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Integrative analysis of DNA methylation and microRNA expression reveals mechanisms of disparity in hepatocellular carcinoma

ABSTRACT: This SuperSeries is composed of the SubSeries listed below.

ORGANISM(S): Homo sapiens

PROVIDER: GSE176289 | GEO | 2021/09/29

REPOSITORIES: GEO

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Integrative analysis of DNA methylation and microRNA expression reveals mechanisms of disparity in hepatocellular carcinoma [DNA-Methylation]

Project description:We evaluate the epigenetic mechanisms of racial disparity in HCC through an integrated analysis of DNA methylation, miRNA, and combined regulation of gene expression. Specifically, we acquired DNA methylation, mRNA-seq, and miRNA-seq data through the analysis of tumor and adjacent normal liver tissues from African Americans (AA) and European Americans (EA) with HCC. Results: Using mixed ANOVA, we identified cytosine-phosphate-guanine (CpG) sites, mRNAs, and miRNAs that are statistically significantly altered in HCC vs. adjacent normal in a race-specific manner. Through integrative analysis, we identified significantly differentially expressed genes in HCC with disparate epigenetic regulation, associated with changes in miRNA expression for AA and DNA methylation for EA.

2021-09-29 | GSE176273 | GEO

Integrative analysis of DNA methylation and microRNA expression reveals mechanisms of disparity in hepatocellular carcinoma [miRNA-Seq]

2021-09-29 | GSE176288 | GEO

Integrative analysis of DNA methylation and microRNA expression reveals mechanisms of disparity in hepatocellular carcinoma [RNA-Seq]

2021-09-29 | GSE176271 | GEO

Integrative Analysis of DNA Methylation and microRNA Expression Reveals Mechanisms of Racial Heterogeneity in Hepatocellular Carcinoma.

Project description:Pathologic alterations in epigenetic regulation have long been considered a hallmark of many cancers, including hepatocellular carcinoma (HCC). In a healthy individual, the relationship between DNA methylation and microRNA (miRNA) expression maintains a fine balance; however, disruptions in this harmony can aid in the genesis of cancer or the propagation of existing cancers. The balance between DNA methylation and microRNA expression and its potential disturbance in HCC can vary by race. There is emerging evidence linking epigenetic events including DNA methylation and miRNA expression to cancer disparities. In this paper, we evaluate the epigenetic mechanisms of racial heterogenity in HCC through an integrated analysis of DNA methylation, miRNA, and combined regulation of gene expression. Specifically, we generated DNA methylation, mRNA-seq, and miRNA-seq data through the analysis of tumor and adjacent non-tumor liver tissues from African Americans (AA) and European Americans (EA) with HCC. Using mixed ANOVA, we identified cytosine-phosphate-guanine (CpG) sites, mRNAs, and miRNAs that are significantly altered in HCC vs. adjacent non-tumor tissue in a race-specific manner. We observed that the methylome was drastically changed in EA with a significantly larger number of differentially methylated and differentially expressed genes than in AA. On the other hand, the miRNA expression was altered to a larger extent in AA than in EA. Pathway analysis functionally linked epigenetic regulation in EA to processes involved in immune cell maturation, inflammation, and vascular remodeling. In contrast, cellular proliferation, metabolism, and growth pathways are found to predominate in AA as a result of this epigenetic analysis. Furthermore, through integrative analysis, we identified significantly differentially expressed genes in HCC with disparate epigenetic regulation, associated with changes in miRNA expression for AA and DNA methylation for EA.

| S-EPMC8453167 | biostudies-literature

Integrative analysis of DNA methylation and microRNA expression reveals mechanisms of disparity in hepatocellular carcinoma [DNA-Methylation]

Project description:Integrative analysis of DNA methylation and microRNA expression reveals mechanisms of disparity in hepatocellular carcinoma [DNA-Methylation]

| PRJNA735701 | ENA

Integrative analysis of DNA methylation and microRNA expression reveals mechanisms of disparity in hepatocellular carcinoma

Project description:Integrative analysis of DNA methylation and microRNA expression reveals mechanisms of disparity in hepatocellular carcinoma

| PRJNA735726 | ENA

Integrative analysis of DNA methylation and microRNA expression reveals mechanisms of disparity in hepatocellular carcinoma [RNA-Seq]

Project description:Integrative analysis of DNA methylation and microRNA expression reveals mechanisms of disparity in hepatocellular carcinoma [RNA-Seq]

| PRJNA735698 | ENA

Integrative analysis of DNA methylation and microRNA expression reveals mechanisms of disparity in hepatocellular carcinoma [miRNA-Seq]

Project description:Integrative analysis of DNA methylation and microRNA expression reveals mechanisms of disparity in hepatocellular carcinoma [miRNA-Seq]

| PRJNA735728 | ENA

Integrative analysis of DNA methylation and gene expression reveals hepatocellular carcinoma-specific diagnostic biomarkers.

Project description:BACKGROUND:Hepatocellular carcinoma (HCC) is the one of the most common cancers and lethal diseases in the world. DNA methylation alteration is frequently observed in HCC and may play important roles in carcinogenesis and diagnosis. METHODS:Using the TCGA HCC dataset, we classified HCC patients into different methylation subtypes, identified differentially methylated and expressed genes, and analyzed cis- and trans-regulation of DNA methylation and gene expression. To find potential diagnostic biomarkers for HCC, we screened HCC-specific CpGs by comparing the methylation profiles of 375 samples from HCC patients, 50 normal liver samples, 184 normal blood samples, and 3780 samples from patients with other cancers. A logistic regression model was constructed to distinguish HCC patients from normal controls. Model performance was evaluated using three independent datasets (including 327 HCC samples and 122 normal samples) and ten newly collected biopsies. RESULTS:We identified a group of patients with a CpG island methylator phenotype (CIMP) and found that the overall survival of CIMP patients was poorer than that of non-CIMP patients. Our analyses showed that the cis-regulation of DNA methylation and gene expression was dominated by the negative correlation, while the trans-regulation was more complex. More importantly, we identified six HCC-specific hypermethylated sites as potential diagnostic biomarkers. The combination of six sites achieved ~?92% sensitivity in predicting HCC, ~?98% specificity in excluding normal livers, and ~?98% specificity in excluding other cancers. Compared with previously published methylation markers, our markers are the only ones that can distinguish HCC from other cancers. CONCLUSIONS:Overall, our study systematically describes the DNA methylation characteristics of HCC and provides promising biomarkers for the diagnosis of HCC.

| S-EPMC5977535 | biostudies-literature

Integrative analysis of DNA methylation and gene expression reveals distinct hepatocellular carcinoma subtypes with therapeutic implications.

Project description:We aimed to develop an HCC classification model based on the integrated gene expression and methylation data of methylation-driven genes. Genome, methylome, transcriptome, proteomics and clinical data of 369 HCC patients from The Cancer Genome Atlas Network were retrieved and analyzed. Consensus clustering of the integrated gene expression and methylation data from methylation-driven genes identified 4 HCC subclasses with significant prognosis difference. HS1 was well differentiated with a favorable prognosis. HS2 had high serum α-fetoprotein level that was correlated with its poor outcome. High percentage of CTNNB1 mutations corresponded with its activation in WNT signaling pathway. HS3 was well differentiated with low serum α-fetoprotein level and enriched in metabolism signatures, but was barely involved in immune signatures. HS3 also had high percentage of CTNNB1 mutations and therefore enriched in WNT activation signature. HS4 was poorly differentiated with the worst prognosis and enriched in immune-related signatures, but was barely involved in metabolism signatures. Subsequently, a prediction model was developed. The prediction model had high sensitivity and specificity in distributing potential HCC samples into groups identical with the training cohort. In conclusion, this work sheds light on HCC patient prognostication and prediction of response to targeted therapy.

| S-EPMC7138576 | biostudies-literature

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