Chen Chao, Lin Cai-Jin, Li Si-Yuan, Hu Xin, Shao Zhi-Ming
Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.
Key Laboratory of Breast Cancer in Shanghai, Fudan University Shanghai Cancer Center, Shanghai, China.
Ann Transl Med. 2022 Oct;10(20):1095. doi: 10.21037/atm-22-1931.
Although perceived as a highly aggressive disease, triple-negative breast cancer (TNBC) constitutes heterogeneous features with various outcomes. In this study, we aimed to establish a prognostic signature for patients with TNBC to improve risk stratification.
Gene expression data were obtained from The Cancer Genome Atlas (TCGA). Differentially expressed genes (DEGs) were detected pairwise between TNBC and other subtypes of samples. Then, TNBC-correlated modules were determined using coexpression network analysis. A gene signature was established based on the prognostic genes in the intersection between DEGs and selected gene modules using least absolute shrinkage and selection operator (LASSO) Cox regression. Finally, a clinico-transcriptomic signature was developed to predict overall survival (OS). Model performance was quantified, and the bootstrap resampling method was used for validation.
The gene signature included 6 messenger RNAs (mRNAs) and a clinical score indicating an increased likelihood of death when used as continuous or categorical predictors. A nomogram was built by integrating the pathological stage and gene signature to predict 2-, 3-, and 5-year OS. The addition of pathological stage increased the concordance index (C-index) compared with pathological stage alone and the gene signature alone. Bootstrap resampling revealed a stable performance of the nomogram.
A 6-mRNA signature was established to inform prognosis for patients with TNBC. Its combination with pathological stage can contribute to improving performance and provide additional supporting evidence for clinical decision-making.
尽管三阴性乳腺癌（TNBC）被视为一种侵袭性很强的疾病，但它具有异质性特征，预后各不相同。在本研究中，我们旨在为TNBC患者建立一种预后特征，以改善风险分层。
从癌症基因组图谱（TCGA）获取基因表达数据。在TNBC与其他样本亚型之间成对检测差异表达基因（DEG）。然后，使用共表达网络分析确定与TNBC相关的模块。基于DEG与选定基因模块交集内的预后基因，使用最小绝对收缩和选择算子（LASSO）Cox回归建立基因特征。最后，开发一种临床转录组特征来预测总生存期（OS）。对模型性能进行量化，并使用自助重采样方法进行验证。
该基因特征包括6种信使核糖核酸（mRNA）和一个临床评分，当用作连续或分类预测因子时，表明死亡可能性增加。通过整合病理分期和基因特征构建了列线图，以预测2年、3年和5年总生存期。与单独的病理分期和单独的基因特征相比，加入病理分期提高了一致性指数（C指数）。自助重采样显示列线图性能稳定。
建立了一种6-mRNA特征，用于为TNBC患者提供预后信息。它与病理分期的结合有助于提高性能，并为临床决策提供额外的支持证据。
