Dataset Information

DNA methylation biomarkers for hepatocellular carcinoma.

ABSTRACT:

Background

Aberrant methylation of DNA is a key driver of hepatocellular carcinoma (HCC). In this study, we sought to integrate four cohorts profile datasets to identify such abnormally methylated genes and pathways associated with HCC.

Methods

To this end, we downloaded microarray datasets examining gene expression (GSE84402, GSE46408) and gene methylation (GSE73003, GSE57956) from the GEO database. Abnormally methylated differentially expressed genes (DEGs) were sorted and pathways were analyzed. The String database was then used to perform enrichment and functional analysis of identified pathways and genes. Cytoscape software was used to create a protein-protein interaction network, and MCODE was used for module analysis. Finally, overall survival analysis of hub genes was performed by the OncoLnc online tool.

Results

In total, we identified 19 hypomethylated highly expressed genes and 14 hypermethylated lowly expressed genes at the screening step, and finally found six mostly changed hub genes including MAD2L1, CDC20, CCNB1, CCND1, AR and ESR1. Pathway analysis showed that aberrantly methylated-DEGs mainly associated with the cell cycle process, p53 signaling, and MAPK signaling in HCC. After validation in TCGA database, the methylation and expression status of hub genes was significantly altered and same with our results. Patients with high expression of MAD2L1, CDC20 and CCNB1 and low expression of CCND1, AR, and ESR1 was associated with shorter overall survival.

Conclusions

Taken together, we have identified novel aberrantly methylated genes and pathways linked to HCC, potentially offering novel insights into the molecular mechanisms governing HCC progression and serving as novel biomarkers for precision diagnosis and disease treatment.

SUBMITTER: Fan G

PROVIDER: S-EPMC6142709 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Publications

DNA methylation biomarkers for hepatocellular carcinoma.

Fan Guorun G Tu Yaqin Y Chen Cai C Sun Haiying H Wan Chidan C Cai Xiong X

Cancer cell international 20180917

<h4>Background</h4>Aberrant methylation of DNA is a key driver of hepatocellular carcinoma (HCC). In this study, we sought to integrate four cohorts profile datasets to identify such abnormally methylated genes and pathways associated with HCC.<h4>Methods</h4>To this end, we downloaded microarray datasets examining gene expression (GSE84402, GSE46408) and gene methylation (GSE73003, GSE57956) from the GEO database. Abnormally methylated differentially expressed genes (DEGs) were sorted and pathw ...[more]

PMID: 30245591

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Project description:BACKGROUND:Aberrant methylation of DNA plays an important role in the pathogenesis of nasopharyngeal carcinoma (NPC). In the current study, we aimed to integrate three cohorts profile datasets to identify abnormally methylated-differentially expressed genes and pathways associated with NPC. METHODS:Data of gene expression microarrays (GSE53819, GSE412452) and gene methylation microarrays (GSE52068) obtained from the GEO database. Aberrantly methylated differentially expressed genes (DEGs) were obtained by GEO2R. The David database was utilized to perform enrichment and functional analysis regarding selected genes. To create a protein-protein interaction (PPI), STRING and Cytoscape software were utilized. The MCODE was used for module analysis of the PPI network. RESULTS:In total, 181 hypomethylation-high expression genes were identified, which were enriched in the biological mechanisms involved in the differentiation of endodermal cell, mitotic nuclear division, mitotic cell cycle process, chromosome segregation and cell cycle phase transition, etc. Pathway enrichment showed ECM-receptor interaction, PI3K-Akt signaling pathway, Focal adhesion, Protein digestion and absorption and Amoebiasis, etc. The top 3 hub genes of PPI network were FANCI, POSTN, and IFIH1. Additionally, 210 hypermethylation-low expression genes were identified, and our data revealed enrichment in biological processes including axoneme assembly, micro tubular formation, assembly of axonemal dynein complex, cilium movement and cilium organization, etc. Pathway analysis indicated enrichment in B cell receptor signaling pathway, Hematopoietic cell lineage, Leukocyte transendothelial migration, Complement and coagulation cascades and Fc gamma R-mediated phagocytosis, etc. The ZMYND10, PACRG and POU2AF1 were identified as the top three hub genes of PPI network. After validation in TCGA and GEPIA database, most hub genes remained significant. Patients with high expression of POSTN found to have shorter overall survival, while in patients with high expression of ZMYND10 and POU2AF1 longer overall survival was identified. CONCLUSIONS:The data revealed novel aberrantly methylated-differentially expressed genes and pathways in NPC by bioinformatics analysis, potentially providing novel insights for the molecular mechanisms governing NPC progression. Hub genes including FANCI, POSTN, IFIH1, ZMYND10, PACRG and POU2AF1 might serve as novel biomarkers for precision diagnosis and providing medical treatment for patient with NPC.

Project description:Hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) are the two most common pathology subtypes of primary liver cancer (PLC). Identifying DNA methylation biomarkers for diagnosis of PLC and further distinguishing HCC from ICC plays a vital role in subsequent treatment options selection. To obtain potential diagnostic DNA methylation sites for PLC, differentially methylated CpG (DMC) sites were first screened by comparing the methylation data between normal liver samples and PLC samples (ICC samples and HCC samples). A random forest algorithm was then used to select specific DMC sites with top Gini value. To avoid overfitting, another cohort was taken as an external validation for evaluating the area under curves (AUCs) of different DMC sites combination. A similar model construction strategy was applied to distinguish HCC from ICC. In addition, we identified DNA Methylation-Driven Genes in HCC and ICC via MethylMix method and performed pathway analysis by utilizing MetaCore. Finally, we not only performed methylator phenotype based on independent prognostic sites but also analyzed the correlations between methylator phenotype and clinical factors in HCC and ICC, respectively. To diagnose PLC, we developed a model based on three PLC-specific methylation sites (cg24035245, cg21072795, and cg00261162), whose sensitivity and specificity achieved 98.8%,94.8% in training set and 97.3%,81% in validation set. Then, to further divide the PLC samples into HCC and ICC, we established another mode through three methylation sites (cg17769836, cg17591574, and cg07823562), HCC accuracy and ICC accuracy achieved 95.8%, 89.8% in the training set and 96.8%,85.4% in the validation set. In HCC, the enrichment pathways were mainly related to protein folding, oxidative stress, and glutathione metabolism. While in ICC, immune response, embryonic hepatocyte maturation were the top pathways. Both in HCC and ICC, methylator phenotype correlated well with overall survival time and clinical factors involved in tumor progression. In summary, our study provides the biomarkers based on methylation sites not only for the diagnosis of PLC but also for distinguishing HCC from ICC.

Dataset Information

DNA methylation biomarkers for hepatocellular carcinoma.

Background

Methods

Results

Conclusions

Publications

DNA methylation biomarkers for hepatocellular carcinoma.

Similar Datasets

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