Department of Gynecology and Obstetrics, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
State Key Laboratory for Diagnosis and Treatment of Infectious Diseases National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
BMC Med Genomics. 2022 Jul 4;15(1):148. doi: 10.1186/s12920-022-01299-5.
Breast cancer (BRCA) is the primary cause of mortality among females globally. The combination of advanced genomic analysis with proteomics characterization to construct a protein prognostic model will help to screen effective biomarkers and find new therapeutic directions. This study obtained proteomics data from The Cancer Proteome Atlas (TCPA) dataset and clinical data from The Cancer Genome Atlas (TCGA) dataset. Kaplan-Meier and Cox regression analyses were used to construct a prognostic risk model, which was consisted of 6 proteins (CASPASE7CLEAVEDD198, NFKBP65-pS536, PCADHERIN, P27, X4EBP1-pT70, and EIF4G). Based on risk curves, survival curves, receiver operating characteristic curves, and independent prognostic analysis, the protein prognostic model could be viewed as an independent factor to accurately predict the survival time of BRCA patients. We further validated that this prognostic model had good predictive performance in the GSE88770 dataset. The expression of 6 proteins was significantly associated with the overall survival of BRCA patients. The 6 proteins and encoding genes were differentially expressed in normal and primary tumor tissues and in different BRCA stages. In addition, we verified the expression of 3 differential proteins by immunohistochemistry and found that CDH3 and EIF4G1 were significantly higher in breast cancer tissues. Functional enrichment analysis indicated that the 6 genes were mainly related to the HIF-1 signaling pathway and the PI3K-AKT signaling pathway. This study suggested that the prognosis-related proteins might serve as new biomarkers for BRCA diagnosis, and that the risk model could be used to predict the prognosis of BRCA patients.
乳腺癌（BRCA）是全球女性死亡的主要原因。将先进的基因组分析与蛋白质组学特征相结合，构建蛋白质预后模型，有助于筛选有效的生物标志物并找到新的治疗方向。本研究从癌症蛋白质组图谱（TCPA）数据集获取蛋白质组学数据，并从癌症基因组图谱（TCGA）数据集获取临床数据。使用 Kaplan-Meier 和 Cox 回归分析构建预后风险模型，该模型由 6 种蛋白质（CASPASE7CLEAVEDD198、NFKBP65-pS536、PCADHERIN、P27、X4EBP1-pT70 和 EIF4G）组成。基于风险曲线、生存曲线、接受者操作特征曲线和独立预后分析，蛋白质预后模型可以作为独立因素准确预测 BRCA 患者的生存时间。我们进一步在 GSE88770 数据集验证了该预后模型具有良好的预测性能。6 种蛋白质的表达与 BRCA 患者的总生存期显著相关。6 种蛋白质及其编码基因在正常和原发性肿瘤组织以及不同 BRCA 阶段的表达存在差异。此外，我们通过免疫组织化学验证了 3 种差异蛋白的表达，发现 CDH3 和 EIF4G1 在乳腺癌组织中明显升高。功能富集分析表明，这 6 个基因主要与 HIF-1 信号通路和 PI3K-AKT 信号通路有关。本研究表明，预后相关蛋白可能作为 BRCA 诊断的新生物标志物，风险模型可用于预测 BRCA 患者的预后。
