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Identification of hub genes in hepatocellular carcinoma using integrated bioinformatic analysis.

ABSTRACT: The molecular mechanisms underlying hepatocellular carcinoma (HCC) progression remain largely undefined. Here, we identified 176 commonly upregulated genes in HCC tissues based on three Gene Expression Omnibus datasets and The Cancer Genome Atlas (TCGA) cohort. We integrated survival and methylation analyses to further obtain 12 upregulated genes for validation. These genes were overexpressed in HCC tissues at the transcription and protein levels, and increased mRNA levels were related to higher tumor grades and cancer stages. The expression of all markers was negatively associated with overall and disease-free survival in HCC patients. Most of these hub genes can promote HCC proliferation and/or metastasis. These 12 hub genes were also overexpressed and had strong prognostic value in many other cancer types. Methylation and gene copy number analyses indicated that the upregulation of these hub genes was probably due to hypomethylation or increased gene copy numbers. Further, the methylation levels of three genes, KPNA2, MCM3, and LRRC1, were associated with HCC clinical features. Moreover, the levels of most hub genes were related to immune cell infiltration in HCC microenvironments. Finally, we identified three upregulated genes (KPNA2, TARBP1, and RNASEH2A) that could comprehensively and accurately provide diagnostic and prognostic value for HCC patients.

SUBMITTER: Hua S

PROVIDER: S-EPMC7138582 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Identification of hub genes in hepatocellular carcinoma using integrated bioinformatic analysis.

Hua Shengni S Ji Zhonghua Z Quan Yingyao Y Zhan Meixiao M Wang Hao H Li Wei W Li Yong Y He Xu X Lu Ligong L

Aging 20200326 6

The molecular mechanisms underlying hepatocellular carcinoma (HCC) progression remain largely undefined. Here, we identified 176 commonly upregulated genes in HCC tissues based on three Gene Expression Omnibus datasets and The Cancer Genome Atlas (TCGA) cohort. We integrated survival and methylation analyses to further obtain 12 upregulated genes for validation. These genes were overexpressed in HCC tissues at the transcription and protein levels, and increased mRNA levels were related to higher ...[more]

PMID: 32213663

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Project description:BackgroundHepatocellular carcinoma (HCC), the main type of liver cancer in human, is one of the most prevalent and deadly malignancies in the world. The present study aimed to identify hub genes and key biological pathways by integrated bioinformatics analysis.MethodsA bioinformatics pipeline based on gene co-expression network (GCN) analysis was built to analyze the gene expression profile of HCC. Firstly, differentially expressed genes (DEGs) were identified and a GCN was constructed with Pearson correlation analysis. Then, the gene modules were identified with 3 different community detection algorithms, and the correlation analysis between gene modules and clinical indicators was performed. Moreover, we used the Search Tool for the Retrieval of Interacting Genes (STRING) database to construct a protein protein interaction (PPI) network of the key gene module, and we identified the hub genes using nine topology analysis algorithms based on this PPI network. Further, we used the Oncomine analysis, survival analysis, GEO data set and random forest algorithm to verify the important roles of hub genes in HCC. Lastly, we explored the methylation changes of hub genes using another GEO data (GSE73003).ResultsFirstly, among the expression profiles, 4,130 up-regulated genes and 471 down-regulated genes were identified. Next, the multi-level algorithm which had the highest modularity divided the GCN into nine gene modules. Also, a key gene module (m1) was identified. The biological processes of GO enrichment of m1 mainly included the processes of mitosis and meiosis and the functions of catalytic and exodeoxyribonuclease activity. Besides, these genes were enriched in the cell cycle and mitotic pathway. Furthermore, we identified 11 hub genes, MCM3, TRMT6, AURKA, CDC20, TOP2A, ECT2, TK1, MCM2, FEN1, NCAPD2 and KPNA2 which played key roles in HCC. The results of multiple verification methods indicated that the 11 hub genes had highly diagnostic efficiencies to distinguish tumors from normal tissues. Lastly, the methylation changes of gene CDC20, TOP2A, TK1, FEN1 in HCC samples had statistical significance (P-value < 0.05).ConclusionMCM3, TRMT6, AURKA, CDC20, TOP2A, ECT2, TK1, MCM2, FEN1, NCAPD2 and KPNA2 could be potential biomarkers or therapeutic targets for HCC. Meanwhile, the metabolic pathway, the cell cycle and mitotic pathway might played vital roles in the progression of HCC.

Project description:PurposeTo investigate the potential pathophysiological association between tuberculosis (TB) and diabetes mellitus (DM) using bioinformatic analyses.Patients and methodsGene expression datasets for healthy controls (HCs), TB patients, DM patients, TB+DM patients (GSE114192), and metformin-treated cells (GSE102677) were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified from pairwise dataset comparisons TB vs HCs and DM vs HCs. DEGs were verified by comparing them to DEGs for TB+DM vs HCs. Enrichment analysis of DEGs common to all three dataset comparisons was conducted using DAVID. The protein-protein interaction (PPI) network was established via STRING and visualised in Cytoscape. Hub genes were identified using the Cytoscape plug-in cytoHubba and then were verified using reverse transcription-quantitative polymerase chain reaction (RT-qPCR) analysis. Targeted miRNA prediction analysis identified metformin treatment-induced gene expression changes in peripheral blood mononuclear cells.ResultsA total of 422 DEGs were common to all three dataset comparisons. Functional enrichment analysis revealed these DEGs were enriched for functional terms of type I interferon signaling pathway, innate immune response, inflammatory response, and infectious diseases. Ten hub genes identified using PPI network analysis were screened for interactions with metformin target gene INS using cytoHubba based on maximal clique centrality (MCC) score. Subsequently, five hub genes were predicted to functionally interact with INS, including STAT1, IFIT3, RSAD2, IFI44L, and XAF1, as verified by RT-qPCR. Meanwhile, seven miRNAs (miR-3680-3p, miR-3059-5p, miR-629-3p, miR-29b-2-5p, miR-514b-5p, miR-4755-5p, miR-4691-3p) were associated with regulation of hub genes. Notably, six hub genes (STAT1, IFIT3, RSAD2, ISG15, IFI44, IFI6) were down-regulated in cells exposed to both metformin and Mycobacterium tuberculosis antigens.ConclusionNetwork hub genes hold promise as disease status biomarkers and as metformin treatment targets for alleviating TB and DM. This study describes a strategy for exploring pathogenic mechanisms of diseases such as TB and DM.

Project description:BackgroundHepatocellular carcinoma (HCC) is one of the most commonly diagnosed cancers and the fourth leading cause of cancer-related deaths in the world. Although the treatment of HCC has made great progress in recent years, the therapeutic effects on HCC are still unsatisfactory due to difficulty in early diagnosis, chemoresistance and high recurrence rate post-surgery.MethodsIn this study, we identified differentially expressed genes (DEGs) based on four Gene Expression Omnibus (GEO) datasets (GSE45267, GSE98383, GSE101685 and GSE112790) between HCC and normal hepatic tissues. A protein-protein interaction (PPI) network was established to identify the central nodes associated with HCC. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of the central nodes were conducted to find the hub genes. The expression levels of the hub genes were validated based on the ONCOMINE and Gene Expression Profiling Interactive Analysis (GEPIA) databases. Additionally, the genetic alterations of the hub genes were evaluated by cBioPortal. The role of the hub genes on the overall survival (OS) and relapse survival (RFS) of HCC patients was evaluated by Kaplan-Meier plotter. At last, the mechanistic role of the hub genes was illustrated by in vitro experiments.ResultsWe found the following seven hub genes: BUB1B, CCNB1, CCNB2, CDC20, CDK1, MAD2L1 and RRM2 using integrated bioinformatics analysis. All of the hub genes were significantly upregulated in HCC tissues. And the seven hub genes were associated with the OS and RFS of HCC patients. Finally, in vitro experiments indicated that BUB1B played roles in HCC cell proliferation, migration, invasion, apoptosis and cell cycle by partially affecting mitochondrial functions.ConclusionsIn summary, we identified seven hub genes that were associated with the expression and prognosis of HCC. The mechanistic oncogenic role of BUB1B in HCC was first illustrated. BUB1B might play an important role in HCC and could be potential therapeutic targets for HCC.

Dataset Information

Identification of hub genes in hepatocellular carcinoma using integrated bioinformatic analysis.

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Identification of hub genes in hepatocellular carcinoma using integrated bioinformatic analysis.

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