卵巢癌基因共表达网络及预后标志物的研究

Research on gene co-expression network and prognostic biomarkers of ovarian cancer

卵巢癌是一种早期诊断率低而致死率较高的恶性肿瘤,对其预后标志物的鉴定和生存率的预测仍是生存分析的重要任务。利用卵巢癌预后相关基因构建基因共表达网络,鉴定预后生物标志物并进行生存率的预测。首先,对TCGA(The cancer genome atlas)数据库下载的卵巢癌基因表达数据实施单因素回归分析,利用得到的747个预后相关基因构建卵巢癌预后加权基因共表达网络。其次,考虑网络的生物学意义,利用蛋白质相互作用(Protein-protein interaction,PPI)数据对共表达网络中的模块重新加权,并根据网络中基因的拓扑重要性对基因进行排序。最后,运用Cox比例风险回归对网络中的重要基因构建卵巢癌预后模型,鉴定了3个预后生物标志物。生存分析结果显示,这3个标志物能够显著区分不同预后的患者,较好地预测卵巢癌患者的预后情况。

Ovarian cancer is a malignant tumor with low early diagnosis rate and high mortality rate.The identification of prognostic biomarkers and the prediction of patient risk are still important tasks in survival analysis.In this paper,a weighted gene co-expression network constructed by ovarian cancer prognosis-related genes was used to identify prognostic biomarkers and predict patient risk.Firstly,data of 320 patients with ovarian cancer were obtained from the TCGA(The cancer genome atlas)database,and 747 prognosis-related genes were selected by Cox univariable regression to construct a weight edgene co-expression network.Then,considering the biological significance of the network,the modulein co-expression network was re-weighted by integrating the protein-protein interaction(PPI)data from the module genes.Since topologically important genes in the network tend to play key roles in ovarian cancer,the topological properties of the genes in re-weighted network were used to rank the module genes.Finally,the Cox proportional hazards model was employed to construct prognostic models by these topologically important genes.By considering a balance between the model prediction ability and the number of genes,three prognostic biomarkers were identified.Survival analysis showed that the three biomarkers could significantly distinguish patients with different prognosis.