基于生物信息学分析构建卵巢癌预后风险预测模型

Construction of a prognostic risk prediction model for ovarian cancer based on bioinformatics analysis

卵巢癌(ovarian cancer,OC)是死亡率较高的妇科恶性肿瘤之一.预后风险预测模型可用于预测患者的生存和预后,有利于有针对性地为OC患者制定个体化治疗方案.通过筛选和分析6个OC相关基因芯片数据集,构建了OC预后风险模型并分析其预测价值,采用qRT-PCR验证风险因子的表达.结果共筛选出99个在6个芯片中共失调的差异表达基因(differentially expressed genes,DEGs),这些DEGs主要与细胞周期过程调控、细胞成分组织的负调控、免疫系统细胞因子信号转导、p53、HIF-1信号通路等相关.OC预后风险模型中5个风险因子为PS8、ARL4C、HMGB3、JUP和USP18,模型风险得分与OC患者总体生存期、癌症状态、预后结局显著相关(P<0.05),也可用于区分OC与正常样本(P<0.05).该研究将对OC的早期筛查和预后风险评估提供新思路.

Ovarian cancer(OC)is one of the gynecological malignancies with high mortality.The prognostic risk prediction model can be used to predict the survival and prognosis of patients,which is conducive to the targeted development of individualized treatment plans for OC patients.Six chips were selected and analyzed to construct prognostic risk model of OC,and the prognostic value of this risk model was analyzed.The expression of risk factors was verified by qRT-PCR.A total of 99 differentially expressed genes(DEGs)that were dysregulated in the six chips were screened.These DEGs were related to cell cycle process regulation,negative regulation of cell component organization,immune system cytokine signal transduction,p53,HIF-1 signaling pathway,etc.The five risk factors in the OC prognostic risk model were PS8,ARL4C,HMGB3,JUP and USP18.The risk score of this model was significantly related to the overall survival,cancer status,and prognostic outcome of OC patients(P<0.05),and could also be used to distinguish OC from normal samples(P<0.05).This study will provide new ideas and theoretical basis for early diagnosis and prognostic risk assessment of OC.