Breast cancer (BRCA) represents the most common malignancy among women worldwide with high mortality. Radiotherapy is a prevalent therapeutic for BRCA that with heterogeneous effectiveness among patients. Here, we proposed to develop a gene expression-based signature for BRCA radiotherapy sensitivity estimation. Gene expression profiles of BRCA samples from the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) were obtained and used as training and independent testing dataset, respectively. Differential expression genes (DEGs) in BRCA samples compared with their paracancerous samples in the training set were identified by using the edgeR Bioconductor package. Univariate Cox regression analysis and LASSO Cox regression method were applied to screen optimal genes for constructing a radiotherapy sensitivity estimation signature. Nomogram combining independent prognostic factors was used to predict 1-, 3-, and 5-year OS of radiation-treated BRCA patients. Relative proportions of tumor infiltrating immune cells (TIICs) calculated by CIBERSORT and mRNA levels of key immune checkpoint receptors was adopted to explore the relation between the signature and tumor immune response. As a result, 603 DEGs were obtained in BRCA tumor samples, 6 of which were retained and used to construct the radiotherapy sensitivity prediction model. The signature was proved to be robust in both training and testing sets. In addition, the signature was closely related to the immune microenvironment of BRCA in the context of TIICs and immune checkpoint receptors' mRNA levels. In conclusion, this study obtained a radiotherapy sensitivity estimation signature for BRCA, which should shed new light in clinical and experimental research.

中文翻译：

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用于乳腺癌放射治疗敏感性估计的基于六基因的特征。

乳腺癌 (BRCA) 是全球女性中最常见的恶性肿瘤，死亡率很高。放射治疗是 BRCA 的普遍治疗方法，在患者中具有异质性。在这里，我们建议为 BRCA 放射治疗敏感性估计开发基于基因表达的特征。获得了来自癌症基因组图谱 (TCGA) 和国际癌症基因组联盟 (ICGC) 的 BRCA 样本的基因表达谱，并分别用作训练和独立测试数据集。BRCA 样本中的差异表达基因 (DEG) 与其在训练集中的癌旁样本相比，使用 edgeR Bioconductor 包进行了鉴定。应用单变量 Cox 回归分析和 LASSO Cox 回归方法筛选用于构建放射治疗敏感性估计特征的最佳基因。结合独立预后因素的列线图用于预测接受放射治疗的 BRCA 患者的 1 年、3 年和 5 年 OS。采用CIBERSORT计算的肿瘤浸润免疫细胞（TIICs）的相对比例和关键免疫检查点受体的mRNA水平来探索特征与肿瘤免疫反应之间的关系。结果，在BRCA肿瘤样本中获得了603个DEG，其中6个被保留并用于构建放疗敏感性预测模型。签名被证明在训练和测试集中都是稳健的。此外，在 TIIC 和免疫检查点受体的 mRNA 水平的背景下，该特征与 BRCA 的免疫微环境密切相关。总之，这项研究获得了 BRCA 的放射治疗敏感性估计特征，这应该为临床和实验研究提供新的思路。