scholarly journals Identification of significant gene and pathways involved in HBV-related hepatocellular carcinoma by bioinformatics analysis

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7408 ◽  
Author(s):  
Shucai Xie ◽  
Xili Jiang ◽  
Jianquan Zhang ◽  
Shaowei Xie ◽  
Yongyong Hua ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Hepatitis B ◽  
Pathway Analysis ◽  
Expression Profiles ◽  
Financial Burden ◽  
Disease Free Survival ◽  
Ppi Network ◽  
Hub Genes ◽  
Bioinformatics Analyses ◽  
Extracellular Region

Background Hepatocellular carcinoma (HCC) is a common malignant tumor affecting the digestive system and causes serious financial burden worldwide. Hepatitis B virus (HBV) is the main causative agent of HCC in China. The present study aimed to investigate the potential mechanisms underlying HBV-related HCC and to identify core biomarkers by integrated bioinformatics analyses. Methods In the present study, HBV-related HCC GSE19665, GSE55092, GSE94660 and GSE121248 expression profiles were downloaded from the Gene Expression Omnibus database. These databases contain data for 299 samples, including 145 HBV-related HCC tissues and 154 non-cancerous tissues (from patients with chronic hepatitis B). The differentially expressed genes (DEGs) from each dataset were integrated and analyzed using the RobustRankAggreg (RRA) method and R software, and the integrated DEGs were identified. Subsequently, the gene ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed using the DAVID online tool, and the protein–protein interaction (PPI) network was constructed using STRING and visualized using Cytoscape software. Finally, hub genes were identified, and the cBioPortal online platform was used to analyze the association between the expression of hub genes and prognosis in HCC. Results First, 341 DEGs (117 upregulated and 224 downregulated) were identified from the four datasets. Next, GO analysis showed that the upregulated genes were mainly involved in cell cycle, mitotic spindle, and adenosine triphosphate binding. The majority of the downregulated genes were involved in oxidation reduction, extracellular region, and electron carrier activity. Signaling pathway analysis showed that the integrated DEGs shared common pathways in retinol metabolism, drug metabolism, tryptophan metabolism, caffeine metabolism, and metabolism of xenobiotics by cytochrome P450. The integrated DEG PPI network complex comprised 288 nodes, and two important modules with high degree were detected using the MCODE plug-in. The top ten hub genes identified from the PPI network were SHCBP1, FOXM1, KIF4A, ANLN, KIF15, KIF18A, FANCI, NEK2, ECT2, and RAD51AP1. Finally, survival analysis revealed that patients with HCC showing altered ANLN and KIF18A expression profiles showed worse disease-free survival. Nonetheless, patients with FOXM1, NEK2, RAD51AP1, ANLN, and KIF18A alterations showed worse overall survival. Conclusions The present study identified key genes and pathways involved in HBV-related HCC, which improved our understanding of the mechanisms underlying the development and recurrence of HCC and identified candidate targets for the diagnosis and treatment of HBV-related HCC.

scholarly journals Identification of Key Genes and Prognostic Value Analysis in Hepatocellular Carcinoma by Integrated Bioinformatics Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-21 ◽  
Author(s):  
Meng Wang ◽  
Licheng Wang ◽  
Shusheng Wu ◽  
Dongsheng Zhou ◽  
Xianming Wang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Expression Profiles ◽  
Cyclin B1 ◽  
Ppi Network ◽  
Hub Genes ◽  
Altered Expression ◽  
Value Analysis ◽  
Kaplan Meier ◽  
Key Genes

Emerging evidence indicates that various functional genes with altered expression are involved in the tumor progression of human cancers. This study is aimed at identifying novel key genes that may be used for hepatocellular carcinoma (HCC) diagnosis, prognosis, and targeted therapy. This study included 3 expression profiles (GSE45267, GSE74656, and GSE84402), which were obtained from the Gene Expression Omnibus (GEO). GEO2R was used to analyze the differentially expressed genes (DEGs) between HCC and normal samples. The functional and pathway enrichment analysis was performed by the Database for Annotation, Visualization and Integrated Discovery. A protein-protein interaction (PPI) network of the identified DEGs was constructed using the Search Tool for the Retrieval of Interacting Gene, and hub genes were identified. ONCOMINE and CCLE databases were used to verify the expression of the hub genes in HCC tissues and cells. Kaplan-Meier plotter was used to assess the effects of the hub genes on the overall survival of HCC patients. A total of 99 DEGs were identified from the 3 expression profiles. These DEGs were enriched with functional processes and pathways related to HCC pathogenesis. From the PPI network, 5 hub genes were identified. The expression of the 5 hub genes was all upregulated in HCC tissues and cells compared with the control tissues and cells. Kaplan-Meier survival curves indicated that high expression of cyclin-dependent kinase (CDK1), cyclin B1 (CCNB1), cyclin B2 (CCNB2), MAD2 mitotic arrest deficient-like 1 (MAD2L1), and topoisomerase IIα (TOP2A) predicted poor overall survival in HCC patients (all log-rank P<0.01). These results revealed that the DEGs may serve as candidate key genes during HCC pathogenesis. The 5 hub genes, including CDK1, CCNB1, CCNB2, MAD2L1, and TOP2A, may serve as promising prognostic biomarkers in HCC.

scholarly journals Screening and Functional Prediction of Key Candidate Genes in Hepatitis B Virus-Associated Hepatocellular Carcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Xia Chen ◽  
Ling Liao ◽  
Yuwei Li ◽  
Hengliu Huang ◽  
Qing Huang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Hepatitis B Virus ◽  
Hepatitis B ◽  
Pathway Analysis ◽  
Kegg Pathway ◽  
Metabolic Process ◽  
Hub Genes ◽  
Kegg Pathway Analysis ◽  
B Virus

Background. The molecular mechanism by which hepatitis B virus (HBV) induces hepatocellular carcinoma (HCC) is still unknown. The genomic expression profile and bioinformatics methods were used to investigate the potential pathogenesis and therapeutic targets for HBV-associated HCC (HBV-HCC). Methods. The microarray dataset GSE55092 was downloaded from the Gene Expression Omnibus (GEO) database. The data was analyzed by the bioinformatics software to find differentially expressed genes (DEGs). Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, ingenuity pathway analysis (IPA), and protein-protein interaction (PPI) network analysis were then performed on DEGs. The hub genes were identified using Centiscape2.2 and Molecular Complex Detection (MCODE) in the Cytoscape software (Cytoscape_v3.7.2). The survival data of these hub genes was downloaded from the Gene Expression Profiling Interactive Analysis (GEPIA). Results. A total of 2264 mRNA transcripts were differentially expressed, including 764 upregulated and 1500 downregulated in tumor tissues. GO analysis revealed that these DEGs were related to the small-molecule metabolic process, xenobiotic metabolic process, and cellular nitrogen compound metabolic process. KEGG pathway analysis revealed that metabolic pathways, complement and coagulation cascades, and chemical carcinogenesis were involved. Diseases and biofunctions showed that DEGs were mainly associated with the following diseases or biological function abnormalities: cancer, organismal injury and abnormalities, gastrointestinal disease, and hepatic system disease. The top 10 upstream regulators were predicted to be activated or inhibited by Z-score and identified 25 networks. The 10 genes with the highest degree of connectivity were defined as the hub genes. Cox regression revealed that all the 10 genes (CDC20, BUB1B, KIF11, TTK, EZH2, ZWINT, NDC80, TPX2, MELK, and KIF20A) were related to the overall survival. Conclusion. Our study provided a registry of genes that play important roles in regulating the development of HBV-HCC, assisting us in understanding the molecular mechanisms that underlie the carcinogenesis and progression of HCC.

Identification of Potential Biomarkers in Synovial Tissue of Rheumatoid Arthritis via Integrated Microarray Analysis. Identification of Biomarkers of Rheumatoid Arthritis

2021 ◽  
Author(s):  
Li Tao ◽  
ChaoLiang Xiong ◽  
Li Xue
Keyword(s):  
Rheumatoid Arthritis ◽  
Gene Expression ◽  
Pathway Analysis ◽  
Expression Profiles ◽  
Diagnostic Value ◽  
Functional Enrichment ◽  
Venn Diagram ◽  
Ppi Network ◽  
Hub Genes ◽  
Gene Markers

Abstract Background: Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by synovitis and subsequent destruction of cartilage and bone. This study aimed to explore RA-related gene markers and the underlying molecular mechanism.Material and Methods: The expression profiles of GSE77298, GSE55235 and GSE12021 were obtained from the Gene Expression Omnibus database. Then, the differential gene expression analysis was conducted between GSE77298 and GSE55235 datasets. Limma package and a Venn diagram were utilized to screen the overlapping differentially expressed genes (DEGs), and Functional enrichment and pathway analysis were performed by using DAVID database. Subsequently, a protein-protein interaction (PPI) network was established, and candidate hub genes were recognized by using STRING and Cytoscape software. Finally, another dataset (GSE12021) was used for the validation of diagnostic value of the candidate hub genes and to identify real hub genes by using receiver operating characteristic (ROC) curves.Results: A total of 385 DEGs were detected, which include 19 downregulated genes and 366 upregulated genes. GO and KEGG pathway analysis showed that DEGs was mainly enriched in various immune and inflammatory response-related functions and pathways. The PPI network was composed of 374 nodes and 767 edges. A total of 8 real hub genes (HLA-DRA, HLA-DRB1, LCK, VAV1, HLA-DPA1, HLA-DPB1, C3AR1 and CD3D) which displayed an excellent diagnostic value for RA were identified.Conclusion: these findings may provide novel and reliable biomarkers for RA, which have some interesting implications for early diagnosis, prognosis and targeted therapy.

scholarly journals Network analyses of circular RNAs expression profiles and their hub genes in pancreatic ductal adenocarcinoma

2019 ◽  
Author(s):  
Yanyan Tang ◽  
Ping Zhang
Keyword(s):  
Gene Expression ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Signaling Pathway ◽  
Pathway Analysis ◽  
Kegg Pathway ◽  
Expression Profiles ◽  
Ductal Adenocarcinoma ◽  
Ppi Network ◽  
Hub Genes ◽  
Kegg Pathway Analysis

Abstract Pancreatic ductal adenocarcinoma (PDAC) is one of the most common malignant tumor in digestive system. CircRNAs involve in lots of biological processes through interacting with miRNAs and their targeted mRNA. We obtained the circRNA gene expression profiles from Gene Expression Omnibus (GEO) and identified differentially expressed genes (DEGs) between PDAC samples and paracancerous tissues. Bioinformatics analyses, including GO analysis, KEGG pathway analysis and PPI network analysis, were conducted for further investigation. We also constructed circRNA‑microRNA-mRNA co-expression network. A total 291 differentially expressed circRNAs were screened out. The GO enrichment analysis revealed that up-regulated DEGs were mainly involved metabolic process, biological regulation, and gene expression, and down-regulated DEGs were involved in cell communication, single-organism process, and signal transduction. The KEGG pathway analysis, the upregulated circRNAs were enriched cGMP-PKG signaling pathway, and HTLV-I infection, while the downregulated circRNAs were enriched in protein processing in endoplasmic reticulum, insulin signaling pathway, regulation of actin cytoskeleton, etc. Four genes were identified from PPI network as both hub genes and module genes, and their circRNA‑miRNA-mRNA regulatory network also be constructed. Our study indicated possible involvement of dysregulated circRNAs in the development of PDAC and promoted our understanding of the underlying molecular mechanisms.

scholarly journals Combining Bioinformatics and Experiments to Identify and Verify Gene Prognostic Markers in Hepatocellular Carcinoma

2021 ◽  
pp. 1-12
Author(s):  
Li Luo ◽  
Rong Wang ◽  
Liaoyun Zhang ◽  
Piao Zhang ◽  
Dongmei Tian ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Proportional Hazards ◽  
Malignant Tumors ◽  
Expression Profiles ◽  
Disease Free Survival ◽  
Enrichment Analysis ◽  
Cox Proportional Hazards ◽  
Gene Set Enrichment Analysis ◽  
Hub Genes

Background: Hepatocellular Carcinoma (HCC) is one of the highly malignant tumors threatening human health. The current research aimed to identify potential prognostic gene biomarkers for HCC. Materials and Methods: Microarray data of gene expression profiles of HCC from GEO were downloaded. After screening overlapping differentially expressed genes (DEGs) by R software. The STRING database and Cytoscape were used to identify hub genes. Cox proportional hazards regression was performed to screen the potential prognostic genes. Moreover, quantitative real-time PCR analyses were performed to detect the expression of ANLN in liver cancer cells and tissues. Finally, its possible pathways and functions were predicted using gene set enrichment analysis (GSEA). Result: A total of 566 DEGs were obtained from the overlapping analysis of three mRNA microarray dataset. Six key hub genes including RACGAP1, KIF20, DLGAP5, CDK1, BUB1B and ANLN, were associated with poor prognosis of patients with HCC. Higher expression of ANLN was associated with reduced overall survival and disease-free survival in patients with HCC. Multivariate analysis revealed that ANLN expression was an independent risk factor affecting overall survival. RT-PCR and Western blot analysis further demonstrated that ANLN expression was increased in HCC compared with patient-matched adjacent normal tissues. Notably, Gene enrichment analysis revealed that DEGs in ANLN-high patients were enriched in cell cycle, DNA duplication and p53 signaling pathway. Conclusion: The high expression of RACGAP1, KIF20, DLGAP5, CDK1, BUB1B and ANLN might be poor prognostic biomarkers in HCC patients, and may help to individualize the management of HCC.

scholarly journals Bioinformatic Identification and Validation of Key Genes and Biological Pathways Involved in Hepatitis B Virus-related Hepatocellular Carcinoma

2020 ◽  
Author(s):  
Guan-Hua Ren ◽  
Fan-Biao Mei ◽  
Chao-Jun Zhang ◽  
Dong-Mei Cai ◽  
Long Long ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Hepatitis B Virus ◽  
Hepatitis B ◽  
Disease Free Survival ◽  
The Cancer Genome Atlas ◽  
Hub Genes ◽  
Chronic Hepatitis B Virus ◽  
B Virus ◽  
Key Genes

Abstract Objectives: Hepatocellular carcinoma (HCC) is a common malignant tumor severely scathing human health. As we all know, one of the main risk factors of HCC is chronic hepatitis B virus (HBV) infection, which involved in the oncogenesis of HCC through direct and indirect mechanisms such as inflammatory injury, integration into the host genome and interaction with some special target genes. This study aimed to identify these key genes potentially associated with HBV-related HCC by bioinformatics analyses.Results: Total of 320 DEGs was identified between HBV-related HCC tissue samples and adjacent normal samples. These DGEs were strongly associated with several biological processes, such as retinol metabolism and steroid hormone biosynthesis. A PPI network was constructed and top six hub genes, including CDK1, CCNB1, CDC20, CDKN3, HMMR and MKI67, were determined. GEPIA online tool analysis validated the six key hub genes had the same expression trend as predicted in The Cancer Genome Atlas (TCGA) datasets. The overall survival and disease-free survival reflected that high expression of CDK1, CDC20, HMMR, MKI67 and CCNB1 significantly predicted poor prognosis, whereas CDKN3 expression has no statistical differences in overall survival.Conclusion: The present study identified key genes and pathways involved in HBV-related HCC, which will improve our understanding of the mechanisms underlying the development and recurrence of HCC. The six identified genes might be potential biomarkers for the diagnosis and treatment of HBV-related HCC.

scholarly journals Screening Hub Genes as Prognostic Biomarkers of Hepatocellular Carcinoma by Bioinformatics Analysis

2019 ◽  
Vol 28 (1_suppl) ◽  
pp. 76S-86S ◽  
Author(s):  
Zengyuan Zhou ◽  
Yuzheng Li ◽  
Haiyue Hao ◽  
Yuanyuan Wang ◽  
Zihao Zhou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Expression Profiles ◽  
Gene Expression Omnibus ◽  
Prognostic Biomarkers ◽  
Ppi Network ◽  
Hub Genes ◽  
P53 Signaling

Hepatocellular carcinoma (HCC) is a widespread, common type of cancer in Asian countries, and the need for biomarker-matched molecularly targeted therapy for HCC has been increasingly recognized. However, the effective treatment for HCC is unclear. Therefore, identifying additional hub genes and pathways as novel prognostic biomarkers for HCC is necessary. In this study, the expression profiles of GSE121248, GSE45267 and GSE84402 were obtained from the Gene Expression Omnibus (GEO), including 132 HCC and 90 noncancerous liver tissues. Differentially expressed genes (DEGs) between HCC and noncancerous samples were identified by GEO2 R and Venn diagrams. In total, 109 DEGs were identified in these datasets, including 24 upregulated genes and 85 downregulated genes. Subsequently, Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) preliminary analyses of the DEGs were performed using DAVID. The protein–protein interaction (PPI) network of the DEGs was constructed with the Search Tool for the Retrieval of Interacting Genes (STRING) and visualized in Cytoscape. Module analysis of the PPI network was performed using MCODE to get hub genes. Moreover, the influence of the hub genes on overall survival was determined with Kaplan–Meier plotter. All hub genes were analyzed by Gene Expression Profiling Interactive Analysis (GEPIA) and KEGG. Overall, the hub genes DTL, CDK1, CCNB1, RACGAP1, ECT2, NEK2, BUB1B, PBK, TOP2A, ASPM, HMMR, RRM2, CDKN3, PRC1, and ANLN were upregulated in HCC, and the survival rate was lower for HCC with increased expression of these hub genes. CCNB1, CDK1, and RRM2 were enriched in the p53 signaling pathway, and CCNB1, CDK1, and BUB1B were enriched in the cell cycle. In brief, we screened 15 hub genes and pathways to identify potential prognostic markers for HCC treatment. However, the specific occurrence and development of HCC with expression of the hub genes should be verified in vivo and in vitro.

scholarly journals The Identification of Core Gene Expression Signature in Hepatocellular Carcinoma

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Ning Li ◽  
Ling Li ◽  
Yongshun Chen
Keyword(s):  
Oxidative Stress ◽  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Cell Cycle ◽  
Financial Burden ◽  
Gene Expression Signature ◽  
Core Gene ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction

Hepatocellular carcinoma (HCC) is one of the most common malignancies, which causes serious financial burden worldwide. This study aims to investigate the potential mechanisms contributing to HCC and identify core biomarkers. The HCC gene expression profile GSE41804 was picked out to analyze the differentially expressed genes (DEGs). Gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were carried out using DAVID. We constructed a protein-protein interaction (PPI) network to visualize interactions of the DEGs. The survival analysis of these hub genes was conducted to evaluate their potential effects on HCC. In this analysis, 503 DEGs were captured (360 downregulated genes and 143 upregulated genes). Meanwhile, 15 hub genes were identified. GO analysis showed that the DEGs were mainly enriched in oxidative stress, cell cycle, and extracellular structure. KEGG analysis suggested the DEGs were enriched in the absorption, metabolism, and cell cycle pathway. PPI network disclosed that the top3 modules were mainly enriched in cell cycle, oxidative stress, and liver detoxification. In conclusion, our analysis uncovered that the alterations of oxidative stress and cell cycle are two major signatures of HCC. TOP2A, CCNB1, and KIF4A might promote the development of HCC, especially in proliferation and differentiation, which could be novel biomarkers and targets for diagnosis and treatment of HCC.

scholarly journals Transcriptomic analysis and Novel gene pair-based signatures for Hepatitis B-related hepatocellular carcinoma

2021 ◽  
Author(s):  
Biao Song ◽  
Yulin Wang ◽  
Shaocong Mo
Keyword(s):  
Hepatocellular Carcinoma ◽  
Hepatitis B ◽  
Molecular Mechanism ◽  
Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Prognostic Models ◽  
Prognostic Signature ◽  
Hub Genes ◽  
Good Efficiency ◽  
Diagnostic Signature

Abstract Background: Hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) misses the opportunity for surgery because it is not detected early. The molecular mechanism of hepatitis B-related liver cancer needs further understanding, and effective diagnostic and prognostic models are in urgent need. Methods: Expression profiles from the Cancer Genome Atlas (TCGA) Liver Hepatocellular Carcinoma (LIHC), GSE121248, GSE94660 GSE76724 from Gene Expression Omnibus (GEO) database were obtained. Differentially expressed genes (DEGs) between normal and tumor HBV-related HCC samples based on GSE121248 and GSE94660. Gene pairs are generated by comparing the expression levels of every two DEGs. A diagnostic signature of pairs of DEGs was built using cross-validation Lasso and Best Subset Selection regression. Hub genes and significant modules were screened by Cytoscape, and potential drugs were predicted by DGIdb. A prognostic signature was established and xCell and ssGSEA were utilized to reveal the cell composition and cancer hallmarks to get an elucidation for the risk.Results: 457 DEGs were screened. A powerful diagnostic signature of 2 pairs of DEGs was built and validated in TCGA-LIHC and GEO datasets repeatedly with assured performance. 10 Hub genes were found and fostamatinib was predicted to have potential therapeutic effect on HBV-related HCC. A prognostic signature with good efficiency (Log-rank P value<0.05, AUC=93.1%) were established, with stromal score and several hallmarks related to the risk Conclusion: Taken together, the study provided sight into the molecular mechanism as well as a novel strategy for the early diagnosis and prognosis for HBV-related HCC.

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