The protein expression profiles and clinical information of 879 breast cancer （BC） patient samples were downloaded from the TCPA and TCGA databases， respectively. We performed univariate Cox regression， lasso regression and multivariate Cox regression analysis to identify prognostic proteins and establish a prognostic model. Then， we further analyzed the differential expression and survival of the prognostic model protein， and analyzed the predictive performance of the model risk score. In addition， we analyzed all proteins related to prognostic model proteins.

Based on the 224 proteins in the TCPA database， we constructed a prognostic model containing 6 proteins （CASPASE7CLIVEDD198， NFKBP65_pS536， PCADHERIN， P27， X4EBP1_pT70， EIF4G）， and divided the patients into two groups according to the median of risk score. The overall survival rate was significantly different. Univariate and multivariate Cox regression analysis showed that the risk score was an independent prognostic factor for patients （P< 0.001）. In addition， the nomogram and ROC are used to demonstrate the predictive performance of the predictive model. The protein interaction relationship showed that the proteins CASPASE7CLEAVEDD198， PCADHERIN， X4EBP1_pT70 and EIF4G showed obvious correlation with other proteins （P< 0.001）.

Studies have shown that six key proteins may be considered as new targets for BC treatment， and the protein risk model can be used to predict the overall survival of BC patients.

分别从TCPA和TCGA数据库下载879名乳腺癌患者样本的蛋白质表达谱和临床信息，进行单变量Cox回归、lasso回归和多变量Cox回归分析以鉴定预后蛋白质建立预后模型，然后对预后模型蛋白进行差异表达及生存分析，对模型风险评分进行预测性能分析，并对与预后模型蛋白相关的所有蛋白进行分析。

通过TCPA数据库中的224个蛋白质，我们构建了包含CASPASE7CLIVEDD198、NFKBP65_pS536、PCADHERIN、P27、X4EBP1_pT70、EIF4G等 6个蛋白的预后模型，根据风险评分中位值将患者分为两组，总生存率差异有统计学意义。单因素和多因素Cox回归分析表明，该风险评分是患者的独立预后因素（P< 0.001）。此外，应用列线图及ROC对预测模型的预测性能进行了展示。蛋白质相互作用关系显示，蛋白质CASPASE7CLEAVEDD198、PCADHERIN、X4EBP1_pT70和EIF4G与其他蛋白质表现出较明显的相关性（P< 0.001）。

6个关键蛋白可能是乳腺癌治疗的新靶点，蛋白质风险模型可用于预测乳腺癌患者的预后。