Cargando…
Identifying the novel key genes in renal cell carcinoma by bioinformatics analysis and cell experiments
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 GS...
Autores principales: | , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7372855/ https://www.ncbi.nlm.nih.gov/pubmed/32699530 http://dx.doi.org/10.1186/s12935-020-01405-6 |
_version_ | 1783561396280098816 |
---|---|
author | Chen, Yeda Gu, Di Wen, Yaoan Yang, Shuxin Duan, Xiaolu Lai, Yongchang Yang, Jianan Yuan, Daozhang Khan, Aisha Wu, Wenqi Zeng, Guohua |
author_facet | Chen, Yeda Gu, Di Wen, Yaoan Yang, Shuxin Duan, Xiaolu Lai, Yongchang Yang, Jianan Yuan, Daozhang Khan, Aisha Wu, Wenqi Zeng, Guohua |
author_sort | Chen, Yeda |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7372855 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-73728552020-07-21 Identifying the novel key genes in renal cell carcinoma by bioinformatics analysis and cell experiments Chen, Yeda Gu, Di Wen, Yaoan Yang, Shuxin Duan, Xiaolu Lai, Yongchang Yang, Jianan Yuan, Daozhang Khan, Aisha Wu, Wenqi Zeng, Guohua Cancer Cell Int Primary Research 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. BioMed Central 2020-07-21 /pmc/articles/PMC7372855/ /pubmed/32699530 http://dx.doi.org/10.1186/s12935-020-01405-6 Text en © The Author(s) 2020, corrected publication 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Primary Research Chen, Yeda Gu, Di Wen, Yaoan Yang, Shuxin Duan, Xiaolu Lai, Yongchang Yang, Jianan Yuan, Daozhang Khan, Aisha Wu, Wenqi Zeng, Guohua Identifying the novel key genes in renal cell carcinoma by bioinformatics analysis and cell experiments |
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 |
topic | Primary Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7372855/ https://www.ncbi.nlm.nih.gov/pubmed/32699530 http://dx.doi.org/10.1186/s12935-020-01405-6 |
work_keys_str_mv | AT chenyeda identifyingthenovelkeygenesinrenalcellcarcinomabybioinformaticsanalysisandcellexperiments AT gudi identifyingthenovelkeygenesinrenalcellcarcinomabybioinformaticsanalysisandcellexperiments AT wenyaoan identifyingthenovelkeygenesinrenalcellcarcinomabybioinformaticsanalysisandcellexperiments AT yangshuxin identifyingthenovelkeygenesinrenalcellcarcinomabybioinformaticsanalysisandcellexperiments AT duanxiaolu identifyingthenovelkeygenesinrenalcellcarcinomabybioinformaticsanalysisandcellexperiments AT laiyongchang identifyingthenovelkeygenesinrenalcellcarcinomabybioinformaticsanalysisandcellexperiments AT yangjianan identifyingthenovelkeygenesinrenalcellcarcinomabybioinformaticsanalysisandcellexperiments AT yuandaozhang identifyingthenovelkeygenesinrenalcellcarcinomabybioinformaticsanalysisandcellexperiments AT khanaisha identifyingthenovelkeygenesinrenalcellcarcinomabybioinformaticsanalysisandcellexperiments AT wuwenqi identifyingthenovelkeygenesinrenalcellcarcinomabybioinformaticsanalysisandcellexperiments AT zengguohua identifyingthenovelkeygenesinrenalcellcarcinomabybioinformaticsanalysisandcellexperiments |