• C-reactive protein
• Cell-free DNA methylation
• Hepatocellular carcinoma
• Methylation risk score
• Methylation-based inflammation measures

• U54CA221205 Division of Cancer Prevention, National Cancer Institute
• RS-2024-00405650 National Research Foundation of Korea

• DNA methylation
• Gene expression
• Liver cancer
• Tumor-specific markers

• Carcinoma, Hepatocellular/genetics
• Carcinoma, Hepatocellular/metabolism
• Carcinoma, Hepatocellular/pathology
• DNA Methylation
• Liver Neoplasms/genetics
• Liver Neoplasms/metabolism
• Liver Neoplasms/pathology
• Humans
• CpG Islands
• Gene Expression Regulation, Neoplastic
• Epigenesis, Genetic
• Promoter Regions, Genetic
• Computer Simulation
• Liver/metabolism
• Liver/pathology

• BES-2017-079883 Ministerio de Ciencia e Innovación
• RYC2018-024475-1 Ministerio de Ciencia e Innovación
• PI19/00613 Instituto de Salud Carlos III (SCIII) cofinanced by 'Fondo Europeo de Desarrollo Regional' (FEDER) 'Una Manera de Hacer Europa'
• INVES223049AREC Fundación Científica Asociación Española Contra el Cáncer
• Instituto de Salud Carlos III (SCIII) cofinanced by ‘Fondo Europeo de Desarrollo Regional’ (FEDER) ‘Una Manera de Hacer Europa’
• Universidad de Navarra

• DPF2
• hepatocellular carcinoma (HCC)
• immune infiltration
• tumor prognosis

• cancer
• cancer genetics
• epigenetics

• Humans
• DNA Methylation
• Adenocarcinoma/diagnosis
• Adenocarcinoma/genetics
• Adenocarcinoma/pathology
• Pancreatic Neoplasms/genetics
• Biomarkers, Tumor/genetics
• Carcinoma, Hepatocellular/diagnosis
• Carcinoma, Hepatocellular/genetics
• Liver Neoplasms/diagnosis
• Liver Neoplasms/genetics
• Bile Duct Neoplasms/genetics
• Lung Neoplasms/genetics
• Bile Ducts, Intrahepatic/pathology
• Cell-Free Nucleic Acids

• P3-0054 Slovenian Research and Innovation Agency
• P3-0054 Slovenian Research and Innovation Agency

• hepatocellular carcinoma
• immunotherapy
• interventional oncology
• interventional radiology
• intrahepatic cholangiocarcinoma

• Humans
• Adenoma, Liver Cell/blood
• Adenoma, Liver Cell/genetics
• Adenoma, Liver Cell/metabolism
• alpha-Fetoproteins
• Biomarkers, Tumor/metabolism
• Carcinoma, Hepatocellular/blood
• Carcinoma, Hepatocellular/genetics
• Carcinoma, Hepatocellular/metabolism
• DNA
• Liver Neoplasms/blood
• Liver Neoplasms/genetics
• Liver Neoplasms/metabolism

• drug resistance
• hepatocellular carcinoma
• immune checkpoint inhibitor
• lenvatinib
• molecular target agent
• regorafenib
• sorafenib
• systemic therapy
• tumor microenvironment
• tyrosine kinase

• Humans
• Carcinoma, Hepatocellular/drug therapy
• Carcinoma, Hepatocellular/genetics
• Liver Neoplasms/drug therapy
• Liver Neoplasms/genetics
• Liver Neoplasms/pathology
• Tumor Microenvironment/genetics
• Epigenesis, Genetic
• Chemoembolization, Therapeutic

• Cellular reprogramming
• Cholangiocarcinoma
• Epigenetics
• Hepatocellular carcinoma
• Liver progenitor cell
• Transdifferentiation

• Humans
• Cell Plasticity
• DNA Methylation
• Cell Proliferation
• Liver/metabolism
• Liver Neoplasms/genetics

• P30 DK120531 NIDDK NIH HHS
• R01 CA258449 NCI NIH HHS

• Ki-67
• MCM6
• biomarkers
• genome-wide DNA methylation
• meningioma
• methylome
• proliferation signature
• survival

• hepatocellular carcinoma
• methylation
• thyroid hormones
• thyroid peroxidase
• tumor thrombosis

• Biomarkers
• DNA methylation
• Epidrugs
• Epigenetics
• Hepatocellular carcinoma
• Histone modification
• ncRNAs

• DNA methylation
• ElasticNet
• colorectal cancer
• gene expression
• immune system
• regression
• survival analysis

• 2021R1C1C1008307 National Research Foundation of Korea
• 2021R1A6A1A10044154 National Research Foundation of Korea

• BCL
• DLBCL
• Golden Retriever
• MIRA
• cancer
• canine
• lymphoma
• methylome
• sequencing

• Epidrugs
• Epigenetics
• HCC
• Histone modifications
• Methylation
• Non-coding RNAs

• Carcinogenesis/genetics
• Carcinoma, Hepatocellular/pathology
• Epigenesis, Genetic
• Humans
• Liver Neoplasms/pathology
• Prognosis

• Associazione Italiana per la Ricerca sul Cancro

The aims were to profile the DNA methylation in colorectal cancer (CRC) and to explore cancer-specific methylation biomarkers. Fifty-four pairs of CRCs and the adjacent normal tissues were subjected to Infinium Human Methylation 450K assay and analysed using ChAMP R package. A total of 26,093 differentially methylated probes were identified, which represent 6156 genes; 650 probes were hypermethylated, and 25,443 were hypomethylated. Hypermethylated sites were common in CpG islands, while hypomethylated sites were in open sea. Most of the hypermethylated genes were associated with pathways in cancer, while the hypomethylated genes were involved in the PI3K-AKT signalling pathway. Among the identified differentially methylated probes, we found evidence of four potential probes in CRCs versus adjacent normal; HOXA2 cg06786372, OPLAH cg17301223, cg15638338, and TRIM31 cg02583465 that could serve as a new biomarker in CRC since these probes were aberrantly methylated in CRC as well as involved in the progression of CRC. Furthermore, we revealed the potential of promoter methylation ADHFE1 cg18065361 in differentiating the CRC from normal colonic tissue from the integrated analysis. In conclusion, aberrant DNA methylation is significantly involved in CRC pathogenesis and is associated with gene silencing. This study reports several potential important methylated genes in CRC and, therefore, merit further validation as novel candidate biomarker genes in CRC.

• Allogeneic hematopoietic cell transplantation
• Chronic myelomonocytic leukemia
• PHF6
• Relapse
• TNF-α signaling pathway

• DNA methylation
• cancer disparities
• epigenomic and transcriptomic data
• hepatocellular carcinoma
• miRNA expression

• F30 CA250307 NCI NIH HHS
• R01 GM123766 NIGMS NIH HHS
• U01 CA185188 NCI NIH HHS

Gastrointestinal (GI) tract cancers are the most common malignant cancers with high mortality rate. Pan-cancer multi-omics data fusion provides a powerful strategy to examine commonalities and differences among various cancer types and benefits for the identification of pan-cancer drug targets. Herein, we conducted an integrative omics analysis on The Cancer Genome Atlas pan-GI samples including six carcinomas and stratified into 9 clusters, i.e. 5 single-type-dominant clusters and 4 mixed clusters, the clustering reveals the molecular features of different subtypes, other than the organ and cell-of-origin classifications. Especially the mixed clusters revealed the homogeneity of pan-GI cancers. We demonstrated that the prognosis differences among pan-GI subtypes based on multi-omics integration are more significant than clustering by single-omics. The potential prognostic markers for pan-GI stratification were identified by proportional hazards model, such as PSCA (for colorectal and stomach cancer) and PPP1CB (for liver and pancreatic cancer), which have prominent prognostic power supported by high concordance index.

•

Pan-cancer multi-omics strategy reveals homogeneity and heterogeneity of pan-GI cancers

• Biological sciences
• Cancer
• Genetics
• Genomics

For the identification of abnormally methylated differentially expressed genes (MDEGs) in hepatocellular carcinoma (HCC), this study integrated four microarray datasets to investigate the fundamental mechanisms of tumorigenesis.

We obtained the expression (GSE76427, GSE57957) and methylation (GSE89852, GSE54503) profiles from Gene Expression Omnibus (GEO). The abnormally MDEGs were identified by using R software. We used the clusterProfiler package for the functional and pathway enrichment analysis. The String database was used to build the protein–protein interaction (PPI) network and visualize it in Cytoscape. MCODE was employed in the module analysis. Additionally, Gene Expression Profiling Interactive Analysis (GEPIA) and The Cancer Genome Atlas (TCGA) were employed to validate results. Lastly, we used cBioPortal software to examine the hub genetic alterations.

We identified 162 hypermethylated, down-regulated genes and 190 hypomethylated, up-regulated genes. Up-regulated genes with low methylation were enriched in biological processes, such as keratinocyte proliferation, and calcium homeostasis. Pathway analysis was enriched in the AMPK and PI3K-Akt signaling pathways. The PPI network identified PTK2, VWF, and ITGA2 as hypomethylated, high-expressing hub genes. Down-regulated genes with high methylation were related to responses to peptide hormones and estradiol, multi-multicellular organism process. Pathway analysis indicated enrichment in camp, oxytocin signaling pathways. The PPI network identified CFTR, ESR1, and CXCL12 as hypermethylated, low-expressing hub genes. Upon verification in TCGA databases, we found that the expression and methylation statuses of the hub genes changed significantly, and it was consistent with our results.

The novel abnormally MDEGs and pathways in HCC were identified. These results helped us further understand the molecular mechanisms underlying HCC invasion, metastasis, and development. Hub genes can serve as biomarkers for an accurate diagnosis and treatment of HCC, and PTK2, VWF, ITGA2, CFTR, ESR1, and CXCL12 are included.

• bioinformatics analysis
• gene expression
• hepatocellular carcinoma
• hub genes
• methylation

• Bacteria/enzymology
• DNA Methylation
• Databases, Factual
• Drugs, Investigational/metabolism
• Enzymes/classification
• Enzymes/metabolism
• Fungi/enzymology
• Histones/genetics
• Histones/metabolism
• Humans
• Inactivation, Metabolic/genetics
• Internet
• Metabolic Clearance Rate
• Microbiota/genetics
• Prescription Drugs/metabolism
• Protein Processing, Post-Translational
• RNA, Long Noncoding/genetics
• RNA, Long Noncoding/metabolism
• Software
• Xenobiotics/metabolism

• National Key Research and Development Program of China
• National Natural Science Foundation of China
• Key R&D Program of Zhejiang Province
• Central Universities
• Zhejiang University
• China Knowledge Centre for Engineering Sciences and Technology

• DNA methylation
• Tumor purity
• cancer epigenetics
• differential methylation
• statistical modeling

• CpG Islands/genetics
• DNA Methylation/genetics
• Gene Expression Profiling
• Genetic Association Studies
• Humans
• Microarray Analysis

The methylome of open chromatins was investigated in colorectal cancer (CRC) to explore cancer-specific methylation and potential biomarkers. Epigenome-wide methylome of open chromatins was studied in colorectal cancer tissues using the Infinium DNA MethylationEPIC assay. Differentially methylated regions were identified using the ChAMP Bioconductor. Our stringent analysis led to the discovery of 2187 significant differentially methylated open chromatins in CRCs. More hypomethylated probes were observed and the trend was similar across all chromosomes. The majority of hyper- and hypomethylated probes in open chromatin were in chromosome 1. Our unsupervised hierarchical clustering analysis showed that 40 significant differentially methylated open chromatins were able to segregate CRC from normal colonic tissues. Receiver operating characteristic analyses from the top 40 probes revealed several significant, highly discriminative, specific and sensitive probes such as OPLAH cg26256223, EYA4 cg01328892, and CCNA1 cg11513637, among others. OPLAH cg26256223 hypermethylation is associated with reduced gene expression in the CRC. This study reports many open chromatin loci with novel differential methylation statuses, some of which with the potential as candidate markers for diagnostic purposes.

• DNA methylation
• Infinium MethylationEPIC
• colorectal cancer
• epigenetics aberrations
• open chromatin

• DNA methylation
• Gene expression
• Hepatitis B virus
• Hepatitis C virus
• Hepatocellular carcinoma

• Macaca fascicularis
• RNA-Seq (quantification) analysis
• differentially methylated CpG sites
• repeat elements
• whole genome bisulfite sequencing (WGBS)

• DNA methylation
• Epigenome
• HCC
• Histone methylation
• Liver cancer
• ncRNA

• Colorectal carcinoma
• DNMTs
• WGCNA
• dendritic cell
• neutrophil

• HCC
• HDAC
• epigenetics
• immunotherapeutics
• resminostat

• DNA methylation
• HCC
• epigenetics
• histone methylation
• metastasis
• miRNA

• Animals
• Biomarkers, Tumor/metabolism
• Carcinoma, Hepatocellular/genetics
• Carcinoma, Hepatocellular/pathology
• DNA Methylation/genetics
• Epigenesis, Genetic
• Humans
• Liver Neoplasms/genetics
• Liver Neoplasms/pathology
• MicroRNAs/genetics
• MicroRNAs/metabolism
• Neoplasm Metastasis

• Hepatocellular carcinoma
• epigenetics
• liver cirrhosis

Hepatocellular carcinoma (HCC) is the most common form of primary liver cancer with increases in new cases being reported annually. Histopathologists have identified hepatic steatosis as a characteristic of a broad range of chronic liver diseases that are associated with the onset and development of HCC. In this context, epigenetic modifications may serve as precancerous factors predisposing normal cells to the initiation of carcinogenesis. This study demonstrated that hepatic tumorigenesis and differentiated adipocytes may modulate both global histone deacetylase (HDAC) expression and specific class I HDAC genes in the tumour microenvironment. The novel class I HDAC inhibitor Resminostat was shown to reduce the proliferation of HCC cells along with its specificity in targeting class I HDACs and oncogenes. The combined effect of Resminostat with several pharmaceutical agents such as Sorafenib, Cisplatin and Doxorubicin was also demonstrated. The inhibition of heat shock protein 90 (HSP90) has been demonstrated as a potential therapeutic option for HCC. In line with this, the specific HSP90 inhibitor 17-(allylamino)-17-demethoxygeldanamycin (17-AAG) was selected and it was found that the combination of Resminostat and 17-AAG may provide a “smart” clinical strategy for HCC patients by targeting cellular communication within the tumour microenvironment. This study provides an insight into the use of Resminostat as an epigenetic based therapeutic for HCC along with other pharmaceutical options, in particular by targeting the cell-to-cell communication that occurs between hepatoma and adipocytes.

• ER stress/UPR
• HCC
• HDAC inhibitor resminostat
• HSP90 inhibitor 17-AAG
• adipocytes

• DNA methylation
• epigenetics
• genome-wide
• hepatocellular carcinoma
• stochastic epigenetic mutation

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer mortality, accounting for almost 90% of total liver cancer burden. Surgical resection followed by adjuvant and systemic chemotherapy are the most meticulously followed treatment procedures but the complex etiology and high metastatic potential of the disease renders surgical treatment futile in majority of the cases. Another hindrance to the scenario is the acquired resistance to drugs resulting in relapse of the disease. Hence, to provide insights into development of novel therapeutic targets and diagnostic biomarkers, this review focuses on the various molecular mechanisms underlying chemoresistance in HCC. We have provided a comprehensive summary of the various strategies adopted by HCC cells, extending from apoptosis evasion, autophagy activation, drug expulsion to epigenetic transformation as modes of therapy resistance. The role of stem cells in imparting chemoresistance is also discussed. Furthermore, the review also focuses on how this knowledge might be exploited for the development of an effective, prospective therapy against HCC.

• Apoptosis
• Autophagy
• Cancer
• Chemoresistance
• HCC
• Metastasis

• hepatocellular carcinoma
• multi-omics
• deep learning
• survival

• Algorithms
• Biomarkers, Tumor
• Computational Biology/methods
• Deep Learning
• Gene Expression Profiling
• Gene Regulatory Networks
• Genomics/methods
• Humans
• Liver Neoplasms/genetics
• Liver Neoplasms/metabolism
• Liver Neoplasms/mortality
• Liver Neoplasms/pathology
• Metabolomics/methods
• Prognosis
• Proportional Hazards Models
• Proteomics/methods
• Reproducibility of Results
• Transcriptome

• R01 LM012373 NLM NIH HHS
• P30 GM131944 NIGMS NIH HHS
• R01 HD084633 NICHD NIH HHS
• P20 GM103457 NIGMS NIH HHS
• K01 ES025434 NIEHS NIH HHS

• Antigens, Neoplasm/genetics
• Antigens, Neoplasm/metabolism
• Carcinoma, Hepatocellular/epidemiology
• Carcinoma, Hepatocellular/metabolism
• Carcinoma, Hepatocellular/virology
• Cell Adhesion Molecules/genetics
• Cell Adhesion Molecules/metabolism
• Claudin-1/genetics
• Claudin-1/metabolism
• Down-Regulation
• Hepacivirus/pathogenicity
• Hepatitis C/complications
• Hepatitis C/metabolism
• Hepatitis C/virology
• Hepatocytes/metabolism
• Hepatocytes/pathology
• Hepatocytes/virology
• Humans
• Liver Neoplasms/epidemiology
• Liver Neoplasms/metabolism
• Liver Neoplasms/virology
• Occludin/genetics
• Occludin/metabolism
• Virus Internalization
• Virus Replication

Chronic Hepatitis B Virus (HBV) infection is linked to hepatocellular carcinoma (HCC) pathogenesis. Despite the availability of a HBV vaccine, current treatments for HCC are inadequate. Globally, 257 million people are chronic HBV carriers, and children born from HBV-infected mothers become chronic carriers, destined to develop liver cancer. Thus, new therapeutic approaches are needed to target essential pathways involved in HCC pathogenesis. Accumulating evidence supports existence of hepatic cancer stem cells (hCSCs), which contribute to chemotherapy resistance and cancer recurrence after treatment or surgery. Understanding how hCSCs form will enable development of therapeutic strategies to prevent their formation. Recent studies have identified an epigenetic mechanism involving the downregulation of the chromatin modifying Polycomb Repressive Complex 2 (PRC2) during HBV infection, which results in re-expression of hCSC marker genes in infected hepatocytes and HBV-associated liver tumors. However, the genesis of hCSCs requires, in addition to the expression of hCSC markers cellular changes, rewiring of metabolism, cell survival, escape from programmed cell death, and immune evasion. How these changes occur in chronically HBV-infected hepatocytes is not yet understood. In this review, we will present the basics about HBV infection and hepatocarcinogenesis. Next, we will discuss studies describing the mutational landscape of liver cancers and how epigenetic mechanisms likely orchestrate cellular reprograming of hepatocytes to enable formation of hCSCs.

• Epigenetics
• Epithelial Cell Adhesion Molecule (EpCAM)
• Hepatitis B Virus (HBV)
• Hepatocellular Carcinoma (HCC)
• Hox transcript antisense RNA (HOTAIR)
• Polycomb Repressive Complex 2 (PRC2)
• hepatic Cancer Stem Cells (hCSCs)
• p68/DDX5 RNA helicase
• pluripotency genes

• P30 CA023168 NCI NIH HHS
• R01 DK044533 NIDDK NIH HHS

DNA methylation is an epigenetic mechanism in the pathogenesis of hepatocellular carcinoma (HCC). Here, we conducted a systematic meta-analysis to evaluate the contribution of DNA methylation to the risk of HCC. A total of 2109 publications were initially retrieved from PubMed, Web of Science, Cochrane Library, Embase, CNKI and Wanfang literature database. After a four-step filtration, we harvested 144 case-control articles in the meta-analysis. Our results revealed that 24 genes (carcinoma tissues vs adjacent tissues), 17 genes (carcinoma tissues vs normal tissues) and six genes (carcinoma serums vs normal serums) were significantly hypermethylated in HCC. Subgroup meta-analysis by geographical populations showed that six genes (carcinoma tissues vs adjacent tissues) and four genes (carcinoma tissues vs normal tissues) were significantly hypermethylated in HCC. Our meta-analysis identified the correlations between a number of aberrant methylated genes (p16, RASSF1A, GSTP1, p14, CDH1, APC, RUNX3, SOCS1, p15, MGMT, SFRP1, WIF1, PRDM2, DAPK1, RARβ, hMLH1, p73, DLC1, p53, SPINT2, OPCML and WT1) and HCC. Aberrant DNA methylation might become useful biomarkers for the prediction and diagnosis of HCC.

• DNA methylation
• biomarker
• hepatocellular carcinoma
• meta-analysis

The mechanisms leading to the development and progression of hepatocellular carcinoma (HCC) are complicated and regulated genetically and epigenetically. The recent advancement in high-throughput sequencing has facilitated investigations into the role of genetic and epigenetic regulations in hepatocarcinogenesis. Therefore, we used systems biology and big database mining to construct genetic and epigenetic networks (GENs) using the information about mRNA, miRNA, and methylation profiles of HCC patients. Our approach involves analyzing gene regulatory networks (GRNs), protein-protein networks (PPINs), and epigenetic networks at different stages of hepatocarcinogenesis. The core GENs, influencing each stage of HCC, were extracted via principal network projection (PNP). The pathways during different stages of HCC were compared. We observed that extracellular signals were further transduced to transcription factors (TFs), resulting in the aberrant regulation of their target genes, in turn inducing mechanisms that are responsible for HCC progression, including cell proliferation, anti-apoptosis, aberrant cell cycle, cell survival, and metastasis. We also selected potential multiple drugs specific to prominent epigenetic network markers of each stage of HCC: lestaurtinib, dinaciclib, and perifosine against the NTRK2, MYC, and AKT1 markers influencing HCC progression from stage I to stage II; celecoxib, axitinib, and vinblastine against the DDIT3, PDGFB, and JUN markers influencing HCC progression from stage II to stage III; and atiprimod, celastrol, and bortezomib against STAT3, IL1B, and NFKB1 markers influencing HCC progression from stage III to stage IV.

• DNA methylation
• hepatocarcinogenesis
• miRNAs
• multiple potential drugs
• principal network projection

To identify the key epigenetically modulated genes and pathways in HCC by performing an integrative meta-analysis of all major, well-annotated and publicly available methylation datasets using tools of network analysis.

PubMed and Gene Expression Omnibus were searched for genome-wide DNA methylation datasets. Patient clinical and demographic characteristics were obtained. DNA methylation data were integrated using the Ingenuity Pathway Analysis, a software package for visualizing and analyzing biological networks. Pathway enrichment analysis was performed using IPA, which also provides literature-driven and computationally-predicted annotations for significant association of genes to curated molecular pathways.

From an initial 928 potential abstracts, we identified and analyzed 11 eligible high-throughput methylation datasets representing 354 patients. A significant proportion of studies did not provide concomitant clinical data. In the promoter region, HIST1H2AJ and SPDYA were the most commonly methylated, whereas HRNBP3 gene was the most commonly hypomethylated. ESR1 and ERK were central genes in the principal networks. The pathways most associated with the frequently methylated genes were G-protein coupled receptor and cAMP-mediated signalling.

Using an integrative network-based analysis approach of genome-wide DNA methylation data of both the promoter and body of genes, we identified G-protein coupled receptor signalling as the most highly associated with HCC. This encompasses a diverse range of cancer pathways, such as the PI3K/Akt/mTOR and Ras/Raf/MAPK pathways, and is therefore supportive of previous literature on gene expression in HCC. However, there are novel targetable genes such as HIST1H2AJ that are epigenetically modified, suggesting their potential as biomarkers and for therapeutic targeting of the HCC epigenome.

• Network analysis
• Hepatocellular carcinoma
• Methylation

• Long noncoding RNAs
• cancer
• hepatocellular carcinoma
• microRNAs

• Adult
• Aged
• Area Under Curve
• Biomarkers, Tumor/genetics
• Carcinoma, Hepatocellular/blood
• Carcinoma, Hepatocellular/genetics
• Carcinoma, Hepatocellular/pathology
• Case-Control Studies
• Cyclin-Dependent Kinase Inhibitor p16
• Cyclin-Dependent Kinase Inhibitor p18/genetics
• DNA Methylation
• Female
• Humans
• Kruppel-Like Transcription Factors/genetics
• Liver Neoplasms/blood
• Liver Neoplasms/genetics
• Liver Neoplasms/pathology
• Male
• Membrane Proteins/genetics
• Middle Aged
• Oxidoreductases/genetics
• Risk Factors
• T-Box Domain Proteins/genetics
• Tumor Suppressor Proteins/genetics

• P30 CA013696 NCI NIH HHS
• P30 ES009089 NIEHS NIH HHS
• R01 ES005116 NIEHS NIH HHS