Journal of Shandong University (Health Sciences) ›› 2023, Vol. 61 ›› Issue (9): 101-117.doi: 10.6040/j.issn.1671-7554.0.2023.0468

• Clinical Medicine • Previous Articles     Next Articles

Comprehensive bioinformatics analysis to identify differentially expressed genes for aberrant methylation modification in HBV-associated HCC

CHEN Yingjun, LIU Tonggang   

  1. Department of Infectious Diseases, Binzhou Medical University Hospital, Binzhou 256603, Shandong, China
  • Received:2023-06-01 Published:2023-10-10

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Abstract: Objective To explore the differentially expressed genes(DEGs)and molecular mechanism of abnormal methylation modification associated with the development of hepatitis B virus(HBV)-associated hepatocellular carcinoma(HCC)for the early diagnosis of this disease. Methods After the expression profile chips GSE121248, GSE107170 and DNA methylation chip GSE136319 were downloaded from the Gene Expression Database(GEO), the DEGs and differentially methylated genes(DMGs)between HBV-associated HCC tissues and adjacent tissues were screened with R language, and the visual volcano map was drawn. Gene ontology(GO)and Kyoto encyclopedia of genes and genomes(KEGG)enrichment analysis of the methylated-differentially expressed genes(MDEGs)were performed to construct protein-protein interaction(PPI)networks. The molecular complex detection(MCODE)was conducted with Cytoscap, and key genes were screened with cytoHubba plugin. The mRNA expression levels of key genes were verified with the Cancer Genome Atlas(TCGA). The relationship between methylation and gene expression of key genes in HCC was determined with Pearson correlation coefficient. HPA database, Cox proportional hazard regression model, Kaplan Meier-plotter database and receiver operating characteristic(ROC)curve were used to verify the protein expressions of key genes, survival analysis and prediction accuracy. The correlation between the expressions of key genes and clinical indicators(tumor size, pathological stage)were analyzed. Results A total of 921 and 1,172 DEGs were screened from the GSE121248 and GSE107170 datasets, respectively, with 570 and 714 down-regulated and 351 and 458 upregulated genes, respectively. After differential analysis of GSE136319 data, 7 952 genes were hyptrmethylated and 2 630 genes were hypomethylated. A comprehensive analysis of DEGs and DMGs yielded 33 genes upregulated under hypomethylation modification and 158 genes downregulated under hypermethylation modification. GO enrichment analysis showed that the DEGs with abnormal methylation modification were mainly involved in organic acid catabolic process, carboxylic acid catabolic process and heme binding; KEGG pathways were mainly involved in chemical carcinogenesis, complements, coagulation cascades and PPAR signaling pathway. STRING and Cytoscape screened out 12 key genes related to methylation, including FTCD, HRG, C8A, FOXM1, FGA, KLKB1, MBL2, FETUB, TTK, AURKA, PRC1and MAD2L1. After clinical verification, FTCD, HRG, C8A, FOXM1, AURKA, PRC1, TTK and MAD2L1 were confirmed to be differentially expressed in HBV-related HCC and were associated with poor prognosis. The expression levels of FTCD, HRG, FOXM1, TTK, AURKA, PRC1 and MAD2L1 were correlated with tumor size and pathological stage. Conclusion FTCD, HRG, C8A, FOXM1, TTK, AURKA, PRC1 and MAD2L1 may play important roles in the pathogenesis of HBV-related HCC, which may serve as potential diagnostic markers and therapeutic targets.

Key words: Hepatitis B virus, Hepatocellular carcinoma, DNA methylation, Biomarker, Bioinformatics analysis

CLC Number: 

  • R735.7
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