基于条件关联互补基因的乳腺癌预后分析

为了提高乳腺癌患者的生存率,改善病人的临床治疗效果,从分子机制上研究了乳腺癌的致病基因。首先对113个正常组织和1 109个癌症组织的表达量进行差异分析,然后对差异表达的基因采用条件联合分析方式对互补基因进行分组,并用逐步Cox回归挑选出一组基因拟合预后模型。研究结果显示:VWCE,SPDYC,CRYBG3,DEFB1,SEL1L2,NMNAT2 6个基因对患者生存率是有害的,AMZ1,GJB2,CXCL2,ALDOC 4个基因对患者生存率是有利的,最终确定10个基因的预后模型能够显著地将样本分为高风险组和低风险组,并且对乳腺癌患者5年和10年的生存率进行了预测,依赖时间的AUC值均可达0.7以上。所提方法能够利用基因与基因之间的关联性,很好地对高维数据进行降维,消除基因与基因之间的共线性问题,10个基因的预后模型可以对患者的临床预测提供帮助。

Prognostic analysis of breast cancer based on conditionally associated complementary genes

In order to improve the survival rate and the clinical treatment effect of breast cancer patients, the pathogenic genes of breast cancer were studied from the molecular mechanism. At first, the differential expression of 113 normal tissues and 1 109 cancer tissues was analyzed. Then, the complementary genes were grouped in a conditional joint analysis method for differentially expressed genes, and a set of gene fitting prognostic models were selected by stepwise Cox regression. The results show that six genes-[WTBX]VWCE, SPDYC, CRYBG3, DEFB1, SEL1L2 and NMNAT2-have a harmful effect on survival rate. Four genes AMZ1, GJB2, CXCL2 and ALDOC are beneficial to survival rate. The final prognostic model of the 10 genes can significantly divide the samples into high-risk group and low-risk group, and predict the 5-year and 10-year survival rates of breast cancer patients, and the time-dependent AUC values are both up to 0.7 or more. This method can take advantage of the correlation between genes to reduce dimensionality of high-dimensional data and eliminate the problem of collinearity between genes. The prognosis model of these 10 genes can provide help for the clinical prediction of patients.