Identification of significant gene and pathways involved in HBV-related hepatocellular carcinoma by bioinformatics analysis
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-rela...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
PeerJ Inc.
2019-07-01
|
| Series: | PeerJ |
| Subjects: | |
| Online Access: | https://peerj.com/articles/7408.pdf |
| _version_ | 1797423915128061952 |
|---|---|
| author | Shucai Xie Xili Jiang Jianquan Zhang Shaowei Xie Yongyong Hua Rui Wang Yijun Yang |
| author_facet | Shucai Xie Xili Jiang Jianquan Zhang Shaowei Xie Yongyong Hua Rui Wang Yijun Yang |
| author_sort | Shucai Xie |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T07:53:34Z |
| format | Article |
| id | doaj.art-64cc962d9a404cb0a5e1c52e73ef3e07 |
| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T07:53:34Z |
| publishDate | 2019-07-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ |
| spelling | doaj.art-64cc962d9a404cb0a5e1c52e73ef3e072023-12-03T01:20:27ZengPeerJ Inc.PeerJ2167-83592019-07-017e740810.7717/peerj.7408Identification of significant gene and pathways involved in HBV-related hepatocellular carcinoma by bioinformatics analysisShucai Xie0Xili Jiang1Jianquan Zhang2Shaowei Xie3Yongyong Hua4Rui Wang5Yijun Yang6Department of Hepatobiliary Surgery, Haikou People’s Hospital/Affiliated Haikou Hospital of Xiangya Medical College, Central South University, Haikou, Hainan, ChinaDepartment of Radiology, The Second People’s Hospital of Hunan Province/Brain Hospital of Hunan Province, Changsha, ChinaDepartment of Hepatobiliary Surgery, Haikou People’s Hospital/Affiliated Haikou Hospital of Xiangya Medical College, Central South University, Haikou, Hainan, ChinaDepartment of Hepatobiliary Surgery, Haikou People’s Hospital/Affiliated Haikou Hospital of Xiangya Medical College, Central South University, Haikou, Hainan, ChinaDepartment of Hepatobiliary Surgery, Haikou People’s Hospital/Affiliated Haikou Hospital of Xiangya Medical College, Central South University, Haikou, Hainan, ChinaDepartment of Hepatobiliary Surgery, Haikou People’s Hospital/Affiliated Haikou Hospital of Xiangya Medical College, Central South University, Haikou, Hainan, ChinaDepartment of Hepatobiliary Surgery, Haikou People’s Hospital/Affiliated Haikou Hospital of Xiangya Medical College, Central South University, Haikou, Hainan, ChinaBackground 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.https://peerj.com/articles/7408.pdfHepatocellular carcinomaRobustRankAggregBioinformatic analysisDifferentially expressed genes |
| spellingShingle | Shucai Xie Xili Jiang Jianquan Zhang Shaowei Xie Yongyong Hua Rui Wang Yijun Yang Identification of significant gene and pathways involved in HBV-related hepatocellular carcinoma by bioinformatics analysis PeerJ Hepatocellular carcinoma RobustRankAggreg Bioinformatic analysis Differentially expressed genes |
| title | Identification of significant gene and pathways involved in HBV-related hepatocellular carcinoma by bioinformatics analysis |
| title_full | Identification of significant gene and pathways involved in HBV-related hepatocellular carcinoma by bioinformatics analysis |
| title_fullStr | Identification of significant gene and pathways involved in HBV-related hepatocellular carcinoma by bioinformatics analysis |
| title_full_unstemmed | Identification of significant gene and pathways involved in HBV-related hepatocellular carcinoma by bioinformatics analysis |
| title_short | Identification of significant gene and pathways involved in HBV-related hepatocellular carcinoma by bioinformatics analysis |
| title_sort | identification of significant gene and pathways involved in hbv related hepatocellular carcinoma by bioinformatics analysis |
| topic | Hepatocellular carcinoma RobustRankAggreg Bioinformatic analysis Differentially expressed genes |
| url | https://peerj.com/articles/7408.pdf |
| work_keys_str_mv | AT shucaixie identificationofsignificantgeneandpathwaysinvolvedinhbvrelatedhepatocellularcarcinomabybioinformaticsanalysis AT xilijiang identificationofsignificantgeneandpathwaysinvolvedinhbvrelatedhepatocellularcarcinomabybioinformaticsanalysis AT jianquanzhang identificationofsignificantgeneandpathwaysinvolvedinhbvrelatedhepatocellularcarcinomabybioinformaticsanalysis AT shaoweixie identificationofsignificantgeneandpathwaysinvolvedinhbvrelatedhepatocellularcarcinomabybioinformaticsanalysis AT yongyonghua identificationofsignificantgeneandpathwaysinvolvedinhbvrelatedhepatocellularcarcinomabybioinformaticsanalysis AT ruiwang identificationofsignificantgeneandpathwaysinvolvedinhbvrelatedhepatocellularcarcinomabybioinformaticsanalysis AT yijunyang identificationofsignificantgeneandpathwaysinvolvedinhbvrelatedhepatocellularcarcinomabybioinformaticsanalysis |