Cargando…

Identification of Three Potential Prognostic Genes in Platinum-Resistant Ovarian Cancer via Integrated Bioinformatics Analysis

PURPOSE: Ovarian cancer is the most lethal gynecologic malignancy. Resistance to platinum-based chemotherapy affects the overall survival of patients. This study used an integrated bioinformatics to find the poorly understood molecular mechanisms underlying platinum resistance in ovarian cancer. MET...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhang, Xue, Wei, Xuan, Bai, Gaigai, Huang, Xueyao, Hu, Shunxue, Mao, Hongluan, Liu, Peishu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8607279/
https://www.ncbi.nlm.nih.gov/pubmed/34824550
http://dx.doi.org/10.2147/CMAR.S336672
_version_ 1784602533660983296
author Zhang, Xue
Wei, Xuan
Bai, Gaigai
Huang, Xueyao
Hu, Shunxue
Mao, Hongluan
Liu, Peishu
author_facet Zhang, Xue
Wei, Xuan
Bai, Gaigai
Huang, Xueyao
Hu, Shunxue
Mao, Hongluan
Liu, Peishu
author_sort Zhang, Xue
collection PubMed
description PURPOSE: Ovarian cancer is the most lethal gynecologic malignancy. Resistance to platinum-based chemotherapy affects the overall survival of patients. This study used an integrated bioinformatics to find the poorly understood molecular mechanisms underlying platinum resistance in ovarian cancer. METHODS: Based on the RNA-seq data of tissues in The Cancer Genome Atlas (TCGA) and RNA-seq data of cells from the Cancer Cell Encyclopedia (CCLE), we integrated differentially expressed genes (DEGs) in ovarian cancer tissue and cells. After screening for DEGs related to platinum resistance, we conducted survival analysis and built protein interaction networks to identify genes that may affect prognosis and interact with each other. Least absolute shrinkage and selection operator (Lasso) regression analysis was used to construct a predictive model. Immunohistochemistry and Western blot were used to validate the results. Finally, gene set enrichment analysis (GSEA) was performed on the expression of genes individually. RESULTS: We found that ATPase Na(+)/K(+) transporting subunit alpha 2 (ATP1A2), calsequestrin 2 (CASQ2) and ryanodine receptor 2 (RYR2) interacted with each other and could predict resistance to platinum-based therapy, correlating negatively with prognosis. Moreover, we constructed a predictive model based on nine genes, including ATP1A2 and CASQ2. Immunohistochemistry and Western blot validated the upregulation of these genes in ovarian cancer tissue samples and cell lines. The immunohistochemistry results also confirmed the prognostic value of ATP1A2, CASQ2 and RYR2. GSEA predicted that ATP1A2, CASQ2 and RYR2 may act on the KRAS and mTORC1 pathways and participate in metabolic reprogramming and regulation of calcium homeostasis in platinum-resistant cells. CONCLUSION: ATP1A2, CASQ2 and RYR2 were highly expressed in platinum-resistant ovarian cancer. ATP1A2 and CASQ2 were related to the prognosis of platinum-resistant ovarian cancer patients. These genes might act on KARS and mTORC1 pathways and participate in metabolic reprogramming and regulation of calcium homeostasis in platinum-resistant cells.
format Online
Article
Text
id pubmed-8607279
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Dove
record_format MEDLINE/PubMed
spelling pubmed-86072792021-11-24 Identification of Three Potential Prognostic Genes in Platinum-Resistant Ovarian Cancer via Integrated Bioinformatics Analysis Zhang, Xue Wei, Xuan Bai, Gaigai Huang, Xueyao Hu, Shunxue Mao, Hongluan Liu, Peishu Cancer Manag Res Original Research PURPOSE: Ovarian cancer is the most lethal gynecologic malignancy. Resistance to platinum-based chemotherapy affects the overall survival of patients. This study used an integrated bioinformatics to find the poorly understood molecular mechanisms underlying platinum resistance in ovarian cancer. METHODS: Based on the RNA-seq data of tissues in The Cancer Genome Atlas (TCGA) and RNA-seq data of cells from the Cancer Cell Encyclopedia (CCLE), we integrated differentially expressed genes (DEGs) in ovarian cancer tissue and cells. After screening for DEGs related to platinum resistance, we conducted survival analysis and built protein interaction networks to identify genes that may affect prognosis and interact with each other. Least absolute shrinkage and selection operator (Lasso) regression analysis was used to construct a predictive model. Immunohistochemistry and Western blot were used to validate the results. Finally, gene set enrichment analysis (GSEA) was performed on the expression of genes individually. RESULTS: We found that ATPase Na(+)/K(+) transporting subunit alpha 2 (ATP1A2), calsequestrin 2 (CASQ2) and ryanodine receptor 2 (RYR2) interacted with each other and could predict resistance to platinum-based therapy, correlating negatively with prognosis. Moreover, we constructed a predictive model based on nine genes, including ATP1A2 and CASQ2. Immunohistochemistry and Western blot validated the upregulation of these genes in ovarian cancer tissue samples and cell lines. The immunohistochemistry results also confirmed the prognostic value of ATP1A2, CASQ2 and RYR2. GSEA predicted that ATP1A2, CASQ2 and RYR2 may act on the KRAS and mTORC1 pathways and participate in metabolic reprogramming and regulation of calcium homeostasis in platinum-resistant cells. CONCLUSION: ATP1A2, CASQ2 and RYR2 were highly expressed in platinum-resistant ovarian cancer. ATP1A2 and CASQ2 were related to the prognosis of platinum-resistant ovarian cancer patients. These genes might act on KARS and mTORC1 pathways and participate in metabolic reprogramming and regulation of calcium homeostasis in platinum-resistant cells. Dove 2021-11-16 /pmc/articles/PMC8607279/ /pubmed/34824550 http://dx.doi.org/10.2147/CMAR.S336672 Text en © 2021 Zhang et al. https://creativecommons.org/licenses/by-nc/3.0/This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/ (https://creativecommons.org/licenses/by-nc/3.0/) ). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php).
spellingShingle Original Research
Zhang, Xue
Wei, Xuan
Bai, Gaigai
Huang, Xueyao
Hu, Shunxue
Mao, Hongluan
Liu, Peishu
Identification of Three Potential Prognostic Genes in Platinum-Resistant Ovarian Cancer via Integrated Bioinformatics Analysis
title Identification of Three Potential Prognostic Genes in Platinum-Resistant Ovarian Cancer via Integrated Bioinformatics Analysis
title_full Identification of Three Potential Prognostic Genes in Platinum-Resistant Ovarian Cancer via Integrated Bioinformatics Analysis
title_fullStr Identification of Three Potential Prognostic Genes in Platinum-Resistant Ovarian Cancer via Integrated Bioinformatics Analysis
title_full_unstemmed Identification of Three Potential Prognostic Genes in Platinum-Resistant Ovarian Cancer via Integrated Bioinformatics Analysis
title_short Identification of Three Potential Prognostic Genes in Platinum-Resistant Ovarian Cancer via Integrated Bioinformatics Analysis
title_sort identification of three potential prognostic genes in platinum-resistant ovarian cancer via integrated bioinformatics analysis
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8607279/
https://www.ncbi.nlm.nih.gov/pubmed/34824550
http://dx.doi.org/10.2147/CMAR.S336672
work_keys_str_mv AT zhangxue identificationofthreepotentialprognosticgenesinplatinumresistantovariancancerviaintegratedbioinformaticsanalysis
AT weixuan identificationofthreepotentialprognosticgenesinplatinumresistantovariancancerviaintegratedbioinformaticsanalysis
AT baigaigai identificationofthreepotentialprognosticgenesinplatinumresistantovariancancerviaintegratedbioinformaticsanalysis
AT huangxueyao identificationofthreepotentialprognosticgenesinplatinumresistantovariancancerviaintegratedbioinformaticsanalysis
AT hushunxue identificationofthreepotentialprognosticgenesinplatinumresistantovariancancerviaintegratedbioinformaticsanalysis
AT maohongluan identificationofthreepotentialprognosticgenesinplatinumresistantovariancancerviaintegratedbioinformaticsanalysis
AT liupeishu identificationofthreepotentialprognosticgenesinplatinumresistantovariancancerviaintegratedbioinformaticsanalysis