Dataset Information

Identification of Potential Hub Genes Related to Diagnosis and Prognosis of Hepatitis B Virus-Related Hepatocellular Carcinoma via Integrated Bioinformatics Analysis.

ABSTRACT: Hepatocellular carcinoma (HCC) is a common malignant cancer with poor survival outcomes, and hepatitis B virus (HBV) infection is most likely to contribute to HCC. But the molecular mechanism remains obscure. Our study intended to identify the candidate potential hub genes associated with the carcinogenesis of HBV-related HCC (HBV-HCC), which may be helpful in developing novel tumor biomarkers for potential targeted therapies. Four transcriptome datasets (GSE84402, GSE25097, GSE94660, and GSE121248) were used to screen the 309 overlapping differentially expressed genes (DEGs), including 100 upregulated genes and 209 downregulated genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment were used to explore the biological function of DEGs. A PPI network based on the STRING database was constructed and visualized by the Cytoscape software, consisting of 209 nodes and 1676 edges. Then, we recognized 17 hub genes by CytoHubba plugin, which were further validated on additional three datasets (GSE14520, TCGA-LIHC, and ICGC-LIRI-JP). The diagnostic effectiveness of hub genes was assessed with receiver operating characteristic (ROC) analysis, and all hub genes displayed good performance in discriminating TNM stage I patient samples and normal tissue ones. For prognostic analysis, two prognostic key genes (TOP2A and KIF11) out of the 17 hub genes were screened and used to develop a prognostic signature, which showed good potential for overall survival (OS) stratification of HBV-HCC patients. Gene Set Enrichment Analysis (GSEA) was performed in order to better understand the function of this prognostic gene signature. Finally, the miRNA-mRNA regulatory relationships of all hub genes in human liver were predicted using miRNet. In conclusion, the current study gives further insight on the pathogenesis and carcinogenesis of HBV-HCC, and the identified DEGs provide a promising direction for improving the diagnostic, prognostic, and therapeutic outcomes of HBV-HCC.

SUBMITTER: Tang Y

PROVIDER: S-EPMC7744201 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Identification of Potential Hub Genes Related to Diagnosis and Prognosis of Hepatitis B Virus-Related Hepatocellular Carcinoma via Integrated Bioinformatics Analysis.

Tang Yuqin Y Zhang Yongqiang Y Hu Xun X

BioMed research international 20201208

Hepatocellular carcinoma (HCC) is a common malignant cancer with poor survival outcomes, and hepatitis B virus (HBV) infection is most likely to contribute to HCC. But the molecular mechanism remains obscure. Our study intended to identify the candidate potential hub genes associated with the carcinogenesis of HBV-related HCC (HBV-HCC), which may be helpful in developing novel tumor biomarkers for potential targeted therapies. Four transcriptome datasets (GSE84402, GSE25097, GSE94660, and GSE121 ...[more]

PMID: 33376723

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Project description:BackgroundThe majority of primary liver cancers in adults worldwide are hepatocellular carcinomas (HCCs, or hepatomas). Thus, a deep understanding of the underlying mechanisms for the pathogenesis and carcinogenesis of HCC at the molecular level could facilitate the development of novel early diagnostic and therapeutic treatments to improve the approaches and prognosis for HCC patients. Our study elucidates the underlying molecular mechanisms of HBV-HCC development and progression and identifies important genes related to the early diagnosis, tumour stage, and poor outcomes of HCC.MethodsGSE55092 and GSE121248 gene expression profiling data were downloaded from the Gene Expression Omnibus (GEO) database. There were 119 HCC samples and 128 nontumour tissue samples. GEO2R was used to screen for differentially expressed genes (DEGs). Volcano plots and Venn diagrams were drawn by using the ggplot2 package in R. A heat map was generated by using Heatmapper. By using the clusterProfiler R package, KEGG and GO enrichment analyses of DEGs were conducted. Through PPI network construction using the STRING database, key hub genes were identified by cytoHubba. Finally, KM survival curves and ROC curves were generated to validate hub gene expression.ResultsBy GO enrichment analysis, 694 DEGs were enriched in the following GO terms: organic acid catabolic process, carboxylic acid catabolic process, carboxylic acid biosynthetic process, collagen-containing extracellular matrix, blood microparticle, condensed chromosome kinetochore, arachidonic acid epoxygenase activity, arachidonic acid monooxygenase activity, and monooxygenase activity. In the KEGG pathway enrichment analysis, DEGs were enriched in arachidonic acid epoxygenase activity, arachidonic acid monooxygenase activity, and monooxygenase activity. By PPI network construction and analysis of hub genes, we selected the top 10 genes, including CDK1, CCNB2, CDC20, BUB1, BUB1B, CCNB1, NDC80, CENPF, MAD2L1, and NUF2. By using TCGA and THPA databases, we found five genes, CDK1, CDC20, CCNB1, CENPF, and MAD2L1, that were related to the early diagnosis, tumour stage, and poor outcomes of HBV-HCC.ConclusionsFive abnormally expressed hub genes of HBV-HCC are informative for early diagnosis, tumour stage determination, and poor outcome prediction.

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:BackgroundThere is evidence that abnormal expression of lncRNAs is associated with hepatitis B virus (HBV) infection-induced hepatocellular carcinoma (HCC). However, the mechanisms remain not fully elucidated. The study aimed to identify novel lncRNAs and explore their underlying mechanisms based on the ceRNA hypothesis.MethodsThe RNA and miRNA expression profiling in 20 tumor and matched adjacent tissues from HBV-HCC patients were retrieved from the Gene Expression Omnibus database under accession numbers GSE77509 and GSE76903, respectively. Differentially expressed lncRNAs (DELs), miRNAs (DEMs), and genes (DEGs) were identified using the EdgeR package. Protein-protein interaction (PPI) network was constructed for DEGs followed by module analysis. The ceRNA network was constructed based on interaction relationships between miRNAs and mRNAs/lncRNAs. The functions of DEGs were predicted using DAVID and BinGO databases. The prognosis values (overall survival [OS] and recurrence-free survival [RFS]) of ceRNA network genes were determined using The Cancer Genome Atlas (TCGA) data with Cox regression analysis and Kaplan-Meier method.ResultsThe present study screened 643 DELs, 83 DEMs, and 1,187 DEGs. PPI network analysis demonstrated that CDK1 and CCNE1 were hub genes and extracted in functionally related modules. E2F2, CDK1, and CCNE1 were significantly enriched into cell cycle pathway. FAM182B-miR-125b-5p-E2F2 and LINC00346-miR-10a-5p-CDK1/CCNE1 ceRNA axes were obtained by constructing the ceRNA network. Patients with high expressions of DELs and DEGs in the above ceRNA axes had poor OS, while patients with the high expression of DEMs possessed excellent OS. CDK1 was also an RFS-related biomarker, with its high expression predicting poor RFS. The upregulation of LINC00346 and CDK1 but the downregulation of miR-10a-5p in HCC was validated in other microarray datasets and TCGA database.ConclusionThe LINC00346-miR-10a-5p-CDK1 axis may be an important mechanism for HBV-related HCC, and genes in this ceRNA axis may be potential prognostic biomarkers and therapeutic targets.

Dataset Information

Identification of Potential Hub Genes Related to Diagnosis and Prognosis of Hepatitis B Virus-Related Hepatocellular Carcinoma via Integrated Bioinformatics Analysis.

Publications

Identification of Potential Hub Genes Related to Diagnosis and Prognosis of Hepatitis B Virus-Related Hepatocellular Carcinoma via Integrated Bioinformatics Analysis.

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