Abstract: Objective To establish a multi-gene prognostic model for predicting the prognosis of breast cancer using Cancer Genome Atlas (TCGA) database, and to analyze the relationship between the multi-gene prognostic model and clinical and pathological features of breast cancer. Methods The mRNA data and clinical information of breast cancer cohort were downloaded from TCGA database. Differentially expressed genes (DEGs) were identified by R language software in breast cancer tissues and normal tissues. DEGs related to overall survival of patients were selected by univariate Cox regression model, and a multi-gene signature model was identified by multivariate Cox regression model. Patients were divided into high risk cohort and low risk cohort according to prognostic index calculated by prognostic index formula based on the result of multivariate Cox regression model. Factors were analyzed by univariate and multivariate Cox regression models according to clinicopathological characteristics and prognostic index related with survival of patients with breast cancer. Survival analysis of subgroups was conducted according to age, estrogen receptor status, Her-2 receptor status, lymph node status and pathological stage. Kaplan-Meier（K-M）survival analysis was used to evaluate the prognostic prediction of the multi-gene signature in overall patients and subgroups. Results Eight DEGs were selected to conduct a survival related multi-gene signature from total of 2 142 DEGs in univariate and multivariate Cox regression model analysis including CEL， POU3F2， CYP24A1， FABP7， MURC， GCCR， LRP1B and PRSS2. Prognostic index formula was as follows： PI=0.156 × the expression of CEL + 0.112 × the expression of POU3F2-0.071 × the expression of CYP24A1-0.065 × the expression of FABP7+0.135×the expression of MURC-0.201×the expression of GCGR-0.063×the expression of LRP1B- 0.090×the expression of PRSS2. Cox regression model analysis demonstrated that age, pathological stage and eight-gene signature were validated as the novel and independent prognostic factors (P＜0.05). According to survival analysis (K-M plot), the accurate prognostic performance of eight-gene signature was confirmed in both overall patients and subgroups (except Ⅳ stage). Patients with low risk of prognostic score showed significantly longer OS compared with patients of high risk of prognostic score (P＜0.01). Conclusion The eight-gene prognostic signature can be used to predict the prognosis of breast cancer patients. It is verified in the subgroup of breast cancer according to the clinicopathological features, which is helpful to further guide the clinical treatment.

Key words: breast neoplasms, prognosis, genes, TCGA database, Cox proportional regression model