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The identification of six risk genes for ovarian cancer platinum response based on global network algorithm and verification analysis

Ovarian cancer is the most lethal gynaecological cancer, and resistance of platinum‐based chemotherapy is the main reason for treatment failure. The aim of the present study was to identify candidate genes involved in ovarian cancer platinum response by analysing genes from homologous recombination...

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Autores principales: Xing, Linan, Mi, Wanqi, Zhang, Yongjian, Tian, Songyu, Zhang, Yunyang, Qi, Rui, Lou, Ge, Zhang, Chunlong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7520306/
https://www.ncbi.nlm.nih.gov/pubmed/32762026
http://dx.doi.org/10.1111/jcmm.15567
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author Xing, Linan
Mi, Wanqi
Zhang, Yongjian
Tian, Songyu
Zhang, Yunyang
Qi, Rui
Lou, Ge
Zhang, Chunlong
author_facet Xing, Linan
Mi, Wanqi
Zhang, Yongjian
Tian, Songyu
Zhang, Yunyang
Qi, Rui
Lou, Ge
Zhang, Chunlong
author_sort Xing, Linan
collection PubMed
description Ovarian cancer is the most lethal gynaecological cancer, and resistance of platinum‐based chemotherapy is the main reason for treatment failure. The aim of the present study was to identify candidate genes involved in ovarian cancer platinum response by analysing genes from homologous recombination and Fanconi anaemia pathways. Associations between these two functional genes were explored in the study, and we performed a random walk algorithm based on reconstructed gene‐gene network, including protein‐protein interaction and co‐expression relations. Following the random walk, all genes were ranked and GSEA analysis showed that the biological functions focused primarily on autophagy, histone modification and gluconeogenesis. Based on three types of seed nodes, the top two genes were utilized as examples. We selected a total of six candidate genes (FANCA, FANCG, POLD1, KDM1A, BLM and BRCA1) for subsequent verification. The validation results of the six candidate genes have significance in three independent ovarian cancer data sets with platinum‐resistant and platinum‐sensitive information. To explore the correlation between biomarkers and clinical prognostic factors, we performed differential analysis and multivariate clinical subgroup analysis for six candidate genes at both mRNA and protein levels. And each of the six candidate genes and their neighbouring genes with a mutation rate greater than 10% were also analysed by network construction and functional enrichment analysis. In the meanwhile, the survival analysis for platinum‐treated patients was performed in the current study. Finally, the RT‐qPCR assay was used to determine the performance of candidate genes in ovarian cancer platinum response. Taken together, this research demonstrated that comprehensive bioinformatics methods could help to understand the molecular mechanism of platinum response and provide new strategies for overcoming platinum resistance in ovarian cancer treatment.
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spelling pubmed-75203062020-09-30 The identification of six risk genes for ovarian cancer platinum response based on global network algorithm and verification analysis Xing, Linan Mi, Wanqi Zhang, Yongjian Tian, Songyu Zhang, Yunyang Qi, Rui Lou, Ge Zhang, Chunlong J Cell Mol Med Original Articles Ovarian cancer is the most lethal gynaecological cancer, and resistance of platinum‐based chemotherapy is the main reason for treatment failure. The aim of the present study was to identify candidate genes involved in ovarian cancer platinum response by analysing genes from homologous recombination and Fanconi anaemia pathways. Associations between these two functional genes were explored in the study, and we performed a random walk algorithm based on reconstructed gene‐gene network, including protein‐protein interaction and co‐expression relations. Following the random walk, all genes were ranked and GSEA analysis showed that the biological functions focused primarily on autophagy, histone modification and gluconeogenesis. Based on three types of seed nodes, the top two genes were utilized as examples. We selected a total of six candidate genes (FANCA, FANCG, POLD1, KDM1A, BLM and BRCA1) for subsequent verification. The validation results of the six candidate genes have significance in three independent ovarian cancer data sets with platinum‐resistant and platinum‐sensitive information. To explore the correlation between biomarkers and clinical prognostic factors, we performed differential analysis and multivariate clinical subgroup analysis for six candidate genes at both mRNA and protein levels. And each of the six candidate genes and their neighbouring genes with a mutation rate greater than 10% were also analysed by network construction and functional enrichment analysis. In the meanwhile, the survival analysis for platinum‐treated patients was performed in the current study. Finally, the RT‐qPCR assay was used to determine the performance of candidate genes in ovarian cancer platinum response. Taken together, this research demonstrated that comprehensive bioinformatics methods could help to understand the molecular mechanism of platinum response and provide new strategies for overcoming platinum resistance in ovarian cancer treatment. John Wiley and Sons Inc. 2020-08-06 2020-09 /pmc/articles/PMC7520306/ /pubmed/32762026 http://dx.doi.org/10.1111/jcmm.15567 Text en © 2020 The Authors. Journal of Cellular and Molecular Medicine published by Foundation for Cellular and Molecular Medicine and John Wiley … Sons Ltd This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Articles
Xing, Linan
Mi, Wanqi
Zhang, Yongjian
Tian, Songyu
Zhang, Yunyang
Qi, Rui
Lou, Ge
Zhang, Chunlong
The identification of six risk genes for ovarian cancer platinum response based on global network algorithm and verification analysis
title The identification of six risk genes for ovarian cancer platinum response based on global network algorithm and verification analysis
title_full The identification of six risk genes for ovarian cancer platinum response based on global network algorithm and verification analysis
title_fullStr The identification of six risk genes for ovarian cancer platinum response based on global network algorithm and verification analysis
title_full_unstemmed The identification of six risk genes for ovarian cancer platinum response based on global network algorithm and verification analysis
title_short The identification of six risk genes for ovarian cancer platinum response based on global network algorithm and verification analysis
title_sort identification of six risk genes for ovarian cancer platinum response based on global network algorithm and verification analysis
topic Original Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7520306/
https://www.ncbi.nlm.nih.gov/pubmed/32762026
http://dx.doi.org/10.1111/jcmm.15567
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