Dataset Information

Identification of Key Regulators of Hepatitis C Virus-Induced Hepatocellular Carcinoma by Integrating Whole-Genome and Transcriptome Sequencing Data.

ABSTRACT: Background: Hepatitis C virus (HCV) infection is a major cause of cirrhosis and hepatocellular carcinoma (HCC). Despite recent advances in the understanding of the biological basis of HCC development, the molecular mechanisms underlying HCV-induced HCC (HCC-HCV) remain unclear. The carcinogenic potential of HCV varies according to the genotype and mutation in its viral sequence. Moreover, regulatory pathways play important roles in many pathogenic processes. Therefore, identifying the pathways by which HCV induces HCC may enable improved HCC diagnosis and treatment. Methods: We employed a systematic approach to identify an important regulatory module in the process of HCV-HCC development to find the important regulators. First, an HCV-related HCC subnetwork was constructed based on the gene expression in HCC-HCV patients and HCC patients. A priority algorithm was then used to extract the module from the subnetworks, and all the regulatory relationships of the core genes of the network were extracted. Integrating the significantly highly mutated genes involved in the HCC-HCV patients, core regulatory modules and key regulators related to disease prognosis and progression were identified. Result: The key regulatory genes including EXO1, VCAN, KIT, and hsa-miR-200c-5p were found to play vital roles in HCV-HCC development. Based on the statistics analysis, EXO1, VCAN, and KIT mutations are potential biomarkers for HCV-HCC prognosis at the genomic level, whereas has-miR-200c-5P is a potential biomarker for HCV-HCC prognosis at the expression level. Conclusion: We identified three significantly mutated genes and one differentially expressed miRNA, all related to HCC prognosis. As potential pathogenic factors of HCC, these genes and the miRNA could be new biomarkers for HCV-HCC diagnosis.

SUBMITTER: Chen G

PROVIDER: S-EPMC8460086 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundHepatocellular carcinoma (HCC) is one of the most common cancers worldwide. However, the molecular mechanisms involved in HCC remain unclear and are in urgent need of elucidation. Therefore, we sought to identify biomarkers in the prognosis of HCC through an integrated bioinformatics analysis.MethodsMessenger RNA (mRNA) expression profiles were obtained from the Gene Expression Omnibus database and The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) for the screening of common differentially expressed genes (DEGs). Function and pathway enrichment analysis, protein-protein interaction network construction and key gene identification were performed. The significance of key genes in HCC was validated by overall survival analysis and immunohistochemistry. Meanwhile, based on TCGA data, prognostic microRNAs (miRNAs) were decoded using univariable and multivariable Cox regression analysis, and their target genes were predicted by miRWalk.ResultsEleven hub genes (upregulated ASPM, AURKA, CCNB2, CDC20, PRC1 and TOP2A and downregulated AOX1, CAT, CYP2E1, CYP3A4 and HP) with the most interactions were considered as potential biomarkers in HCC and confirmed by overall survival analysis. Moreover, AURKA, PRC1, TOP2A, AOX1, CYP2E1, and CYP3A4 were considered candidate liver-biopsy markers for high risk of developing HCC and poor prognosis in HCC. Upregulation of hsa-mir-1269b, hsa-mir-518d, hsa-mir-548aq, hsa-mir-548f-1, and hsa-mir-6728, and downregulation of hsa-mir-139 and hsa-mir-4800 were determined to be risk factors of poor prognosis, and most of these miRNAs have strong potential to help regulate the expression of key genes.ConclusionsThis study undertook the first large-scale integrated bioinformatics analysis of the data from Illumina BeadArray platforms and the TCGA database. With a comprehensive analysis of transcriptional alterations, including mRNAs and miRNAs, in HCC, our study presented candidate biomarkers for the surveillance and prognosis of the disease, and also identified novel therapeutic targets at the molecular and pathway levels.

Dataset Information

Identification of Key Regulators of Hepatitis C Virus-Induced Hepatocellular Carcinoma by Integrating Whole-Genome and Transcriptome Sequencing Data.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets