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.

中文翻译：

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基于全局网络算法和验证分析的卵巢癌铂类反应6个风险基因识别[J].

卵巢癌是致死率最高的妇科癌症，铂类化疗耐药是治疗失败的主要原因。本研究的目的是通过分析同源重组和范可尼贫血途径中的基因来确定参与卵巢癌铂反应的候选基因。在研究中探索了这两个功能基因之间的关联，我们基于重建的基因-基因网络进行了随机游走算法，包括蛋白质-蛋白质相互作用和共表达关系。在随机游走之后，对所有基因进行排序，GSEA 分析表明生物学功能主要集中在自噬、组蛋白修饰和糖异生上。基于三种类型的种子节点，以前两个基因为例。我们总共选择了六个候选基因（FANCA、FANCG、POLD1、KDM1A、BLM 和 BRCA1）进行后续验证。六个候选基因的验证结果在三个具有铂耐药和铂敏感信息的独立卵巢癌数据集中具有显着意义。为了探索生物标志物与临床预后因素之间的相关性，我们在 mRNA 和蛋白质水平对六个候选基因进行了差异分析和多变量临床亚组分析。并对突变率大于10%的6个候选基因及其相邻基因进行了网络构建和功能富集分析。同时，本研究对铂类治疗患者进行了生存分析。最后，RT-qPCR 测定用于确定候选基因在卵巢癌铂反应中的表现。总之，这项研究表明综合生物信息学方法可以帮助理解铂类反应的分子机制，并为克服卵巢癌治疗中的铂类耐药性提供新的策略。