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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...

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Autores principales: Chen, Yeda, Gu, Di, Wen, Yaoan, Yang, Shuxin, Duan, Xiaolu, Lai, Yongchang, Yang, Jianan, Yuan, Daozhang, Khan, Aisha, Wu, Wenqi, Zeng, Guohua
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
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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.
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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
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