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

Autor: Yongjian Zhang, Rui Qi, Chunlong Zhang, Linan Xing, Yunyang Zhang, Ge Lou, Songyu Tian, Wanqi Mi
Rok vydání: 2020
Předmět:
Adult
0301 basic medicine
Mutation rate
Candidate gene
endocrine system diseases
homologous recombination
Subgroup analysis
Computational biology
Biology
Disease-Free Survival
03 medical and health sciences
0302 clinical medicine
Risk Factors
FANCG
Cell Line
Tumor
medicine
Humans
Fanconi Anemia Complementation Group G Protein
Gene
Survival analysis
Aged
DNA Polymerase III
Platinum
Aged
80 and over
Histone Demethylases
Ovarian Neoplasms
Fanconi Anemia Complementation Group A Protein
RecQ Helicases
platinum treatment
Original Articles
Cell Biology
Middle Aged
medicine.disease
FANCA
Gene Expression Regulation
Neoplastic
Fanconi Anemia
ovarian cancer
030104 developmental biology
Drug Resistance
Neoplasm
030220 oncology & carcinogenesis
Molecular Medicine
Female
Original Article
Fanconi anaemia
Ovarian cancer
Algorithms
Zdroj: Journal of Cellular and Molecular Medicine
ISSN: 1582-4934
1582-1838
DOI: 10.1111/jcmm.15567
Popis: 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.
Databáze: OpenAIRE
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