Analysis Reveals Prognostic Significance of TMEM234

The long‐read capacity of Nanopore sequencing enabled isoform level integration of differential m5C modification and expression data. A total of 57 dysregulated transcripts were identified (Figure 5A, Supporting Information: Supplementary Table 2). After these transcripts were corresponded to their specific genes, survival analysis using the TCGA‐LIHC cohort (n = 439) indicated that one gene, TMEM234, showed a significant negative association with overall survival in HCC patients (Figure 5B). Higher TMEM234 expression was significantly correlated with poorer overall survival (p < 0.05). Notably, a specific TMEM234 transcript was upregulated in tumors despite exhibiting a decrease in m5C modification (Figure 5C). However, the change of TMEM234 expression in tumor tissues was modest and not significantly different in TCGA and GTEx datasets (Figure 5D). These findings suggest that m5C modification may exert transcript isoform‐specific effects, thus influencing clinical outcomes in HCC and warranting further investigation.

Figure 5.

Figure 5

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Integrated analysis of aberrantly m5C‐modified transcripts and differentially expressed transcripts (DETs). (A) Venn diagram showing overlap between aberrantly m5C‐modified transcripts and DETs. (B) Kaplan‐Meier survival curve for TMEM234. (C) Distribution of m5C sites within TMEM234. (D) Expression profiles of TMEM234 in HCC tumor and normal tissues from the TCGA and GTEx databases.

4. Discussion

mRNA m5C modification is an emerging epitranscriptomic mark with growing recognition for its role in RNA stability, translation and cellular homeostasis. However, its contribution to HCC has remained elusive. In this study, we provided the first comprehensive characterization of mRNA modifications in HBV‐negative HCC using Nanopore direct RNA sequencing. We demonstrate that m5C sites are widely distributed across the transcriptome and exhibit pronounced tumor‐specific alterations, supporting an essential role of m5C in hepatocarcinogenesis.

Our results revealed that differential m5C‐modified genes are predominantly enriched in protein‐binding and plasma membrane‐associated functions, suggesting that m5C influences gene expression and cellular function through modulation of RNA‐protein interactions. This is consistent with previous studies showing that m5C modification can alter the affinity of RNA‐binding proteins involved in splicing, transport, and translation regulation [37].

The spatial distribution of m5C sites in this study offers new insights into the selectivity of epitranscriptomic regulation in HCC. We observed significant enrichment of differential m5C sites within miRNA regions, implying a potential role in miRNA maturation and target recognition [6, 38, 39]. This finding aligns with accumulating evidence that RNA methylating contributes to post‐transcriptional gene silencing by modulating miRNA processing and interaction with target transcripts. Conversely, the depletion of differential m5C sites in intergenic and intronic regions suggests that methylation changes preferentially occur in transcriptionally active regions rather than nonfunctional genomic domains. The pronounced enrichment of m5C modifications in TTS may reflect altered termination efficiency [40] and transcript elongation dynamics [41]. Collectively, these results support the model that m5C is a regulated epigenetic mark deposited at sites of transcriptional activity and potential regulatory significance.

Our functional enrichment analysis revealed that genes with aberrant m5C modifications are significantly enriched in immune‐related pathways. This suggests the m5C epitranscriptome may help shape the tumor immune landscape, consistent with broader roles for RNA modifications in immune regulation. A concrete mechanistic link exists in which the m5C writer NSUN2, together with the reader ALYREF, stabilizes PD‐L1 mRNA in an m5C‐dependent manner to promote its translation in cancers like non‐small cell lung cancer [42, 43]. This provides a direct model for how m5C can regulate a central immune checkpoint in the tumor microenvironment.

Among these identified genes, CES3 emerged as a candidate tumor suppressor under epitranscriptomic regulation. Prior studies have shown that CES3 expression was downregulated in colon adenocarcinoma and correlated with immune infiltration and therapeutic response [44]. The finding that higher m5C modification correlates with CES3 downregulation suggests m5C may act as a repressive mark. One hypothesis is that m5C recruits reader proteins that promote mRNA decay or inhibit translation, reducing CES3 protein. Since CES3 is a carboxylesterase critical for lipid metabolism and drug detoxification, its suppression could alter lipid homeostasis or increase chemoresistance, potentially conferring a survival advantage to tumor cells and contributing to poorer outcomes. Further functional investigations are warranted to delineate the causal relationship between m5C and CES3 downregulation in HCC.

The loss of m5C at specific TMEM234 sites could potentially stabilize its transcript or enhance nuclear export, thereby increasing its translational availability. Isoform‐specific analysis further revealed the prognostic relevance of TMEM234. While total TMEM234 expression showed minimal change in public datasets, its isoform‐level dysregulation correlated strongly with patient survival. This finding highlights the value of long‐read sequencing in detecting isoform‐dependent regulation that conventional short‐read approaches may overlook. We propose that TMEM234's prognostic impact may be driven by m5C‐dependent modulation of specific transcript variants, warranting further experimental validation.

The small sample size may limit the generalizability of our findings due to potential inter‐sample heterogeneity. However, the key candidate genes, CES3 and TMEM234, showed a consistent direction of change across all samples, suggesting a shared dysregulation pattern in this cohort.

This study has several limitations. First, the sample size was small (three paired samples), which restricts statistical power and generalizability. The limited sample size reduces statistical power, making it more difficult to detect subtle but biologically relevant differential methylation events. Larger cohorts are required to validate these findings. Second, only HBV‐negative HCC cases were analyzed, limiting extrapolation to other etiological subtypes such as HBV or HCV related HCC. Third, while we observed correlations between m5C, CES3, and TMEM234, the mechanistic pathways underlying these alterations to HCC remain to be elucidated. Future investigations integrating functional assays, methylation editing, and single cell transcriptomics could help clarify the undying mechanism of m5C dysregulation in HCC.

In conclusion, our study provides the first Nanopore‐based transcriptome‐wide landscape of m5C modifications in HBV‐negative HCC. The global alteration of m5C, coupled with dysregulated expression of its regulators, underscores the significance of m5C in post‐transcriptional gene control and tumor progression. These results identify m5C as a promising biomarker and potential therapeutic target for precision oncology in HCC.

Author Contributions

Tianhan Sun and Liying Zhou contributed equally to this work. Hongyuan Cui conceived and supervised the study. XiaoQing Li collected the patient samples. Gaoyuan Sun and Wei Huang performed the sequencing experiments. Tianhan Sun, Liying Zhou, XiaoQing Li, Ryan Xiao, Lili Zhang, and Yifei Li analyzed the data. Liying Zhou, Yayu Li, Lu Kuai, and Xuanmei Luo prepared the figures and tables. Tianhan Sun drafted the manuscript. Meng Chen critically reviewed and revised the manuscript. All authors reviewed and approved the final version.

Ethics Statement

The study protocol was reviewed and approved by the Independent Ethics Committee of National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (Approval No. NCC2023C‐612).

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Supplementary Table 1: A total of 10 genes exhibited concurrent m5C and expression alterations between tumor and normal samples. Supplementary Table 2: A total of 57 transcripts exhibited concurrent m5C and expression alterations between tumor and normal samples.

MC-65-397-s001.docx (17.6KB, docx)

Acknowledgments

We sincerely appreciate the patients and their families for their invaluable participation in this research. Their contributions were essential to the successful completion of this work. This work was supported by the Beijing Natural Science Foundation (Grant/Award Number: IS23101); the CAMS Innovation Fund for Medical Sciences (CIFMS) (Grant/Award Number: 2021‐I2M‐1‐066); the National Key Research and Development Program of China (Grant/Award Number: 2022YFC2705000); and the National High Level Hospital Clinical Research Funding (Grant/Award Number: BJ‐2025‐129).

Sun T., Zhou L., Li X., et al., “The 5‐methylcytosine RNA modification in Hepatitis B Virus‐Negative Hepatocellular Carcinoma: Insights From Long‐Read Nanopore Sequencing,” Molecular Carcinogenesis 65 (2026): 397‐406, 10.1002/mc.70080.

Tianhan Sun and Liying Zhou should be considered joint first author.

Contributor Information

Hongyuan Cui, Email: cuihongyuan3921@bjhmoh.cn.

Meng Chen, Email: chenmeng@cicams.ac.cn.

Data Availability Statement

The data that support the findings of this study are openly available in Genome Sequence Archive for Human at https://ngdc.cncb.ac.cn/gsa-human/, reference number HRA012605.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Table 1: A total of 10 genes exhibited concurrent m5C and expression alterations between tumor and normal samples. Supplementary Table 2: A total of 57 transcripts exhibited concurrent m5C and expression alterations between tumor and normal samples.

MC-65-397-s001.docx (17.6KB, docx)

Data Availability Statement

The data that support the findings of this study are openly available in Genome Sequence Archive for Human at https://ngdc.cncb.ac.cn/gsa-human/, reference number HRA012605.

Articles from Molecular Carcinogenesis are provided here courtesy of Wiley

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