Integrated bioinformatics analysis for the screening of hub genes and therapeutic drugs in ovarian cancer
Abstract Background Ovarian cancer (OC) ranks fifth as a cause of gynecological cancer-associated death globally. Until now, the molecular mechanisms underlying the tumorigenesis and prognosis of OC have not been fully understood. This study aims to identify hub genes and therapeutic drugs involved...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2020-01-01
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| Series: | Journal of Ovarian Research |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13048-020-0613-2 |
| _version_ | 1797967774719410176 |
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| author | Dan Yang Yang He Bo Wu Yan Deng Nan Wang Menglin Li Yang Liu |
| author_facet | Dan Yang Yang He Bo Wu Yan Deng Nan Wang Menglin Li Yang Liu |
| author_sort | Dan Yang |
| collection | DOAJ |
| description | Abstract Background Ovarian cancer (OC) ranks fifth as a cause of gynecological cancer-associated death globally. Until now, the molecular mechanisms underlying the tumorigenesis and prognosis of OC have not been fully understood. This study aims to identify hub genes and therapeutic drugs involved in OC. Methods Four gene expression profiles (GSE54388, GSE69428, GSE36668, and GSE40595) were downloaded from the Gene Expression Omnibus (GEO), and the differentially expressed genes (DEGs) in OC tissues and normal tissues with an adjusted P-value < 0.05 and a |log fold change (FC)| > 1.0 were first identified by GEO2R and FunRich software. Next, Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) analyses were performed for functional enrichment analysis of these DEGs. Then, the hub genes were identified by the cytoHubba plugin and the other bioinformatics approaches including protein-protein interaction (PPI) network analysis, module analysis, survival analysis, and miRNA-hub gene network construction was also performed. Finally, the GEPIA2 and DGIdb databases were utilized to verify the expression levels of hub genes and to select the candidate drugs for OC, respectively. Results A total of 171 DEGs were identified, including 114 upregulated and 57 downregulated DEGs. The results of the GO analysis indicated that the upregulated DEGs were mainly involved in cell division, nucleus, and protein binding, whereas the biological functions showing enrichment in the downregulated DEGs were mainly negative regulation of transcription from RNA polymerase II promoter, protein complex and apicolateral plasma membrane, and glycosaminoglycan binding. As for the KEGG-pathway, the upregulated DEGs were mainly associated with metabolic pathways, biosynthesis of antibiotics, biosynthesis of amino acids, cell cycle, and HTLV-I infection. Additionally, 10 hub genes (KIF4A, CDC20, CCNB2, TOP2A, RRM2, TYMS, KIF11, BIRC5, BUB1B, and FOXM1) were identified and survival analysis of these hub genes showed that OC patients with the high-expression of CCNB2, TYMS, KIF11, KIF4A, BIRC5, BUB1B, FOXM1, and CDC20 were statistically more likely to have poorer progression free survival. Meanwhile, the expression levels of the hub genes based on GEPIA2 were in accordance with those based on GEO. Finally, DGIdb database was used to identify 62 small molecules as the potentially targeted drugs for OC treatment. Conclusions In summary, the data may produce new insights regarding OC pathogenesis and treatment. Hub genes and candidate drugs may improve individualized diagnosis and therapy for OC in future. |
| first_indexed | 2024-04-11T02:35:21Z |
| format | Article |
| id | doaj.art-2cb1cd784e554e5194a79ce3280b9080 |
| institution | Directory Open Access Journal |
| issn | 1757-2215 |
| language | English |
| last_indexed | 2024-04-11T02:35:21Z |
| publishDate | 2020-01-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Ovarian Research |
| spelling | doaj.art-2cb1cd784e554e5194a79ce3280b90802023-01-02T20:09:12ZengBMCJournal of Ovarian Research1757-22152020-01-0113111810.1186/s13048-020-0613-2Integrated bioinformatics analysis for the screening of hub genes and therapeutic drugs in ovarian cancerDan Yang0Yang He1Bo Wu2Yan Deng3Nan Wang4Menglin Li5Yang Liu6Department of Environmental Health, School of Public Health, China Medical UniversityDepartment of Central Laboratory, The First Affiliated Hospital, China Medical UniversityDepartment of Anus and Intestine Surgery, The First Affiliated Hospital, China Medical UniversityDepartment of Environmental Health, School of Public Health, China Medical UniversityDepartment of Environmental Health, School of Public Health, China Medical UniversityDepartment of Environmental Health, School of Public Health, China Medical UniversityDepartment of Environmental Health, School of Public Health, China Medical UniversityAbstract Background Ovarian cancer (OC) ranks fifth as a cause of gynecological cancer-associated death globally. Until now, the molecular mechanisms underlying the tumorigenesis and prognosis of OC have not been fully understood. This study aims to identify hub genes and therapeutic drugs involved in OC. Methods Four gene expression profiles (GSE54388, GSE69428, GSE36668, and GSE40595) were downloaded from the Gene Expression Omnibus (GEO), and the differentially expressed genes (DEGs) in OC tissues and normal tissues with an adjusted P-value < 0.05 and a |log fold change (FC)| > 1.0 were first identified by GEO2R and FunRich software. Next, Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) analyses were performed for functional enrichment analysis of these DEGs. Then, the hub genes were identified by the cytoHubba plugin and the other bioinformatics approaches including protein-protein interaction (PPI) network analysis, module analysis, survival analysis, and miRNA-hub gene network construction was also performed. Finally, the GEPIA2 and DGIdb databases were utilized to verify the expression levels of hub genes and to select the candidate drugs for OC, respectively. Results A total of 171 DEGs were identified, including 114 upregulated and 57 downregulated DEGs. The results of the GO analysis indicated that the upregulated DEGs were mainly involved in cell division, nucleus, and protein binding, whereas the biological functions showing enrichment in the downregulated DEGs were mainly negative regulation of transcription from RNA polymerase II promoter, protein complex and apicolateral plasma membrane, and glycosaminoglycan binding. As for the KEGG-pathway, the upregulated DEGs were mainly associated with metabolic pathways, biosynthesis of antibiotics, biosynthesis of amino acids, cell cycle, and HTLV-I infection. Additionally, 10 hub genes (KIF4A, CDC20, CCNB2, TOP2A, RRM2, TYMS, KIF11, BIRC5, BUB1B, and FOXM1) were identified and survival analysis of these hub genes showed that OC patients with the high-expression of CCNB2, TYMS, KIF11, KIF4A, BIRC5, BUB1B, FOXM1, and CDC20 were statistically more likely to have poorer progression free survival. Meanwhile, the expression levels of the hub genes based on GEPIA2 were in accordance with those based on GEO. Finally, DGIdb database was used to identify 62 small molecules as the potentially targeted drugs for OC treatment. Conclusions In summary, the data may produce new insights regarding OC pathogenesis and treatment. Hub genes and candidate drugs may improve individualized diagnosis and therapy for OC in future.https://doi.org/10.1186/s13048-020-0613-2Ovarian cancerDifferentially expressed genesFunctional enrichment analysisProtein-protein interactionSurvival analysismiRNA-hub gene network |
| spellingShingle | Dan Yang Yang He Bo Wu Yan Deng Nan Wang Menglin Li Yang Liu Integrated bioinformatics analysis for the screening of hub genes and therapeutic drugs in ovarian cancer Journal of Ovarian Research Ovarian cancer Differentially expressed genes Functional enrichment analysis Protein-protein interaction Survival analysis miRNA-hub gene network |
| title | Integrated bioinformatics analysis for the screening of hub genes and therapeutic drugs in ovarian cancer |
| title_full | Integrated bioinformatics analysis for the screening of hub genes and therapeutic drugs in ovarian cancer |
| title_fullStr | Integrated bioinformatics analysis for the screening of hub genes and therapeutic drugs in ovarian cancer |
| title_full_unstemmed | Integrated bioinformatics analysis for the screening of hub genes and therapeutic drugs in ovarian cancer |
| title_short | Integrated bioinformatics analysis for the screening of hub genes and therapeutic drugs in ovarian cancer |
| title_sort | integrated bioinformatics analysis for the screening of hub genes and therapeutic drugs in ovarian cancer |
| topic | Ovarian cancer Differentially expressed genes Functional enrichment analysis Protein-protein interaction Survival analysis miRNA-hub gene network |
| url | https://doi.org/10.1186/s13048-020-0613-2 |
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