Identifying the novel key genes in renal cell carcinoma by bioinformatics analysis and cell experiments
Abstract Background Although major driver gene have been identified, the complex molecular heterogeneity of renal cell cancer (RCC) remains unclear. Therefore, more relevant genes need to be identified to explain the pathogenesis of renal cancer. Methods Microarray datasets GSE781, GSE6344, GSE53000...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2020-07-01
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| Series: | Cancer Cell International |
| Online Access: | http://link.springer.com/article/10.1186/s12935-020-01405-6 |
| _version_ | 1831705325367984128 |
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| author | Yeda Chen Di Gu Yaoan Wen Shuxin Yang Xiaolu Duan Yongchang Lai Jianan Yang Daozhang Yuan Aisha Khan Wenqi Wu Guohua Zeng |
| author_facet | Yeda Chen Di Gu Yaoan Wen Shuxin Yang Xiaolu Duan Yongchang Lai Jianan Yang Daozhang Yuan Aisha Khan Wenqi Wu Guohua Zeng |
| author_sort | Yeda Chen |
| collection | DOAJ |
| description | Abstract Background Although major driver gene have been identified, the complex molecular heterogeneity of renal cell cancer (RCC) remains unclear. Therefore, more relevant genes need to be identified to explain the pathogenesis of renal cancer. Methods Microarray datasets GSE781, GSE6344, GSE53000 and GSE68417 were downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified by employing GEO2R tool, and function enrichment analyses were performed by using DAVID. The protein-protein interaction network (PPI) was constructed and the module analysis was performed using STRING and Cytoscape. Survival analysis was performed using GEPIA. Differential expression was verified in Oncomine. Cell experiments (cell viability assays, transwell migration and invasion assays, wound healing assay, flow cytometry) were utilized to verify the roles of the hub genes on the proliferation of kidney cancer cells (A498 and OSRC-2 cell lines). Results A total of 215 DEGs were identified from four datasets. Six hub gene (SUCLG1, PCK2, GLDC, SLC12A1, ATP1A1, PDHA1) were identified and the overall survival time of patients with RCC were significantly shorter. The expression levels of these six genes were significantly decreased in six RCC cell lines(A498, OSRC-2, 786- O, Caki-1, ACHN, 769-P) compared to 293t cell line. The expression level of both mRNA and protein of these genes were downregulated in RCC samples compared to those in paracancerous normal tissues. Cell viability assays showed that overexpressions of SUCLG1, PCK2, GLDC significantly decreased proliferation of RCC. Transwell migration, invasion, wound healing assay showed overexpression of three genes(SUCLG1, PCK2, GLDC) significantly inhibited the migration, invasion of RCC. Flow cytometry analysis showed that overexpression of three genes(SUCLG1, PCK2, GLDC) induced G1/S/G2 phase arrest of RCC cells. Conclusion Based on our current findings, it is concluded that SUCLG1, PCK2, GLDC may serve as a potential prognostic marker of RCC. |
| first_indexed | 2024-12-20T16:17:56Z |
| format | Article |
| id | doaj.art-e93c91fb02434cb5becfe41926ac2477 |
| institution | Directory Open Access Journal |
| issn | 1475-2867 |
| language | English |
| last_indexed | 2024-12-20T16:17:56Z |
| publishDate | 2020-07-01 |
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| series | Cancer Cell International |
| spelling | doaj.art-e93c91fb02434cb5becfe41926ac24772022-12-21T19:33:44ZengBMCCancer Cell International1475-28672020-07-0120111610.1186/s12935-020-01405-6Identifying the novel key genes in renal cell carcinoma by bioinformatics analysis and cell experimentsYeda Chen0Di Gu1Yaoan Wen2Shuxin Yang3Xiaolu Duan4Yongchang Lai5Jianan Yang6Daozhang Yuan7Aisha Khan8Wenqi Wu9Guohua Zeng10Department of Urology, Minimally Invasive Surgery Center, The First Affiliated Hospital of Guangzhou Medical University, Guangdong Key Laboratory of UrologyDepartment of Urology, Minimally Invasive Surgery Center, The First Affiliated Hospital of Guangzhou Medical University, Guangdong Key Laboratory of UrologyDepartment of Urology, Minimally Invasive Surgery Center, The First Affiliated Hospital of Guangzhou Medical University, Guangdong Key Laboratory of UrologyDepartment of Urology, Minimally Invasive Surgery Center, The First Affiliated Hospital of Guangzhou Medical University, Guangdong Key Laboratory of UrologyDepartment of Urology, Minimally Invasive Surgery Center, The First Affiliated Hospital of Guangzhou Medical University, Guangdong Key Laboratory of UrologyDepartment of Urology, Minimally Invasive Surgery Center, The First Affiliated Hospital of Guangzhou Medical University, Guangdong Key Laboratory of UrologyDepartment of Urology, Affiliated Cancer Hospital and Institute of Guangzhou Medical UniversityDepartment of Urology, Affiliated Cancer Hospital and Institute of Guangzhou Medical UniversityDepartment of Family Medicine, Yunshan Medical HospitalDepartment of Urology, Minimally Invasive Surgery Center, The First Affiliated Hospital of Guangzhou Medical University, Guangdong Key Laboratory of UrologyDepartment of Urology, Minimally Invasive Surgery Center, The First Affiliated Hospital of Guangzhou Medical University, Guangdong Key Laboratory of UrologyAbstract Background Although major driver gene have been identified, the complex molecular heterogeneity of renal cell cancer (RCC) remains unclear. Therefore, more relevant genes need to be identified to explain the pathogenesis of renal cancer. Methods Microarray datasets GSE781, GSE6344, GSE53000 and GSE68417 were downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified by employing GEO2R tool, and function enrichment analyses were performed by using DAVID. The protein-protein interaction network (PPI) was constructed and the module analysis was performed using STRING and Cytoscape. Survival analysis was performed using GEPIA. Differential expression was verified in Oncomine. Cell experiments (cell viability assays, transwell migration and invasion assays, wound healing assay, flow cytometry) were utilized to verify the roles of the hub genes on the proliferation of kidney cancer cells (A498 and OSRC-2 cell lines). Results A total of 215 DEGs were identified from four datasets. Six hub gene (SUCLG1, PCK2, GLDC, SLC12A1, ATP1A1, PDHA1) were identified and the overall survival time of patients with RCC were significantly shorter. The expression levels of these six genes were significantly decreased in six RCC cell lines(A498, OSRC-2, 786- O, Caki-1, ACHN, 769-P) compared to 293t cell line. The expression level of both mRNA and protein of these genes were downregulated in RCC samples compared to those in paracancerous normal tissues. Cell viability assays showed that overexpressions of SUCLG1, PCK2, GLDC significantly decreased proliferation of RCC. Transwell migration, invasion, wound healing assay showed overexpression of three genes(SUCLG1, PCK2, GLDC) significantly inhibited the migration, invasion of RCC. Flow cytometry analysis showed that overexpression of three genes(SUCLG1, PCK2, GLDC) induced G1/S/G2 phase arrest of RCC cells. Conclusion Based on our current findings, it is concluded that SUCLG1, PCK2, GLDC may serve as a potential prognostic marker of RCC.http://link.springer.com/article/10.1186/s12935-020-01405-6 |
| spellingShingle | Yeda Chen Di Gu Yaoan Wen Shuxin Yang Xiaolu Duan Yongchang Lai Jianan Yang Daozhang Yuan Aisha Khan Wenqi Wu Guohua Zeng Identifying the novel key genes in renal cell carcinoma by bioinformatics analysis and cell experiments Cancer Cell International |
| title | Identifying the novel key genes in renal cell carcinoma by bioinformatics analysis and cell experiments |
| title_full | Identifying the novel key genes in renal cell carcinoma by bioinformatics analysis and cell experiments |
| title_fullStr | Identifying the novel key genes in renal cell carcinoma by bioinformatics analysis and cell experiments |
| title_full_unstemmed | Identifying the novel key genes in renal cell carcinoma by bioinformatics analysis and cell experiments |
| title_short | Identifying the novel key genes in renal cell carcinoma by bioinformatics analysis and cell experiments |
| title_sort | identifying the novel key genes in renal cell carcinoma by bioinformatics analysis and cell experiments |
| url | http://link.springer.com/article/10.1186/s12935-020-01405-6 |
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