Deciphering Key Genes and miRNAs Associated With Hepatocellular Carcinoma via Network-Based Approach

Differentially expressed discriminative genes and significant meta-hub genes based key genes identification for hepatocellular carcinoma using statistical machine learning

Hepatocellular carcinoma (HCC) is the most common lethal malignancy of the liver worldwide. Thus, it is important to dig the key genes for uncovering the molecular mechanisms and to improve diagnostic and therapeutic options for HCC. This study aimed to encompass a set of statistical and machine learning computational approaches for identifying the key candidate genes for HCC. Three microarray datasets were used in this work, which were downloaded from the Gene Expression Omnibus Database. At first, normalization and differentially expressed genes (DEGs) identification were performed using limma for each dataset. Then, support vector machine (SVM) was implemented to determine the differentially expressed discriminative genes (DEDGs) from DEGs of each dataset and select overlapping DEDGs genes among identified three sets of DEDGs. Enrichment analysis was performed on common DEDGs using DAVID. A protein-protein interaction (PPI) network was constructed using STRING and the central hub genes were identified depending on the degree, maximum neighborhood component (MNC), maximal clique centrality (MCC), centralities of closeness, and betweenness criteria using CytoHubba. Simultaneously, significant modules were selected using MCODE scores and identified their associated genes from the PPI networks. Moreover, metadata were created by listing all hub genes from previous studies and identified significant meta-hub genes whose occurrence frequency was greater than 3 among previous studies. Finally, six key candidate genes (TOP2A, CDC20, ASPM, PRC1, NUSAP1, and UBE2C) were determined by intersecting shared genes among central hub genes, hub module genes, and significant meta-hub genes. Two independent test datasets (GSE76427 and TCGA-LIHC) were utilized to validate these key candidate genes using the area under the curve. Moreover, the prognostic potential of these six key candidate genes was also evaluated on the TCGA-LIHC cohort using survival analysis.

Comprehensive investigating of MMR gene in hepatocellular carcinoma with chronic hepatitis B virus infection in Han Chinese population

Hepatocellular carcinoma associated with chronic hepatitis B virus infection seriously affects human health. Present studies suggest that genetic susceptibility plays an important role in the mechanism of cancer development. Therefore, this study focused on single nucleotide polymorphisms (SNPs) of MMR genes associated with HBV-HCC. Five groups of participants were included in this study, which were healthy control group (HC), spontaneous clearance (SC), chronic hepatitis B group (CHB), HBV-related liver cirrhosis group (LC) and HBV-related hepatocellular carcinoma group (HBV-HCC). A total of 3128 participants met the inclusion and exclusion criteria for this study. 20 polymorphic loci on MSH2, MSH3 and MSH6 were selected for genotyping. There were four case-control studies, which were HC vs. HCC, SC vs. HCC, CHB vs. HCC and LC vs. HCC. We used Hardy-Weinberg equilibrium test, unconditional logistic regression, haplotype analysis, and gene-gene interaction for genetic analysis. Ultimately, after excluding confounding factors such as age, gender, smoking and drinking, 12 polymorphisms were found to be associated with genetic susceptibility to HCC. Haplotype analysis showed the risk haplotype GTTT (rs1805355_G, rs3776968_T, rs1428030_C, rs181747_C) was more frequent in the HCC group compared with the HC group. The GMDR analysis showed that the best interaction model was the three-factor model of MSH2-rs1981928, MSH3-rs26779 and MSH6-rs2348244 in SC vs. HCC group (P=0.001). In addition, we found multiplicative or additive interactions between genes in our selected SNPs. These findings provide new ideas to further explore the etiology and pathogenesis of HCC. We have attempted to explain the molecular mechanisms by which certain SNPs (MSH2-rs4952887, MSH3-rs26779, MSH3-rs181747 and MSH3-rs32950) affect genetic susceptibility to HCC from the perspectives of eQTL, TFBS, cell cycle and so on. We also explained the results of haplotypes and gene-gene interactions. These findings provide new ideas to further explore the etiology and pathogenesis of HCC.