Dataset Information

Screening and Identification of Potential Biomarkers in Hepatitis B Virus-Related Hepatocellular Carcinoma by Bioinformatics Analysis.

ABSTRACT: Hepatocellular carcinoma (HCC) is one of the most lethal cancers globally. Hepatitis B virus (HBV) infection might cause chronic hepatitis and cirrhosis, leading to HCC. To screen prognostic genes and therapeutic targets for HCC by bioinformatics analysis and determine the mechanisms underlying HBV-related HCC, three high-throughput RNA-seq based raw datasets, namely GSE25599, GSE77509, and GSE94660, were obtained from the Gene Expression Omnibus database, and one RNA-seq raw dataset was acquired from The Cancer Genome Atlas (TCGA). Overall, 103 genes were up-regulated and 127 were down-regulated. A protein-protein interaction (PPI) network was established using Cytoscape software, and 12 pivotal genes were selected as hub genes. The 230 differentially expressed genes and 12 hub genes were subjected to functional and pathway enrichment analyses, and the results suggested that cell cycle, nuclear division, mitotic nuclear division, oocyte meiosis, retinol metabolism, and p53 signaling-related pathways play important roles in HBV-related HCC progression. Further, among the 12 hub genes, kinesin family member 11 (KIF11), TPX2 microtubule nucleation factor (TPX2), kinesin family member 20A (KIF20A), and cyclin B2 (CCNB2) were identified as independent prognostic genes by survival analysis and univariate and multivariate Cox regression analysis. These four genes showed higher expression levels in HCC than in normal tissue samples, as identified upon analyses with Oncomine. In addition, in comparison with normal tissues, the expression levels of KIF11, TPX2, KIF20A, and CCNB2 were higher in HBV-related HCC than in HCV-related HCC tissues. In conclusion, our results suggest that KIF11, TPX2, KIF20A, and CCNB2 might be involved in the carcinogenesis and development of HBV-related HCC. They can thus be used as independent prognostic genes and novel biomarkers for the diagnosis of HBV-related HCC and development of pertinent therapeutic strategies.

SUBMITTER: Zeng XC

PROVIDER: S-EPMC7556301 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Screening and Identification of Potential Biomarkers in Hepatitis B Virus-Related Hepatocellular Carcinoma by Bioinformatics Analysis.

Zeng Xian-Chang XC Zhang Lu L Liao Wen-Jun WJ Ao Lu L Lin Ze-Man ZM Kang Wen W Chen Wan-Nan WN Lin Xu X

Frontiers in genetics 20200930

Hepatocellular carcinoma (HCC) is one of the most lethal cancers globally. Hepatitis B virus (HBV) infection might cause chronic hepatitis and cirrhosis, leading to HCC. To screen prognostic genes and therapeutic targets for HCC by bioinformatics analysis and determine the mechanisms underlying HBV-related HCC, three high-throughput RNA-seq based raw datasets, namely GSE25599, GSE77509, and GSE94660, were obtained from the Gene Expression Omnibus database, and one RNA-seq raw dataset was acquire ...[more]

PMID: 33193629

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Project description:Hepatocellular carcinoma (HCC) is a malignant tumor with high morbidity and mortality, which makes the prognostic prediction challenging. Angiogenesis appears to be of critical importance in the progression and metastasis of HCC. Some of the angiogenesis-related genes promote this process, while other anti-angiogenesis genes suppress tumor growth and metastasis. Therefore, the comprehensive prognostic value of multiple angiogenesis-related genes in HCC needs to be further clarified. In this study, the mRNA expression profile of HCC patients and the corresponding clinical data were acquired from multiple public databases. Univariate Cox regression analysis was utilized to screen out differentially expressed angiogenesis-related genes with prognostic value. A multigene signature was established with the least absolute shrinkage and selection operator Cox regression in the Cancer Genome Atlas cohort, and validated through an independent cohort. The results suggested that a total of 16 differentially expressed genes (DEGs) were associated with overall survival (OS) and a 7-gene signature was constructed. The risk score of each patient was calculated using this signature, the median value of which was used to divide these patients into a high-risk group and a low-risk group. Compared with the low-risk group, the patients in the high-risk group had a poor prognosis. The risk score was an independent predictor for OS through multivariate Cox regression analysis. Then, unsupervised learning was used to verify the validity of this 7-gene signature. A nomogram by further integrating clinical information and the prognostic signature was utilized to predict prognostic risk and individual OS. Functional enrichment analyses demonstrated that these DEGs were enriched in the pathways of cell proliferation and mitosis, and the immune cell infiltration was significantly different between the two risk groups. In summary, a novel angiogenesis-related genes signature could be used to predict the prognosis of HCC and for targeted therapy.

Project description: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.

Project description:Circulating microRNAs are deregulated in liver fibrosis and hepatocellular carcinoma (HCC) and are candidate biomarkers. This study investigated the potential of serum microRNAs; miR-19a, miR-296, miR-130a, miR-195, miR-192, miR-34a, and miR-146a as early diagnostic biomarkers for hepatitis C virus (HCV)-related HCC. As how these microRNAs change during liver fibrosis progression is not clear, we explored their serum levels during fibrosis progression in HCV-associated chronic liver disease (CLD) and if they could serve as non-invasive biomarkers for fibrosis progression to HCC. 112 Egyptian HCV-HCC patients, 125 non-malignant HCV-CLD patients, and 42 healthy controls were included. CLD patients were subdivided according to Metavir fibrosis-scoring. Serum microRNAs were measured by qRT-PCR custom array. Serum microRNAs were deregulated in HCC versus controls, and except miR-130a, they were differentially expressed between HCC and CLD or late fibrosis (F3-F4) subgroup. Serum microRNAs were not significantly different between individual fibrosis-stages or between F1-F2 (early/moderate fibrosis) and F3-F4. Only miR-19a was significantly downregulated from liver fibrosis (F1-F3) to cirrhosis (F4) to HCC. Individual microRNAs discriminated HCC from controls, and except miR-130a, they distinguished HCC from CLD or F3-F4 patients by receiver-operating-characteristic analysis. Multivariate logistic analysis revealed a panel of four microRNAs (miR-19a, miR-195, miR-192, and miR-146a) with high diagnostic accuracy for HCC (AUC = 0.946). The microRNA panel also discriminated HCC from controls (AUC = 0.949), CLD (AUC = 0.945), and F3-F4 (AUC = 0.955). Studied microRNAs were positively correlated in HCC group. miR-19a and miR-34a were correlated with portal vein thrombosis and HCC staging scores, respectively. In conclusion, studied microRNAs, but not miR-130a, could serve as potential early biomarkers for HCC in high-risk groups, with miR-19a as a biomarker for liver fibrosis progression to cirrhosis to HCC. We identified a panel of four serum microRNAs with high accuracy in HCC diagnosis. Additional studies are required to confirm this panel and test its prognostic significance.

Dataset Information

Screening and Identification of Potential Biomarkers in Hepatitis B Virus-Related Hepatocellular Carcinoma by Bioinformatics Analysis.

Publications

Screening and Identification of Potential Biomarkers in Hepatitis B Virus-Related Hepatocellular Carcinoma by Bioinformatics Analysis.

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