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

ABSTRACT: 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 ?-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 ?-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.

SUBMITTER: Huang X

PROVIDER: S-EPMC7138576 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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

Huang Xiaowen X Yang Chen C Wang Jilin J Sun Tiantian T Xiong Hua H

Aging 20200322 6

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 favor ...[more]

PMID: 32201399

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Project description:IntroductionHepatocellular carcinoma (HCC) is an aggressive malignancy with steadily increasing incidence rates worldwide and poor therapeutic outcomes. Studies show that metabolic reprogramming plays a key role in tumor genesis and progression. In this study, we analyzed the metabolic heterogeneity of epithelial cells in the HCC and screened for potential biomarkers.MethodsThe hepatic single-cell RNA sequencing (scRNA-seq) datasets of HCC patients and healthy controls were obtained from the Gene Expression Omnibus (GEO) database. Based on data intergration and measurement of differences among groups, the metabolic epithelial cell subpopulations were identified. The single-cell metabolic pathway was analyzed and the myeloid subpopulations were identified. Cell-cell interaction analysis and single-cell proliferation analysis were performed. The gene expression profiles of HCC patients were obtained from the GSE14520 dataset of GEO and TCGA-LIHC cohort of the UCSC Xena website. Immune analysis was performed. The differentially expressed genes (DEGs) were identified and functionally annotated. Tumor tissues from HCC patients were probed with anti-ALDOA, anti-CD68, anti-CD163, anti-CD4 and anti-FOXP3 antibodies. Results We analyzed the scRNA-seq data from 48 HCC patients and 14 healthy controls. The epithelial cells were significantly enriched in HCC patients compared to the controls (p = 0.011). The epithelial cells from HCC patients were classified into two metabolism-related subpopulations (MRSs) - pertaining to amino acid metabolism (MRS1) and glycolysis (MRS2). Depending on the abundance of these metabolic subpopulations, the HCC patients were also classified into the MRS1 and MRS2 subtype distinct prognoses and immune infiltration. The MRS2 group had significantly worse clinical outcomes and more inflamed tumor microenvironment (TME), as well as a stronger crosstalk between MRS2 cells and immune subpopulations that resulted in an immunosuppressive TME. We also detected high expression levels of ALDOA in the MRS2 cells and HCC tissues. In the clinical cohort, HCC patients with higher ALDOA expression showed greater enrichment of immunosuppressive cells including M2 macrophages and T regulatory cells.DiscussionThe glycolytic subtype of HCC cells with high ALDOA expression is associated with an immunosuppressive TME and predicts worse clinical outcomes, providing new insights into the metabolism and prognosis of HCC.

Project description:There is an urgent need to develop more effective therapies for hepatocellular carcinoma (HCC) because of its aggressiveness. Guadecitabine (SGI-110) is a second-generation DNA methyltransferase inhibitor (DNMTi), which is currently in clinical trials for HCC and shows greater stability and performance over first-generation DNMTis. In order to identify potential therapeutic targets of SGI-110 for clinical trials, HCC cell lines (SNU398, HepG2, and SNU475) were used to evaluate the effects of transient SGI-110 treatment by an integrative analysis of DNA methylation, nucleosome accessibility, gene expression profiles, and its clinical relevance by comparison to The Cancer Genome Atlas (TCGA) HCC clinical data. Each HCC cell line represents a different DNA methylation subtype of primary HCC tumors based on TCGA data. After SGI-110 treatment, all cell lines were sensitive to SGI-110 with prolonged antiproliferation effects. Expression of up-regulated genes, including tumor suppressors, was positively correlated with nucleosome accessibility and negatively correlated with gene promoter DNA methylation. Alternatively, expression of down-regulated genes, such as oncogenes, was negatively correlated with nucleosome accessibility and positively correlated with gene body DNA methylation. SGI-110 can also act as a dual inhibitor to down-regulate polycomb repressive complex 2 (PRC2) genes by demethylating their gene bodies, resulting in reactivation of PRC2 repressed genes without involvement of DNA methylation. Furthermore, it can up-regulate endogenous retroviruses to reactivate immune pathways. Finally, about 48% of frequently altered genes in primary HCC tumors can be reversed by SGI-110 treatment. CONCLUSION:Our integrative analysis has successfully linked the antitumor effects of SGI-110 to detailed epigenetic alterations in HCC cells, identified potential therapeutic targets, and provided a rationale for combination treatments of SGI-110 with immune checkpoint therapies.

Project description:ObjectiveEpigenetic aberration plays an important role in the development and progression of hepatocellular carcinoma (HCC). However, the alteration of RNA N6-methyladenosine (m6A) modifications and its role in HCC progression remain unclear. We therefore aimed to provide evidence using bioinformatics analysis.MethodsWe comprehensively analyzed the m6A regulator modification patterns of 605 HCC samples and correlated them with metabolic alteration characteristics. We elucidated 390 gene-based m6A-related signatures and defined an m6Ascore to quantify m6A modifications. We then assessed their values for predicting prognoses and therapeutic responses in HCC patients.ResultsWe identified 3 distinct m6A modification patterns in HCC, and each pattern had distinct metabolic characteristics. The evaluation of m6A modification patterns using m6Ascores could predict the prognoses, tumor stages, and responses to sorafenib treatments of HCC patients. A nomogram based on m6Ascores showed high accuracy in predicting the overall survival of patients. The area under the receiver operating characteristic curve of predictions of 1, 3, and 5-year overall survivals were 0.71, 0.69, and 0.70 in the training cohort, and in the test cohort it was 0.74, 0.75, and 0.71, respectively. M6Acluster C1, which corresponded to hypoactive mRNA methylation, lower expression of m6A regulators, and a lower m6Ascore, was characterized by metabolic hyperactivity, lower tumor stage, better prognosis, and lower response to sorafenib treatment. In contrast, m6Acluster C3 was distinct in its hyperactive mRNA methylations, higher expression of m6A regulators, and higher m6Ascores, and was characterized by hypoactive metabolism, advanced tumor stage, poorer prognosis, and a better response to sorafenib. The m6Acluster, C2, was intermediate between C1 and C3.ConclusionsHCCs harbored distinct m6A regulator modification patterns that contributed to the metabolic heterogeneity and diversity of HCC. Development of m6A gene signatures and the m6Ascore provides a more comprehensive understanding of m6A modifications in HCC, and helps predict the prognosis and treatment response.

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

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

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