Discovery of DNA Methylation-Driven Genes in Hepatocellular Carcinoma via Multi-Omics Integration and Functional Role of PHYHD1

Discovery of DNA Methylation-Driven Genes in Hepatocellular Carcinoma via Multi-Omics Integration and Functional Role of PHYHD1 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Discovery of DNA Methylation-Driven Genes in Hepatocellular Carcinoma via Multi-Omics Integration and Functional Role of PHYHD1 Tianfu Guo, Yafei Li, Tiansheng He, Hui Luo, Yuwen Liu, binhui Xie, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7776922/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective: Hepatocellular carcinoma (HCC) is characterized by profound epigenetic dysregulation, particularly in DNA methylation. This study leverages integrated multi-omics to systematically identify key methylation-driven genes in HCC, with a subsequent focus on characterizing the expression patterns and biological functions of a previously understudied candidate, PHYHD1 . Methods: We collected five pairs of HCC tissues and matched adjacent non-tumor counterparts for integrated multi-omics profiling, including whole-genome bisulfite sequencing (WGBS), RNA sequencing (RNA-seq), and TMT-based quantitative proteomics. Differentially methylated regions (DMRs), differentially expressed genes (DEGs), and differentially expressed proteins (DEPs) were identified. Bioinformatic analyses, including functional enrichment and protein-protein interaction (PPI) network construction, were used to prioritize candidate genes, which were further validated using public datasets (TCGA, GEO). Methylation status and expression of PHYHD1 were verified using RT-qPCR, Western blot, and immunohistochemistry (IHC) in cell lines, patient tissues, and a DEN/CCl₄-induced murine HCC model. Functional impact of PHYHD1 on HCC cell proliferation, apoptosis, and tumorigenicity was assessed via in vitro assays (CCK-8, colony formation, flow cytometry) and an in vivo Phyhd1 knockout mouse model. Results: WGBS revealed global hypo-methylation in HCC, identifying 97,523 DMRs. Integration of methylome, transcriptome, and proteome data yielded 19 genes with consistent differential status across all three levels. Functional annotation showed enrichment in metabolic pathways, including retinol, tyrosine, and pyruvate metabolism. Although PHYHD1 was hyper-methylated and down-regulated at both mRNA and protein levels in HCC, its gain- or loss-of-function did not significantly affect cell proliferation, colony formation, apoptosis in vitro , or liver tumorigenesis in vivo . Conclusion: Our integrated multi-omics approach identified a panel of potential DNA methylation-driven genes in HCC. PHYHD1 was confirmed as an epigenetically silenced gene in HCC, but its manipulation did not alter classic malignant phenotypes, suggesting it may not act as a canonical driver gene. Its silencing may represent a passenger event or influence tumor progression through non-cell-autonomous mechanisms. The biological role of PHYHD1 warrants further investigation. Hepatocellular Carcinoma Multi-omics DNA Methylation PHYHD1 Figures Figure 1 Figure 2 Figure 3 Introduction Liver cancer represents a major global health challenge, ranking as the sixth most common cancer and the third leading cause of cancer-related mortality worldwide. According to estimates from the International Agency for Research on Cancer (IARC), approximately 865,000 new cases of liver cancer are diagnosed annually, resulting in nearly 757,948 deaths[ 1 ]. As the predominant form of primary liver cancer, Hepatocellular carcinoma (HCC) is characterized by its high malignancy and rapid progression. The disease often remains asymptomatic in its early stages, and the lack of effective treatment options means that most patients are diagnosed at an advanced stage, contributing to a poor prognosis[ 2 ]. Therefore, a deeper understanding of the molecular mechanisms driving HCC development is critical for identifying novel biomarkers to improve early diagnosis and enable precision therapies. Epigenetic alterations, particularly DNA methylation, as key contributors to tumorigenesis. As a fundamental epigenetic mechanism, DNA methylation regulates gene expression by modifying promoter activity and is involved in critical biological processes such as cell differentiation, genomic stability, and cancer development[ 3 , 4 ]. HCC exhibits a distinct methylation landscape, combining genome-wide hypo-methylation, which can promote genomic instability and proto-oncogene activation with localized promoter hyper-methylation that often silences tumor suppressor genes[ 5 ]. These changes play a pivotal role in HCC initiation, progression, and metastasis. Previous studies have largely relied on methylation microarray technologies (e.g., 27K or 450K arrays) to profile DNA methylation in HCC, providing important insights into its epigenetic regulation [ 6 , 7 ]. However, these arrays are limited to pre-defined CpG sites, leaving large portions of the methylome unexplored and potentially missing rare methylation variants and novel driver events. Moreover, most studies have focused exclusively on methylation-transcriptome relationships[ 8 ], which are essential for bridging the gap between genetic alterations and functional protein outcomes. Recent advances in high-throughput sequencing have made it possible to characterize methylation patterns at single-base resolution using whole-genome bisulfite sequencing (WGBS), offering an unbiased view of the HCC methylome [ 9 ]. In parallel, multi-omics integration strategies now enable a systems-level exploration of HCC, combining genomic, epigenomic, transcriptomic, and proteomic data to uncover novel drivers and pathways [ 10 , 11 ]. This approach has already demonstrated promise in identifying new therapeutic targets and refining HCC classification [ 5 ]. Guided by these developments, we applied an integrated multi-omics strategy—combining WGBS, RNA-seq, and TMT-based quantitative proteomics—to HCC and matched non-tumor tissues. Our goal was to systematically identify key genes driven by DNA methylation in HCC and to validate their clinical and functional relevance. Using publicly available datasets (TCGA, GEO, and CPTAC) for confirmation, we further explored the biological role of a leading candidate gene, PHYHD1 , through in vitro and in vivo functional studies. This work aims to provide new molecular insights and a theoretical foundation for early detection and targeted treatment of HCC. Materials and Methods Clinical Samples and Data This study was approved by the Ethics Committee of the First Affiliated Hospital of Gannan Medical University, and written informed consent was obtained from all participating patients. Between February and April 2019, we collected five pairs of fresh-frozen tissue samples, comprising HCC tissue and matched adjacent non-tumor tissue, from male patients (average age: 66 years) with pathologically confirmed HCC. All tissue samples were immediately snap-frozen in liquid nitrogen following surgical resection and stored for subsequent multi-omics sequencing analysis. To further validate our findings, a commercially available tissue microarray (Product No.: HLivH160CS02, Shanghai Outdo Biotech Co., Ltd.) containing 76 pairs of HCC and adjacent non-tumor tissues was utilized for immunohistochemical (IHC) validation. Publicly DNA methylation data (Illumina HumanMethylation450K BeadChip) and RNA-seq data were downloaded from The Cancer Genome Atlas (TCGA) database. Gene methylation and expression levels were further validated using the Gene Expression Omnibus (GEO) datasets (GSE136319, GSE136583, GSE112790, and GSE121248). Proteomics data were obtained from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) database. Animals and Cell Lines Male wild-type (WT) and Phyhd1 knockout ( Phyhd1 −/− ) C57BL/6J mice (2 weeks old) were purchased from GemPharmatech Co., Ltd. (Jiangsu, China). All mice were housed under specific pathogen-free (SPF) conditions in a barrier facility at the Animal Experiment Center of Gannan Medical University, with a 12-hour light/dark cycle, temperature of 24 ± 2 ℃, and relative humidity of 50 ± 5%. The human normal hepatic stellate cell line LX-2 and HCC cell lines (SMMC-7721, MHCC97-H, Huh-7, and BEL-7402) were obtained from the Cell Bank of the Chinese Academy of Sciences (Shanghai, China). LX-2 and MHCC97-H cells were cultured in high-glucose DMEM; SMMC-7721, BEL-7402, and Huh-7 cells were maintained in RPMI 1640 medium. All media were supplemented with 10% FBS and 1% penicillin-streptomycin. Cells were cultured at 37 ℃ in a humidified incubator with 5% CO₂. All cell lines were regularly tested for mycoplasma contamination, and their identities were authenticated by short tandem repeat (STR) profiling. Experimental Methods Mouse HCC Model Establishment Hepatocarcinogenesis was induced in 15-day-old male WT and Phyhd1 −/− mice by a single intraperitoneal (i.p.) injection of diethylnitrosamine (DEN) at a dose of 25 mg/kg body weight. After one week, mice received weekly i.p. injections of 10% carbon tetrachloride (CCl₄) in olive oil at 0.5 mL/kg for a total of 22 weeks. Mice were euthanized by cervical dislocation at the experimental endpoint. Livers were excised, and visible surface tumor nodules (diameter ≥ 1 mm) were counted. The maximum tumor diameter was measured, and the liver-to-body weight ratio (liver weight/body weight × 100%) was calculated. Liver tissues were snap-frozen or fixed in 4% paraformaldehyde for histological analysis. Multi-Omics Sequencing and Data Analysis Five pairs of HCC and adjacent non-tumor tissues underwent WGBS, RNA-seq, and quantitative proteomics analysis. WGBS Genomic DNA was bisulfite-converted using the EZ DNA Methylation-Gold™ Kit (Zymo Research, USA). Libraries were sequenced on an Illumina HiSeq X Ten platform (PE150). Raw data quality-checked with FastQC (v0.11.9) and trimmed with Trimmomatic (v0.39). Clean reads were aligned to the human reference genome (hg19) using Bismark (v0.22.3) to extract cytosine methylation calls[ 12 ]. Differentially methylated regions (DMRs) were identified using the DSS package (v2.42.0) in R with thresholds set at |Δβ| ≥ 0.25 and false discovery rate (FDR) < 0.05[ 13 ]. RNA-seq Total RNA was isolated using TRIzol® reagent (Invitrogen, USA). Sequencing libraries were constructed with the NEBNext® Ultra™ RNA Library Prep Kit (NEB, USA) and sequenced on an Illumina NovaSeq 6000 platform (PE150). Raw reads were quality-controlled and filtered using Fastp (v0.23.2). High-quality clean reads were aligned to the hg19 genome using HISAT2 (v2.2.1). Transcript assembly and abundance estimation were performed with StringTie (v2.1.5). Differentially expressed genes (DEGs) were identified using the edgeR package (v4.0.16) applying a generalized linear model (GLM), with significance thresholds set at |log₂(fold change)| ≥ 1.5 and FDR < 0.05. Proteomics Proteins were extracted from tissues, quantified using a BCA assay (Thermo Scientific, 23225), and digested with trypsin following standard protocols (reduction with DTT, alkylation with IAA). Resulting peptides were labeled using TMT reagents (Thermo Scientific). Labeled samples were pooled and fractionated by high-pH reverse-phase chromatography using a RIGOL L3000 HPLC system with a Waters BEH C18 column. LC-MS/MS analysis was performed on an EASY-nLC 1200 UHPLC system coupled to a Q Exactive HF-X mass spectrometer in data-dependent acquisition (DDA) mode. Raw data were searched against the UniProt human protein database using Proteome Discoverer software (v2.2, Thermo Scientific) with specified search parameters. Protein identification was filtered at 1% FDR at the peptide-spectrum match (PSM) and protein levels. Differential protein expression analysis was performed using the limma package in R, with DEPs defined by |log₂(fold change)| ≥ 1 and FDR < 0.05. Bioinformatic Analysis Functional enrichment analysis, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, was performed for the identified DMGs, DEGs, and DEPs using the DAVID bioinformatics resource, with an FDR 0.7) and visualized using Cytoscape software (v3.6.1)[ 14 ]. Hub genes within the networks were identified using the cytoHubba plugin, selecting the top 10 nodes by connectivity[ 15 ]. Molecular Biology Experiments RT-qPCR Total RNA was extracted using the TransZol Up Plus RNA Kit (TransGen, China). cDNA was synthesized from DNase I-treated RNA using the PrimeScript RT reagent Kit (TaKaRa, Japan). Quantitative PCR was performed using SYBR Premix Ex Taq II (TaKaRa, Japan) on a QuantStudio 7 Flex system (Applied Biosystems, USA). Relative mRNA expression levels were calculated using the 2^(-ΔΔCT) method with β-actin as the endogenous control. Primer specificity was confirmed by BLAST analysis. Western Blotting : Proteins were extracted using RIPA lysis buffer containing PMSF, quantified by BCA assay, separated by SDS-PAGE, and transferred to PVDF membranes. After blocking, membranes were incubated overnight at 4 ℃ with primary antibodies against PHYHD1 (abcam, ab181232; 1:1000) and β-Actin (Proteintech, 66009-1-Ig; 1:5000), followed by incubation with HRP-conjugated secondary antibodies. Signals were detected using ECL substrate and analyzed with ImageJ software, normalized to β-Actin. IHC : Paraffin-embedded sections underwent antigen retrieval, peroxidase blocking, and blocking with normal serum before incubation with anti-PHYHD1 antibody (1:200) overnight at 4°C. Staining was developed using DAB after incubation with an HRP-polymer secondary antibody, followed by hematoxylin counterstaining. Staining intensity and the percentage of positive cells were scored independently by two blinded pathologists[ 16 ]. Cell Function Assays Cell Transfection To modulate PHYHD1 expression, transfection was performed using Lipofectamine™ 3000 reagent (Invitrogen, USA) according to the manufacturer's protocol. The pRP[CMV]-3xFLAG hPHYHD1 overe-xpression plasmid was transfected into MHCC97-H cell lines, while SMMC-7721 cells were transfected with specific short hairpin RNA (shRNA) targeting PHYHD1. Control groups included cells transfected with a blank vector or a scrambled negative shRNA. Cells were harvested 48 hours post-transfection, and transfection efficiency was confirmed by Western blot analysis. Cell Proliferation Assay Cell proliferation was assessed using the CCK-8 assay. Cells from each group were seeded into 96-well plates at a density of 3×10³ cells per well (n = 5 replicates per group). At 0, 24, 48, and 72 hours post-transfection, 10 µL of CCK-8 reagent (Dojindo, Japan) was added to each well, followed by incubation at 37 ℃ for 2 hours. The absorbance at 450 nm was measured using a microplate reader (BioTek, USA), and cell growth curves were generated based on the optical density (OD) values. Colony Formation Assay Stably transfected cells were plated in 6-well plates at a low density of 500 cells per well and cultured under standard conditions (37 ℃, 5% CO₂) for 14 days, with the medium refreshed every three days. After the incubation period, cells were gently washed with PBS, fixed with 4% paraformaldehyde for 15 minutes, and stained with 0.1% crystal violet for 20 minutes. The plates were then rinsed under running water and air-dried at room temperature. Colonies containing more than 50 cells were counted after scanning the images. The experiment was independently repeated three times. Cell Apoptosis Analysis by Flow Cytometry Apoptosis was evaluated 48 hours after transfection using an Annexin V-APC/7-AAD Apoptosis Detection Kit (KeyGEN BioTECH, China) according to the manufacturer's instructions. Briefly, harvested cells were stained and analyzed on a BD FACS Celesta flow cytometer (BD, USA). Data processing was performed using FlowJo software (v10.6.2). The total apoptosis rate was defined as the sum of early apoptotic (Annexin V-APC⁺/7-AAD⁻) and late apoptotic (Annexin V-APC⁺/7-AAD⁺) cell populations. Statistical Analysis All experiments were independently repeated at least three times. Data are presented as the mean ± standard error of the mean (SEM). Comparisons between two groups were performed using Student's t-test (paired or unpaired, as appropriate), while one-way analysis of variance (ANOVA) followed by Tukey's post-hoc test was used for multi-group comparisons. Survival curves were generated using the Kaplan-Meier method, and differences were assessed with the log-rank test. Correlations between variables were analyzed using Pearson's correlation coefficient. A p-value of less than 0.05 was considered statistically significant. All statistical analyses were conducted using GraphPad Prism (version 9.0) and R software (version 4.3.0). Results Multi-omics Profiling Reveals Extensive Epigenetic and Expression Alterations in HCC We performed an integrated multi-omics analysis on 5 paired HCC and adjacent non-tumor tissues using whole-genome bisulfite sequencing (WGBS), RNA sequencing (RNA-seq), and TMT-based quantitative proteomics. WGBS data revealed a global hypomethylation trend in HCC tissues (Fig. 1 A). We identified 97,523 differentially methylated regions (DMRs), which were predominantly enriched in intronic regions (27.82%), CpG islands (16.60%), and CpG island shores (14.97%) (Fig. 1 B), corresponding to 13,384 differentially methylated genes (DMGs). Transcriptome analysis identified 875 differentially expressed genes (DEGs; 547 upregulated, 328 downregulated) (Fig. 1 C). Proteomic profiling revealed 225 differentially expressed proteins (DEPs; 53 upregulated, 172 downregulated) (Fig. 1 D). These results demonstrate widespread dysregulation at the epigenetic, transcriptomic, and proteomic levels in HCC. Identification of Core Methylation-Regulated Genes through Multi-omics Integration Integration of methylome and transcriptome data identified 268 differentially methylated and expressed genes (DMEGs), comprising 80 hypermethylated and downregulated genes and 188 hypomethylated and upregulated genes (Fig. 1 E). Functional enrichment analysis indicated their involvement in extracellular matrix organization and metabolic processes (Fig. S1 A, S1B). Protein-protein interaction (PPI) network analysis highlighted key hub genes among these DMEGs (Fig. S1 C, S1D). Further integration of transcriptome and proteome data identified 74 genes consistently differentially expressed at both mRNA and protein levels (Fig. 1 F), which were significantly enriched in metabolic pathways (Fig. S1 E-S1H). Cross-analysis of all three omics layers pinpointed 19 genes with concordant alterations in methylation, mRNA, and protein levels (13 downregulated, 6 upregulated) (Fig. 1 F). These multi-omics core genes showed significant enrichment in metabolic pathways, including retinol and tyrosine metabolism (Fig. 1 I, 1 J). Candidate Gene Selection We intersected the 19 multi-omics core genes with 338 methylation-driven genes identified from TCGA analysis[ 17 ], which prioritized TAT, SFN, and PHYHD1 for further investigation (Fig. 2 A). Analysis of TCGA data confirmed a hypermethylated/low-expression pattern for TAT and PHYHD1, and a hypomethylated/high-expression pattern for SFN in HCC (Fig. 2 B, 2 C). While TAT and SFN have established roles in HCC[ 18 , 19 ]. the function of PHYHD1 remains unclear. A significant negative correlation was observed between PHYHD1 methylation and its expression (Fig. 2 D), leading us to focus subsequent studies on PHYHD1. PHYHD1 is Hypermethylated and Downregulated in HCC Validation using independent datasets confirmed higher methylation levels (GSE136319, GSE136583) and lower mRNA expression (GSE112790, GSE121248) of PHYHD1 in HCC tissues compared to non-tumor tissues (Fig. 2 E, 2 F). Consistent with this, CPTAC data showed significantly lower PHYHD1 protein levels in HCC (Fig. 2 G). Immunohistochemistry (IHC) revealed that PHYHD1 protein was localized in both the cytoplasm and nucleus, and its expression was significantly reduced in HCC tissues (Fig. 2 H, 2 I). At the cellular level, RT-qPCR showed higher PHYHD1 expression in LX-2 hepatic stellate cells than in several HCC cell lines (SMMC-7721, MHCC97-H, Huh-7) (Fig. 2 J). In a DEN/CCl₄-induced mouse HCC model, Phyhd1 mRNA and protein levels were significantly lower in tumor tissues compared to adjacent non-tumor liver tissues (Fig. 2 K-N). These consistent findings across models and platforms establish PHYHD1 as a hyper-methylated and down-regulated gene in HCC. Modulation of PHYHD1 Expression Does Not Affect HCC Cell Proliferation or Apoptosis To investigate the functional role of PHYHD1 , we over-expressed it in MHCC97-H cells (which have low endogenous expression) and knocked it down in SMMC-7721 cells. CCK-8 assays showed that altering PHYHD1 expression did not significantly affect cell proliferation (Fig. 3 A, 3 B). Similarly, colony formation assays revealed no changes in clonogenic ability upon PHYHD1 over-expression or knockdown (Fig. 3 C, 3 D). Flow cytometry analysis of apoptosis indicated that the rates of early and late apoptosis remained unaltered (Fig. 3 E, 3 F). These in vitro results suggest that PHYHD1 does not directly regulate HCC cell proliferation or apoptosis under the tested conditions. Phyhd1 Knockout Does Not Influence HCC Development in Mice To assess the role of PHYHD1 in vivo, we subjected Phyhd1 -/- mice and wild-type (WT) controls to a DEN/CCl₄-induced HCC carcinogenesis model. Compared to WT mice, Phyhd1 -/- mice showed no significant differences in tumor number, maximum tumor diameter, or liver-to-body weight ratio (Fig. 3 G-J). These in vivo findings are consistent with the in vitro results, indicating that the loss of Phyhd1 does not significantly impact HCC development in this model. Discussion This study identified DNA methylation-driven genes in hepatocellular carcinoma (HCC) by integrating data from WGBS, RNA-seq, and proteomics. We successfully pinpointed 19 candidate genes consistently dysregulated across the epigenetic, transcriptional, and translational levels, and selected PHYHD1 for in-depth expression validation and functional exploration. To our knowledge, this is the first study to confirm that PHYHD1 is hyper-methylated and down-regulated in HCC. Surprisingly, however, altering PHYHD1 expression did not significantly affect HCC cell proliferation, apoptosis, or in vivo tumorigenicity in our experiments. This seemingly contradictory finding prompts a reevaluation of the complex role of epigenetic silencing events in HCC initiation and progression. The integrated multi-omics strategy employed here effectively overcomes the limited coverage of traditional methylation microarrays. WGBS provides a genome-wide methylation profile at single-base resolution, and integrating this with transcriptome and proteome data significantly improves the accuracy of identifying driver events. Consistent with previous reports[ 8 , 20 ],we observed a genome-wide hypo-methylation pattern in HCC tissues. Among our 19 candidate genes, several are well-established HCC-related genes. For instance, SFN promotes the malignant progression of HCC cells by enhancing AKT phosphatase activity through disrupting the PHLPP2-AKT interaction[ 18 ], while TAT induces mitochondria-dependent apoptosis in HCC cells by promoting cytochrome C release, thereby activating caspase-9 and PARP[ 19 ]. The identification of these known genes strongly validates the reliability of our screening approach, justifying our focus on the previously understudied gene, PHYHD1 . PHYHD1 encodes a structural analog of phytanoyl-CoA hydroxylase (PHYH/PAHX). This enzyme acts on fatty acids, localizes to peroxisomes, and is mutated in certain neurological disorders[ 21 ]. Genomic variations in PHYHD1 are associated with altered levels of nucleotide metabolites, including guanine, 5-methylcytosine, and 5-methyluracil[ 22 ]. In various cell lines, endogenous and recombinant PHYHD1 are localized in both the nucleus and cytoplasm. In contrast, its closest homolog, the 2-oxoglutarate-dependent dioxygenase (2OGDD) PHYH, is peroxisomal[ 23 ]. Cells lacking PHYHD1 also exhibit reduced efficiency in utilizing glucose and maltose. Pathway analysis links PHYHD1 to cell division and RNA metabolism proteins (particularly those involved in mRNA splicing), and suggests potential roles in mRNA transport and transcription[ 24 ]. Current research on PHYHD1's role in disease is limited: it has been associated with T cell differentiation and effector T cell function in mice[ 25 ];altered methylation and transcription of PHYHD1 have been observed in non-functioning pituitary adenomas[ 26 ], and a genome-wide association study identified PHYHD1 as a novel genetic determinant of blood metabolites in chronic kidney disease[ 27 ]. However, its role and mechanism in cancer, particularly in HCC initiation and progression, had not been reported prior to our study. In our validation, we consistently observed PHYHD1 hyper-methylation and down-regulation across multiple independent datasets, human tissues, and mouse models. Subsequent functional experiments, however, revealed that neither over-expression nor knockdown of PHYHD1 affected HCC cell proliferation, apoptosis, or in vivo tumorigenicity. This "epigenetic silencing versus functional loss paradox" can be explained in several ways. First, PHYHD1 hyper-methylation may be a "passenger event"—a consequence of global epigenetic dysregulation in HCC—rather than a direct driver of tumorigenesis. Second, PHYHD1 function may be highly context-dependent, with its role becoming critical only under specific conditions, such as metabolic stress or immune microenvironment interactions, which were not replicated in our standard experimental settings. Additionally, functional redundancy or compensatory mechanisms might offset the direct phenotypic impact of PHYHD1 loss. This study has several limitations. First, the initial WGBS cohort was small; although we validated our findings using multiple public datasets, a larger sample size would strengthen our conclusions. Second, the DEN/ CCl₄-induced mouse HCC model does not fully recapitulate the complex heterogeneity and immune microenvironment of human HCC. Most importantly, the specific molecular and biochemical functions of PHYHD1 remain unclear. Conclusions In conclusion, this study identified a panel of potential methylation-driven genes in HCC through integrated multi-omics analysis and, for the first time, systematically validated PHYHD1's expression patterns and explored its function. Our results suggest that the frequent epigenetic silencing of PHYHD1 in HCC does not directly drive the malignant phenotype. Instead, PHYHD1 may act as a context-dependent gene that exerts effects under specific stress conditions or indirectly contributes to HCC progression by regulating the tumor microenvironment. This finding not only deepens our understanding of the complexity of epigenetic regulation in HCC but also highlights future research directions. Future studies should explore PHYHD1's specific roles in hepatic metabolic homeostasis and tumor microenvironment regulation using models that better mimic physiological and pathological conditions, such as systems involving metabolic disturbance, hypoxia, or cell co-culture. Declarations Ethics approval and consent to participate This study was reviewed and approved by the Institutional Research Ethics Committee of Gannan Medical University (Ganzhou, China), with the approval numbers 2024495 for human research and 2024473 for animal research. Written informed consent was obtained from all individual participants included in the study. Funding This work was supported by the Key Project of Science and Technology of Jiangxi Provincial Department of Education (GJJ2401319). Declaration of competing interest The authors affirm that they have no conflicts of interest, whether financial or personal, that could have influenced the outcomes reported in this study. Availability of data and materials The datasets analyzed during the current study are available in the following public repositories: The Cancer Genome Atlas (TCGA)-LIHC, Gene Expression Omnibus (GEO) (accession numbers are provided within the manuscript), and the Clinical Proteomic Tumor Analysis Consortium (CPTAC). The raw sequencing data generated in this study are available as Supplementary Files in this article. CRediT authorship contribution statement Tianfu Guo: Conceptualization, Funding acquisition, Methodology, Formal analysis, Investigation, Writing – original draft, Writing – review & editing. Yafei Li and Tiansheng He: Data curation, Investigation, Methodology. Hui Luo: Investigation. Binhui Xie and Yuewen Liu: Resources, Investigation. Zhiping Liu: Conceptualization, Funding acquisition, Project administration, Resources, Supervision, Validation. Data Availability The datasets analyzed during the current study are available in the following public repositories: The Cancer Genome Atlas (TCGA)-LIHC, Gene Expression Omnibus (GEO) (accession numbers are provided within the manuscript), and the Clinical Proteomic Tumor Analysis Consortium (CPTAC). The raw sequencing data generated in this study are available as Supplementary Files in this article. References Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A: Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2024, 74(3):229–263. Llovet JM, Kelley RK, Villanueva A, Singal AG, Pikarsky E, Roayaie S, Lencioni R, Koike K, Zucman-Rossi J, Finn RSJNrDp: Hepatocellular carcinoma. Nat Rev Dis Primers 2021, 7(1):6. 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Supplementary Files FigS1.pptx Fig2Mactin.jpg Fig2MPHYHD1.jpg protien.txt RNAcount.txt DMR.result.annotation.xls Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7776922","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":535879317,"identity":"791652ec-2ecb-43d8-ba1f-25418d4bbf5d","order_by":0,"name":"Tianfu Guo","email":"","orcid":"","institution":"Gannan Medical University","correspondingAuthor":false,"prefix":"","firstName":"Tianfu","middleName":"","lastName":"Guo","suffix":""},{"id":535879318,"identity":"8127e031-66d7-4e12-aaf5-a369622da971","order_by":1,"name":"Yafei Li","email":"","orcid":"","institution":"Gannan Medical 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1","display":"","copyAsset":false,"role":"figure","size":380617,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMulti-omics integration identifies core methylation-driven genes in HCC.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) DMR methylation levels in HCC versus adjacent non-tumor tissues. (B) Proportions of DMRs in different areas. (C, D) Volcano plots DEGs(C) and DEPs(D) in HCC. Red and blue dots represent significantly up- and down-regulated molecules, respectively. (E) Venn diagram illustrating the overlap between Hypo-DMGs, hyper-DMGs, up-DEGs and down-DEGs. (F) Integration of methylome, transcriptome, and proteome data identifies 19 consistently altered genes across all three omics layers. (G, H) Functional enrichment analysis of the 19 multi-omics core genes: Gene Ontology (GO) terms (G) and KEGG pathways (H).\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7776922/v1/e961f9343d4f5c9faddbaa86.jpeg"},{"id":94640121,"identity":"52c61830-7142-4f0b-80a0-dbb60cc7d987","added_by":"auto","created_at":"2025-10-29 07:48:22","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1221820,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScreening of candidate genes.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Overlap between the 19 multi-omics core genes and 338 methylation driven genes from TCGA. (B, C) mRNA expression (B) and methylation levels (C) of TAT, SFN, and PHYHD1 in HCC versus normal tissues from TCGA. (D) Correlation between PHYHD1 methylation and its mRNA expression.\u003c/p\u003e\n\u003cp\u003e(E) PHYHD1 mRNA levels were significantly lower in tumor tissues. (F) PHYHD1 promoter methylation was significantly higher in tumor tissues compared to adjacent non-tumor tissues. (G) Analysis of the CPTAC database shows lower PHYHD1 protein levels in HCC tissues compared to adjacent tissues. (H) Representative photomicrographs of IHC staining for PHYHD1 in paired HCC and peritumoral tissues. (I) Quantitative analysis of IHC scores, consistent with the down-regulation of PHYHD1 protein in HCC. (J) The mRNA expression of PHYHD1 in different cell lines. (K) Schematic diagram of the DEN/CCl₄-induced mouse HCC model. (L) qPCR analysis of Phyhd1 mRNA in the mouse model. (M, N) Western blot analysis of Phyhd1 protein levels with representative bands (M) and densitometric quantification (N), further supporting the down-regulation of Phyhd1 in murine HCC. * \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05；** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01；*** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001；**** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7776922/v1/a79230de1034347ea2122624.jpeg"},{"id":94625074,"identity":"075a4ca8-3d29-4cd1-907e-87d3989c4547","added_by":"auto","created_at":"2025-10-29 04:43:07","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":762290,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional assessment of PHYHD1 in HCC proliferation, apoptosis, and tumorigenesis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A, B) Cell proliferation measured by CCK-8 assay after PHYHD1 over-expression (A) and knockdown (B). (C, D) Colony formation ability of HCC cells following PHYHD1 over-expression (C) and knockdown (D). (E, F) Apoptosis analysis by flow cytometry after PHYHD1 over-expression (E) and knockdown (F). (G) Representative livers from wild-type (WT) and \u003cem\u003ePhyhd1\u003c/em\u003e\u003csup\u003e\u003cem\u003e-/-\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e \u003c/em\u003emice after DEN/CCl₄-induced hepatocarcinogenesis. (H-J) Quantification of tumor number (H), maximum tumor diameter (I), and liver/body weight ratio (J) in WT versus \u003cem\u003ePhyhd1\u003c/em\u003e\u003csup\u003e\u003cem\u003e-/-\u003c/em\u003e\u003c/sup\u003e mice.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7776922/v1/7910a77e7b18291fb957dba1.jpeg"},{"id":94739536,"identity":"74b932b8-c0a2-49fa-8f45-90b6ccf6a0c7","added_by":"auto","created_at":"2025-10-30 08:24:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3281407,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7776922/v1/d94ff251-aca0-4e36-9b2a-170937a1f0a7.pdf"},{"id":94625070,"identity":"2e665dd7-e959-412f-8166-3cc7ad7ddbe9","added_by":"auto","created_at":"2025-10-29 04:43:07","extension":"pptx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":679528,"visible":true,"origin":"","legend":"","description":"","filename":"FigS1.pptx","url":"https://assets-eu.researchsquare.com/files/rs-7776922/v1/de70e0c2fc8dcf7df08327b3.pptx"},{"id":94625071,"identity":"3f8f0c13-243e-4e2a-abb3-ba724c021b42","added_by":"auto","created_at":"2025-10-29 04:43:07","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":52716,"visible":true,"origin":"","legend":"","description":"","filename":"Fig2Mactin.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7776922/v1/921afbb6b33261e4be982d56.jpg"},{"id":94625073,"identity":"9767f906-f48b-4465-80c3-a60ece1fbfde","added_by":"auto","created_at":"2025-10-29 04:43:07","extension":"jpg","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":67490,"visible":true,"origin":"","legend":"","description":"","filename":"Fig2MPHYHD1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7776922/v1/1459547bae62d9b5ca6c5856.jpg"},{"id":94625097,"identity":"a4823ce7-270c-4f43-8380-f464fa3ad67e","added_by":"auto","created_at":"2025-10-29 04:43:10","extension":"txt","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":433818,"visible":true,"origin":"","legend":"","description":"","filename":"protien.txt","url":"https://assets-eu.researchsquare.com/files/rs-7776922/v1/6aace6880bab08fbb07d71bb.txt"},{"id":94625079,"identity":"0b323245-cb7f-460b-a592-f932f624af0a","added_by":"auto","created_at":"2025-10-29 04:43:07","extension":"txt","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":2008478,"visible":true,"origin":"","legend":"","description":"","filename":"RNAcount.txt","url":"https://assets-eu.researchsquare.com/files/rs-7776922/v1/ea253b8516d2884138e667e8.txt"},{"id":94625086,"identity":"4cf76307-1ddc-41f0-a763-042f7b3d5f87","added_by":"auto","created_at":"2025-10-29 04:43:08","extension":"xls","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":13090343,"visible":true,"origin":"","legend":"","description":"","filename":"DMR.result.annotation.xls","url":"https://assets-eu.researchsquare.com/files/rs-7776922/v1/9e414f6d04268d6c19c8ad5d.xls"}],"financialInterests":"No competing interests reported.","formattedTitle":"Discovery of DNA Methylation-Driven Genes in Hepatocellular Carcinoma via Multi-Omics Integration and Functional Role of PHYHD1","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLiver cancer represents a major global health challenge, ranking as the sixth most common cancer and the third leading cause of cancer-related mortality worldwide. According to estimates from the International Agency for Research on Cancer (IARC), approximately 865,000 new cases of liver cancer are diagnosed annually, resulting in nearly 757,948 deaths[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. As the predominant form of primary liver cancer, Hepatocellular carcinoma (HCC) is characterized by its high malignancy and rapid progression. The disease often remains asymptomatic in its early stages, and the lack of effective treatment options means that most patients are diagnosed at an advanced stage, contributing to a poor prognosis[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Therefore, a deeper understanding of the molecular mechanisms driving HCC development is critical for identifying novel biomarkers to improve early diagnosis and enable precision therapies.\u003c/p\u003e\u003cp\u003eEpigenetic alterations, particularly DNA methylation, as key contributors to tumorigenesis. As a fundamental epigenetic mechanism, DNA methylation regulates gene expression by modifying promoter activity and is involved in critical biological processes such as cell differentiation, genomic stability, and cancer development[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. HCC exhibits a distinct methylation landscape, combining genome-wide hypo-methylation, which can promote genomic instability and proto-oncogene activation with localized promoter hyper-methylation that often silences tumor suppressor genes[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. These changes play a pivotal role in HCC initiation, progression, and metastasis.\u003c/p\u003e\u003cp\u003ePrevious studies have largely relied on methylation microarray technologies (e.g., 27K or 450K arrays) to profile DNA methylation in HCC, providing important insights into its epigenetic regulation [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, these arrays are limited to pre-defined CpG sites, leaving large portions of the methylome unexplored and potentially missing rare methylation variants and novel driver events. Moreover, most studies have focused exclusively on methylation-transcriptome relationships[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], which are essential for bridging the gap between genetic alterations and functional protein outcomes.\u003c/p\u003e\u003cp\u003eRecent advances in high-throughput sequencing have made it possible to characterize methylation patterns at single-base resolution using whole-genome bisulfite sequencing (WGBS), offering an unbiased view of the HCC methylome [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In parallel, multi-omics integration strategies now enable a systems-level exploration of HCC, combining genomic, epigenomic, transcriptomic, and proteomic data to uncover novel drivers and pathways [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This approach has already demonstrated promise in identifying new therapeutic targets and refining HCC classification [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGuided by these developments, we applied an integrated multi-omics strategy\u0026mdash;combining WGBS, RNA-seq, and TMT-based quantitative proteomics\u0026mdash;to HCC and matched non-tumor tissues. Our goal was to systematically identify key genes driven by DNA methylation in HCC and to validate their clinical and functional relevance. Using publicly available datasets (TCGA, GEO, and CPTAC) for confirmation, we further explored the biological role of a leading candidate gene, \u003cem\u003ePHYHD1\u003c/em\u003e, through \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e functional studies. This work aims to provide new molecular insights and a theoretical foundation for early detection and targeted treatment of HCC.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eClinical Samples and Data\u003c/h2\u003e\u003cp\u003e This study was approved by the Ethics Committee of the First Affiliated Hospital of Gannan Medical University, and written informed consent was obtained from all participating patients. Between February and April 2019, we collected five pairs of fresh-frozen tissue samples, comprising HCC tissue and matched adjacent non-tumor tissue, from male patients (average age: 66 years) with pathologically confirmed HCC. All tissue samples were immediately snap-frozen in liquid nitrogen following surgical resection and stored for subsequent multi-omics sequencing analysis. To further validate our findings, a commercially available tissue microarray (Product No.: HLivH160CS02, Shanghai Outdo Biotech Co., Ltd.) containing 76 pairs of HCC and adjacent non-tumor tissues was utilized for immunohistochemical (IHC) validation.\u003c/p\u003e\u003cp\u003ePublicly DNA methylation data (Illumina HumanMethylation450K BeadChip) and RNA-seq data were downloaded from The Cancer Genome Atlas (TCGA) database. Gene methylation and expression levels were further validated using the Gene Expression Omnibus (GEO) datasets (GSE136319, GSE136583, GSE112790, and GSE121248). Proteomics data were obtained from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) database.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eAnimals and Cell Lines\u003c/h3\u003e\n\u003cp\u003eMale wild-type (WT) and Phyhd1 knockout (\u003cem\u003ePhyhd1\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u0026minus;/\u0026minus;\u003c/em\u003e\u003c/sup\u003e) C57BL/6J mice (2 weeks old) were purchased from GemPharmatech Co., Ltd. (Jiangsu, China). All mice were housed under specific pathogen-free (SPF) conditions in a barrier facility at the Animal Experiment Center of Gannan Medical University, with a 12-hour light/dark cycle, temperature of 24\u0026thinsp;\u0026plusmn;\u0026thinsp;2 ℃, and relative humidity of 50\u0026thinsp;\u0026plusmn;\u0026thinsp;5%.\u003c/p\u003e\u003cp\u003eThe human normal hepatic stellate cell line LX-2 and HCC cell lines (SMMC-7721, MHCC97-H, Huh-7, and BEL-7402) were obtained from the Cell Bank of the Chinese Academy of Sciences (Shanghai, China). LX-2 and MHCC97-H cells were cultured in high-glucose DMEM; SMMC-7721, BEL-7402, and Huh-7 cells were maintained in RPMI 1640 medium. All media were supplemented with 10% FBS and 1% penicillin-streptomycin. Cells were cultured at 37 ℃ in a humidified incubator with 5% CO₂. All cell lines were regularly tested for mycoplasma contamination, and their identities were authenticated by short tandem repeat (STR) profiling.\u003c/p\u003e\n\u003ch3\u003eExperimental Methods\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eMouse HCC Model Establishment\u003c/h2\u003e\u003cp\u003eHepatocarcinogenesis was induced in 15-day-old male WT and \u003cem\u003ePhyhd1\u003c/em\u003e\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e mice by a single intraperitoneal (i.p.) injection of diethylnitrosamine (DEN) at a dose of 25 mg/kg body weight. After one week, mice received weekly i.p. injections of 10% carbon tetrachloride (CCl₄) in olive oil at 0.5 mL/kg for a total of 22 weeks. Mice were euthanized by cervical dislocation at the experimental endpoint. Livers were excised, and visible surface tumor nodules (diameter\u0026thinsp;\u0026ge;\u0026thinsp;1 mm) were counted. The maximum tumor diameter was measured, and the liver-to-body weight ratio (liver weight/body weight \u0026times; 100%) was calculated. Liver tissues were snap-frozen or fixed in 4% paraformaldehyde for histological analysis.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMulti-Omics Sequencing and Data Analysis\u003c/h3\u003e\n\u003cp\u003eFive pairs of HCC and adjacent non-tumor tissues underwent WGBS, RNA-seq, and quantitative proteomics analysis.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eWGBS\u003c/strong\u003e\u003cp\u003eGenomic DNA was bisulfite-converted using the EZ DNA Methylation-Gold\u0026trade; Kit (Zymo Research, USA). Libraries were sequenced on an Illumina HiSeq X Ten platform (PE150). Raw data quality-checked with FastQC (v0.11.9) and trimmed with Trimmomatic (v0.39). Clean reads were aligned to the human reference genome (hg19) using Bismark (v0.22.3) to extract cytosine methylation calls[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Differentially methylated regions (DMRs) were identified using the DSS package (v2.42.0) in R with thresholds set at |Δβ| \u0026ge; 0.25 and false discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.05[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eRNA-seq\u003c/strong\u003e\u003cp\u003eTotal RNA was isolated using TRIzol\u0026reg; reagent (Invitrogen, USA). Sequencing libraries were constructed with the NEBNext\u0026reg; Ultra\u0026trade; RNA Library Prep Kit (NEB, USA) and sequenced on an Illumina NovaSeq 6000 platform (PE150). Raw reads were quality-controlled and filtered using Fastp (v0.23.2). High-quality clean reads were aligned to the hg19 genome using HISAT2 (v2.2.1). Transcript assembly and abundance estimation were performed with StringTie (v2.1.5). Differentially expressed genes (DEGs) were identified using the edgeR package (v4.0.16) applying a generalized linear model (GLM), with significance thresholds set at |log₂(fold change)| \u0026ge; 1.5 and FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eProteomics\u003c/strong\u003e\u003cp\u003eProteins were extracted from tissues, quantified using a BCA assay (Thermo Scientific, 23225), and digested with trypsin following standard protocols (reduction with DTT, alkylation with IAA). Resulting peptides were labeled using TMT reagents (Thermo Scientific). Labeled samples were pooled and fractionated by high-pH reverse-phase chromatography using a RIGOL L3000 HPLC system with a Waters BEH C18 column. LC-MS/MS analysis was performed on an EASY-nLC 1200 UHPLC system coupled to a Q Exactive HF-X mass spectrometer in data-dependent acquisition (DDA) mode. Raw data were searched against the UniProt human protein database using Proteome Discoverer software (v2.2, Thermo Scientific) with specified search parameters. Protein identification was filtered at 1% FDR at the peptide-spectrum match (PSM) and protein levels. Differential protein expression analysis was performed using the limma package in R, with DEPs defined by |log₂(fold change)| \u0026ge; 1 and FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eBioinformatic Analysis\u003c/h2\u003e\u003cp\u003eFunctional enrichment analysis, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, was performed for the identified DMGs, DEGs, and DEPs using the DAVID bioinformatics resource, with an FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered significant. Protein-protein interaction (PPI) networks were constructed using the STRING database (minimum interaction score\u0026thinsp;\u0026gt;\u0026thinsp;0.7) and visualized using Cytoscape software (v3.6.1)[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Hub genes within the networks were identified using the cytoHubba plugin, selecting the top 10 nodes by connectivity[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMolecular Biology Experiments\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eRT-qPCR\u003c/strong\u003e\u003cp\u003eTotal RNA was extracted using the TransZol Up Plus RNA Kit (TransGen, China). cDNA was synthesized from DNase I-treated RNA using the PrimeScript RT reagent Kit (TaKaRa, Japan). Quantitative PCR was performed using SYBR Premix Ex Taq II (TaKaRa, Japan) on a QuantStudio 7 Flex system (Applied Biosystems, USA). Relative mRNA expression levels were calculated using the 2^(-ΔΔCT) method with β-actin as the endogenous control. Primer specificity was confirmed by BLAST analysis.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eWestern Blotting\u003c/b\u003e: Proteins were extracted using RIPA lysis buffer containing PMSF, quantified by BCA assay, separated by SDS-PAGE, and transferred to PVDF membranes. After blocking, membranes were incubated overnight at 4 ℃ with primary antibodies against PHYHD1 (abcam, ab181232; 1:1000) and β-Actin (Proteintech, 66009-1-Ig; 1:5000), followed by incubation with HRP-conjugated secondary antibodies. Signals were detected using ECL substrate and analyzed with ImageJ software, normalized to β-Actin.\u003c/p\u003e\u003cp\u003e\u003cb\u003eIHC\u003c/b\u003e: Paraffin-embedded sections underwent antigen retrieval, peroxidase blocking, and blocking with normal serum before incubation with anti-PHYHD1 antibody (1:200) overnight at 4\u0026deg;C. Staining was developed using DAB after incubation with an HRP-polymer secondary antibody, followed by hematoxylin counterstaining. Staining intensity and the percentage of positive cells were scored independently by two blinded pathologists[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eCell Function Assays\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eCell Transfection\u003c/strong\u003e\u003cp\u003eTo modulate PHYHD1 expression, transfection was performed using Lipofectamine\u0026trade; 3000 reagent (Invitrogen, USA) according to the manufacturer's protocol. The pRP[CMV]-3xFLAG hPHYHD1 overe-xpression plasmid was transfected into MHCC97-H cell lines, while SMMC-7721 cells were transfected with specific short hairpin RNA (shRNA) targeting PHYHD1. Control groups included cells transfected with a blank vector or a scrambled negative shRNA. Cells were harvested 48 hours post-transfection, and transfection efficiency was confirmed by Western blot analysis.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCell Proliferation Assay\u003c/strong\u003e\u003cp\u003eCell proliferation was assessed using the CCK-8 assay. Cells from each group were seeded into 96-well plates at a density of 3\u0026times;10\u0026sup3; cells per well (n\u0026thinsp;=\u0026thinsp;5 replicates per group). At 0, 24, 48, and 72 hours post-transfection, 10 \u0026micro;L of CCK-8 reagent (Dojindo, Japan) was added to each well, followed by incubation at 37 ℃ for 2 hours. The absorbance at 450 nm was measured using a microplate reader (BioTek, USA), and cell growth curves were generated based on the optical density (OD) values.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eColony Formation Assay\u003c/strong\u003e\u003cp\u003eStably transfected cells were plated in 6-well plates at a low density of 500 cells per well and cultured under standard conditions (37 ℃, 5% CO₂) for 14 days, with the medium refreshed every three days. After the incubation period, cells were gently washed with PBS, fixed with 4% paraformaldehyde for 15 minutes, and stained with 0.1% crystal violet for 20 minutes. The plates were then rinsed under running water and air-dried at room temperature. Colonies containing more than 50 cells were counted after scanning the images. The experiment was independently repeated three times.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCell Apoptosis Analysis by Flow Cytometry\u003c/strong\u003e\u003cp\u003eApoptosis was evaluated 48 hours after transfection using an Annexin V-APC/7-AAD Apoptosis Detection Kit (KeyGEN BioTECH, China) according to the manufacturer's instructions. Briefly, harvested cells were stained and analyzed on a BD FACS Celesta flow cytometer (BD, USA). Data processing was performed using FlowJo software (v10.6.2). The total apoptosis rate was defined as the sum of early apoptotic (Annexin V-APC⁺/7-AAD⁻) and late apoptotic (Annexin V-APC⁺/7-AAD⁺) cell populations.\u003c/p\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eAll experiments were independently repeated at least three times. Data are presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error of the mean (SEM). Comparisons between two groups were performed using Student's t-test (paired or unpaired, as appropriate), while one-way analysis of variance (ANOVA) followed by Tukey's post-hoc test was used for multi-group comparisons. Survival curves were generated using the Kaplan-Meier method, and differences were assessed with the log-rank test. Correlations between variables were analyzed using Pearson's correlation coefficient. A p-value of less than 0.05 was considered statistically significant. All statistical analyses were conducted using GraphPad Prism (version 9.0) and R software (version 4.3.0).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eMulti-omics Profiling Reveals Extensive Epigenetic and Expression Alterations in HCC\u003c/h2\u003e\n \u003cp\u003eWe performed an integrated multi-omics analysis on 5 paired HCC and adjacent non-tumor tissues using whole-genome bisulfite sequencing (WGBS), RNA sequencing (RNA-seq), and TMT-based quantitative proteomics. WGBS data revealed a global hypomethylation trend in HCC tissues (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA). We identified 97,523 differentially methylated regions (DMRs), which were predominantly enriched in intronic regions (27.82%), CpG islands (16.60%), and CpG island shores (14.97%) (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB), corresponding to 13,384 differentially methylated genes (DMGs). Transcriptome analysis identified 875 differentially expressed genes (DEGs; 547 upregulated, 328 downregulated) (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eC). Proteomic profiling revealed 225 differentially expressed proteins (DEPs; 53 upregulated, 172 downregulated) (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eD). These results demonstrate widespread dysregulation at the epigenetic, transcriptomic, and proteomic levels in HCC.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eIdentification of Core Methylation-Regulated Genes through Multi-omics Integration\u003c/h2\u003e\n \u003cp\u003eIntegration of methylome and transcriptome data identified 268 differentially methylated and expressed genes (DMEGs), comprising 80 hypermethylated and downregulated genes and 188 hypomethylated and upregulated genes (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eE). Functional enrichment analysis indicated their involvement in extracellular matrix organization and metabolic processes (Fig. \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003eA, S1B). Protein-protein interaction (PPI) network analysis highlighted key hub genes among these DMEGs (Fig. \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003eC, S1D). Further integration of transcriptome and proteome data identified 74 genes consistently differentially expressed at both mRNA and protein levels (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eF), which were significantly enriched in metabolic pathways (Fig. \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003eE-S1H). Cross-analysis of all three omics layers pinpointed 19 genes with concordant alterations in methylation, mRNA, and protein levels (13 downregulated, 6 upregulated) (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eF). These multi-omics core genes showed significant enrichment in metabolic pathways, including retinol and tyrosine metabolism (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eI, \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eJ).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eCandidate Gene Selection\u003c/h2\u003e\n \u003cp\u003eWe intersected the 19 multi-omics core genes with 338 methylation-driven genes identified from TCGA analysis[\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e], which prioritized TAT, SFN, and PHYHD1 for further investigation (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA). Analysis of TCGA data confirmed a hypermethylated/low-expression pattern for TAT and PHYHD1, and a hypomethylated/high-expression pattern for SFN in HCC (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB, \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC). While TAT and SFN have established roles in HCC[\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]. the function of PHYHD1 remains unclear. A significant negative correlation was observed between PHYHD1 methylation and its expression (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD), leading us to focus subsequent studies on PHYHD1.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ePHYHD1\u003c/strong\u003e \u003cstrong\u003eis Hypermethylated and Downregulated in HCC\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eValidation using independent datasets confirmed higher methylation levels (GSE136319, GSE136583) and lower mRNA expression (GSE112790, GSE121248) of \u003cem\u003ePHYHD1\u003c/em\u003e in HCC tissues compared to non-tumor tissues (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eE, \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eF). Consistent with this, CPTAC data showed significantly lower \u003cem\u003ePHYHD1\u003c/em\u003e protein levels in HCC (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eG). Immunohistochemistry (IHC) revealed that \u003cem\u003ePHYHD1\u003c/em\u003e protein was localized in both the cytoplasm and nucleus, and its expression was significantly reduced in HCC tissues (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eH, \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eI). At the cellular level, RT-qPCR showed higher \u003cem\u003ePHYHD1\u003c/em\u003e expression in LX-2 hepatic stellate cells than in several HCC cell lines (SMMC-7721, MHCC97-H, Huh-7) (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eJ). In a DEN/CCl₄-induced mouse HCC model, Phyhd1 mRNA and protein levels were significantly lower in tumor tissues compared to adjacent non-tumor liver tissues (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eK-N). These consistent findings across models and platforms establish \u003cem\u003ePHYHD1\u003c/em\u003e as a hyper-methylated and down-regulated gene in HCC.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eModulation of\u003c/strong\u003e \u003cstrong\u003ePHYHD1\u003c/strong\u003e \u003cstrong\u003eExpression Does Not Affect HCC Cell Proliferation or Apoptosis\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eTo investigate the functional role of \u003cem\u003ePHYHD1\u003c/em\u003e, we over-expressed it in MHCC97-H cells (which have low endogenous expression) and knocked it down in SMMC-7721 cells. CCK-8 assays showed that altering \u003cem\u003ePHYHD1\u003c/em\u003e expression did not significantly affect cell proliferation (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA, \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB). Similarly, colony formation assays revealed no changes in clonogenic ability upon \u003cem\u003ePHYHD1\u003c/em\u003e over-expression or knockdown (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC, \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eD). Flow cytometry analysis of apoptosis indicated that the rates of early and late apoptosis remained unaltered (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eE, \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eF). These in vitro results suggest that \u003cem\u003ePHYHD1\u003c/em\u003e does not directly regulate HCC cell proliferation or apoptosis under the tested conditions.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003ePhyhd1 Knockout Does Not Influence HCC Development in Mice\u003c/h2\u003e\n \u003cp\u003eTo assess the role of \u003cem\u003ePHYHD1\u003c/em\u003e in vivo, we subjected \u003cem\u003ePhyhd1\u003c/em\u003e\u003csup\u003e\u003cem\u003e-/-\u003c/em\u003e\u003c/sup\u003e mice and wild-type (WT) controls to a DEN/CCl₄-induced HCC carcinogenesis model. Compared to WT mice, \u003cem\u003ePhyhd1\u003c/em\u003e\u003csup\u003e\u003cem\u003e-/-\u003c/em\u003e\u003c/sup\u003e mice showed no significant differences in tumor number, maximum tumor diameter, or liver-to-body weight ratio (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eG-J). These in vivo findings are consistent with the in vitro results, indicating that the loss of \u003cem\u003ePhyhd1\u003c/em\u003e does not significantly impact HCC development in this model.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study identified DNA methylation-driven genes in hepatocellular carcinoma (HCC) by integrating data from WGBS, RNA-seq, and proteomics. We successfully pinpointed 19 candidate genes consistently dysregulated across the epigenetic, transcriptional, and translational levels, and selected \u003cem\u003ePHYHD1\u003c/em\u003e for in-depth expression validation and functional exploration. To our knowledge, this is the first study to confirm that \u003cem\u003ePHYHD1\u003c/em\u003e is hyper-methylated and down-regulated in HCC. Surprisingly, however, altering \u003cem\u003ePHYHD1\u003c/em\u003e expression did not significantly affect HCC cell proliferation, apoptosis, or in vivo tumorigenicity in our experiments. This seemingly contradictory finding prompts a reevaluation of the complex role of epigenetic silencing events in HCC initiation and progression.\u003c/p\u003e\u003cp\u003eThe integrated multi-omics strategy employed here effectively overcomes the limited coverage of traditional methylation microarrays. WGBS provides a genome-wide methylation profile at single-base resolution, and integrating this with transcriptome and proteome data significantly improves the accuracy of identifying driver events. Consistent with previous reports[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e],we observed a genome-wide hypo-methylation pattern in HCC tissues. Among our 19 candidate genes, several are well-established HCC-related genes. For instance, \u003cem\u003eSFN\u003c/em\u003e promotes the malignant progression of HCC cells by enhancing AKT phosphatase activity through disrupting the PHLPP2-AKT interaction[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], while \u003cem\u003eTAT\u003c/em\u003e induces mitochondria-dependent apoptosis in HCC cells by promoting cytochrome C release, thereby activating caspase-9 and PARP[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The identification of these known genes strongly validates the reliability of our screening approach, justifying our focus on the previously understudied gene, \u003cem\u003ePHYHD1\u003c/em\u003e.\u003c/p\u003e\u003cp\u003e\u003cem\u003ePHYHD1\u003c/em\u003e encodes a structural analog of phytanoyl-CoA hydroxylase (PHYH/PAHX). This enzyme acts on fatty acids, localizes to peroxisomes, and is mutated in certain neurological disorders[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Genomic variations in \u003cem\u003ePHYHD1\u003c/em\u003e are associated with altered levels of nucleotide metabolites, including guanine, 5-methylcytosine, and 5-methyluracil[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In various cell lines, endogenous and recombinant \u003cem\u003ePHYHD1\u003c/em\u003e are localized in both the nucleus and cytoplasm. In contrast, its closest homolog, the 2-oxoglutarate-dependent dioxygenase (2OGDD) PHYH, is peroxisomal[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Cells lacking \u003cem\u003ePHYHD1\u003c/em\u003e also exhibit reduced efficiency in utilizing glucose and maltose. Pathway analysis links \u003cem\u003ePHYHD1\u003c/em\u003e to cell division and RNA metabolism proteins (particularly those involved in mRNA splicing), and suggests potential roles in mRNA transport and transcription[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Current research on PHYHD1's role in disease is limited: it has been associated with T cell differentiation and effector T cell function in mice[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e];altered methylation and transcription of \u003cem\u003ePHYHD1\u003c/em\u003e have been observed in non-functioning pituitary adenomas[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], and a genome-wide association study identified \u003cem\u003ePHYHD1\u003c/em\u003e as a novel genetic determinant of blood metabolites in chronic kidney disease[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. However, its role and mechanism in cancer, particularly in HCC initiation and progression, had not been reported prior to our study.\u003c/p\u003e\u003cp\u003eIn our validation, we consistently observed \u003cem\u003ePHYHD1\u003c/em\u003e hyper-methylation and down-regulation across multiple independent datasets, human tissues, and mouse models. Subsequent functional experiments, however, revealed that neither over-expression nor knockdown of \u003cem\u003ePHYHD1\u003c/em\u003e affected HCC cell proliferation, apoptosis, or in vivo tumorigenicity. This \"epigenetic silencing versus functional loss paradox\" can be explained in several ways. First, \u003cem\u003ePHYHD1\u003c/em\u003e hyper-methylation may be a \"passenger event\"\u0026mdash;a consequence of global epigenetic dysregulation in HCC\u0026mdash;rather than a direct driver of tumorigenesis. Second, \u003cem\u003ePHYHD1\u003c/em\u003e function may be highly context-dependent, with its role becoming critical only under specific conditions, such as metabolic stress or immune microenvironment interactions, which were not replicated in our standard experimental settings. Additionally, functional redundancy or compensatory mechanisms might offset the direct phenotypic impact of \u003cem\u003ePHYHD1\u003c/em\u003e loss.\u003c/p\u003e\u003cp\u003eThis study has several limitations. First, the initial WGBS cohort was small; although we validated our findings using multiple public datasets, a larger sample size would strengthen our conclusions. Second, the DEN/ CCl₄-induced mouse HCC model does not fully recapitulate the complex heterogeneity and immune microenvironment of human HCC. Most importantly, the specific molecular and biochemical functions of \u003cem\u003ePHYHD1\u003c/em\u003e remain unclear.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, this study identified a panel of potential methylation-driven genes in HCC through integrated multi-omics analysis and, for the first time, systematically validated PHYHD1's expression patterns and explored its function. Our results suggest that the frequent epigenetic silencing of \u003cem\u003ePHYHD1\u003c/em\u003e in HCC does not directly drive the malignant phenotype. Instead, \u003cem\u003ePHYHD1\u003c/em\u003e may act as a context-dependent gene that exerts effects under specific stress conditions or indirectly contributes to HCC progression by regulating the tumor microenvironment. This finding not only deepens our understanding of the complexity of epigenetic regulation in HCC but also highlights future research directions. Future studies should explore PHYHD1's specific roles in hepatic metabolic homeostasis and tumor microenvironment regulation using models that better mimic physiological and pathological conditions, such as systems involving metabolic disturbance, hypoxia, or cell co-culture.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003econsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was reviewed and approved by the Institutional Research Ethics Committee of Gannan Medical University (Ganzhou, China), with the approval numbers 2024495 for human research and 2024473 for animal research. Written informed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Key Project of Science and Technology of Jiangxi Provincial Department of Education (GJJ2401319).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors affirm that they have no conflicts of interest, whether financial or personal, that could have influenced the outcomes reported in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during the current study are available in the following public repositories: The Cancer Genome Atlas (TCGA)-LIHC, Gene Expression Omnibus (GEO) (accession numbers are provided within the manuscript), and the Clinical Proteomic Tumor Analysis Consortium (CPTAC). The raw sequencing data generated in this study are available as Supplementary Files in this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTianfu Guo: Conceptualization, Funding acquisition, Methodology, Formal analysis, Investigation, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing. Yafei Li and Tiansheng He: Data curation, Investigation, Methodology. Hui Luo: Investigation. Binhui Xie and Yuewen Liu: Resources, Investigation. Zhiping Liu: Conceptualization, Funding acquisition, Project administration, Resources, Supervision, Validation.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets analyzed during the current study are available in the following public repositories: The Cancer Genome Atlas (TCGA)-LIHC, Gene Expression Omnibus (GEO) (accession numbers are provided within the manuscript), and the Clinical Proteomic Tumor Analysis Consortium (CPTAC). The raw sequencing data generated in this study are available as Supplementary Files in this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A: Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. \u003cem\u003eCA Cancer J Clin\u003c/em\u003e 2024, 74(3):229\u0026ndash;263.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLlovet JM, Kelley RK, Villanueva A, Singal AG, Pikarsky E, Roayaie S, Lencioni R, Koike K, Zucman-Rossi J, Finn RSJNrDp: Hepatocellular carcinoma. \u003cem\u003eNat Rev Dis Primers\u003c/em\u003e 2021, 7(1):6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNagaraju GP, Dariya B, Kasa P, Peela S, El-Rayes BF: Epigenetics in hepatocellular carcinoma. \u003cem\u003eSemin Cancer Biol\u003c/em\u003e 2022, 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588(7836):135\u0026ndash;140.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJansen GA, Mihalik SJ, Watkins PA, Moser HW, Jakobs C, Denis S, Wanders RJA: Phytanoyl-CoA hydroxylase is present in human liver, located in peroxisomes, and deficient in Zellweger syndrome: direct, unequivocal evidence for the new, revised pathway of phytanic acid alpha-oxidation in humans. \u003cem\u003eBiochemical \u0026amp; Biophysical Research Communications\u003c/em\u003e 1996, 229(1):205\u0026ndash;210.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAla-Nisula T, Sah-Teli SK, V.-P. R, Dimova EY, Koivunen P: Human phytanoyl-CoA dioxygenase domain-containing 1 (PHYHD1) is a putative oxygen sensor associated with RNA and carbohydrate metabolism. \u003cem\u003eFEBS letters\u003c/em\u003e 2023, 597(12):1651\u0026ndash;1666.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFurusawa Y, Kubo T, Fukazawa T: Phyhd1, an XPhyH-like homologue, is induced in mouse T cells upon T cell stimulation. \u003cem\u003eBiochemical \u0026amp; Biophysical Research Communications\u003c/em\u003e 2016, 472(3):551\u0026ndash;556.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCheng S, Xie W, Miao Y, Guo J, Zhang Y: Identification of key genes in invasive clinically non-functioning pituitary adenoma by integrating analysis of DNA methylation and mRNA expression profiles. \u003cem\u003eJournal of Translational Medicine\u003c/em\u003e 2019, 17(1):407.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRhee EP, Surapaneni A, Zheng Z, Zhou L, Dutta D, Arking DE, Zhang J, Duong TV, Chatterjee N, Luo S: Trans-ethnic genome-wide association study of blood metabolites in the Chronic Renal Insufficiency Cohort (CRIC) study. \u003cem\u003eJ Kidney international\u003c/em\u003e 2022, 101(4):814\u0026ndash;823.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Hepatocellular Carcinoma, Multi-omics, DNA Methylation, PHYHD1","lastPublishedDoi":"10.21203/rs.3.rs-7776922/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7776922/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eHepatocellular carcinoma (HCC) is characterized by profound epigenetic dysregulation, particularly in DNA methylation. This study leverages integrated multi-omics to systematically identify key methylation-driven genes in HCC, with a subsequent focus on characterizing the expression patterns and biological functions of a previously understudied candidate,\u003cem\u003e PHYHD1\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe collected five pairs of HCC tissues and matched adjacent non-tumor counterparts for integrated multi-omics profiling, including whole-genome bisulfite sequencing (WGBS), RNA sequencing (RNA-seq), and TMT-based quantitative proteomics. Differentially methylated regions (DMRs), differentially expressed genes (DEGs), and differentially expressed proteins (DEPs) were identified. Bioinformatic analyses, including functional enrichment and protein-protein interaction (PPI) network construction, were used to prioritize candidate genes, which were further validated using public datasets (TCGA, GEO). Methylation status and expression of PHYHD1 were verified using RT-qPCR, Western blot, and immunohistochemistry (IHC) in cell lines, patient tissues, and a DEN/CCl₄-induced murine HCC model. Functional impact of \u003cem\u003ePHYHD1\u003c/em\u003e on HCC cell proliferation, apoptosis, and tumorigenicity was assessed via in vitro assays (CCK-8, colony formation, flow cytometry) and an in vivo \u003cem\u003ePhyhd1\u003c/em\u003eknockout mouse model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e WGBS revealed global hypo-methylation in HCC, identifying 97,523 DMRs. Integration of methylome, transcriptome, and proteome data yielded 19 genes with consistent differential status across all three levels. Functional annotation showed enrichment in metabolic pathways, including retinol, tyrosine, and pyruvate metabolism. Although \u003cem\u003ePHYHD1\u003c/em\u003ewas hyper-methylated and down-regulated at both mRNA and protein levels in HCC, its gain- or loss-of-function did not significantly affect cell proliferation, colony formation, apoptosis \u003cem\u003ein vitro\u003c/em\u003e, or liver tumorigenesis \u003cem\u003ein vivo\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Our integrated multi-omics approach identified a panel of potential DNA methylation-driven genes in HCC. \u003cem\u003ePHYHD1\u003c/em\u003ewas confirmed as an epigenetically silenced gene in HCC, but its manipulation did not alter classic malignant phenotypes, suggesting it may not act as a canonical driver gene. Its silencing may represent a passenger event or influence tumor progression through non-cell-autonomous mechanisms. The biological role of \u003cem\u003ePHYHD1\u003c/em\u003e warrants further investigation.\u003c/p\u003e","manuscriptTitle":"Discovery of DNA Methylation-Driven Genes in Hepatocellular Carcinoma via Multi-Omics Integration and Functional Role of PHYHD1","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-29 04:43:02","doi":"10.21203/rs.3.rs-7776922/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ac4201a2-d51f-4f0e-acad-7bbc530148b8","owner":[],"postedDate":"October 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-30T08:23:40+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-29 04:43:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7776922","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7776922","identity":"rs-7776922","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}