Department of General Surgery, Fujian Medical University Provincial Clinical Medical College, Fujian Provincial Hospital, Fujian, 350000, China.
Fujian Medical University, Fuzhou, Fujian, 350000, China.
Biosci Rep. 2020 Dec 23;40(12). doi: 10.1042/BSR20202376.
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, six 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, the present study obtained a radiotherapy sensitivity estimation signature for BRCA, which should shed new light in clinical and experimental research.
乳腺癌（BRCA）是全球女性中最常见的恶性肿瘤，死亡率较高。放疗是 BRCA 的一种常见治疗方法，但患者之间的疗效存在差异。在这里，我们提出了一种基于基因表达的 BRCA 放疗敏感性估计的signature。从癌症基因组图谱（TCGA）和国际癌症基因组联盟（ICGC）获得了 BRCA 样本的基因表达谱，并分别用作训练和独立测试数据集。使用 edgeR Bioconductor 包在训练集中比较 BRCA 样本与其癌旁样本，鉴定出差异表达基因（DEGs）。单因素 Cox 回归分析和 LASSO Cox 回归方法用于筛选构建放疗敏感性估计 signature 的最佳基因。结合独立预后因素的列线图用于预测接受放疗的 BRCA 患者的 1 年、3 年和 5 年总生存期（OS）。采用 CIBERSORT 计算肿瘤浸润免疫细胞（TIICs）的相对比例和关键免疫检查点受体的 mRNA 水平，探讨 signature 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 DEGs，其中 6 个被保留下来用于构建放疗敏感性预测模型。该 signature 在训练集和测试集均具有稳健性。此外，该 signature 与 TIICs 和免疫检查点受体 mRNA 水平背景下的 BRCA 免疫微环境密切相关。总之，本研究获得了用于 BRCA 的放疗敏感性估计 signature，这应该为临床和实验研究提供新的思路。
