Prognostic analysis of breast cancer based on conditionally associated complementary genes

Abstract

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.

Keywords

• biological mathematics; breast cancer; condition-associated genes; prognosis model; clinical prediction