Data_Sheet_8_Identification and Validation of an Immunological Expression-Based Prognostic Signature in Breast Cancer.XLSX

Background: Emerging evidence suggests that the immune system plays a crucial role in the regulation of the response to therapy and long-term outcomes of patients with breast cancer (BRCA). In this study, we aimed to identify a significant signature based on immune-related genes to predict the prognosis of BRCA patients.
Methods: The expression data were downloaded from The Cancer Genome Atlas (TCGA). The immune-related gene list, the transcription factor (TF) gene list, and the immune infiltrate scores of samples in the TCGA database were acquired from the ImmPort database, the Cistrome Cancer database, and the TIMER database, respectively. Univariate Cox regression analysis was utilized to identify prognostic immune-related differentially expressed genes (DEGs) (PIRDEGs) in BRCA. A prognostic immune signature containing 15 PIRDEGs in BRCA was established using the least absolute shrinkage and selection operator (LASSO) model with 1,000 iterations followed by a stepwise Cox proportional hazards model with a training set of 508 samples in TCGA. An independent assessment of the prognostic prediction ability of the signature was conducted using Kaplan–Meier survival analysis with a testing set of 505 samples in TCGA.
Results: We identified 466 PIRDEGs and 80 TFs among the DEGs. A gene signature containing 15 PIRDEGs was constructed. Risk scores of BRCA patients were calculated using this model, which showed a high accuracy of prognosis prediction in both the training set and testing set and could be an independent prognostic factor of BRCA patients.
Conclusions: Our study revealed that a PIRDEG signature could be a candidate prognostic biomarker for predicting the overall survival (OS) of patients with BRCA.

背景：日益增多的研究证据表明，免疫系统在乳腺癌（breast cancer, BRCA）患者的治疗应答调控与长期预后中发挥关键作用。本研究旨在筛选基于免疫相关基因的有效特征，以预测乳腺癌患者的预后。
方法：本研究的表达数据从癌症基因组图谱（The Cancer Genome Atlas, TCGA）下载获得。免疫相关基因列表、转录因子（transcription factor, TF）基因列表，以及TCGA数据库中样本的免疫浸润评分，分别从ImmPort数据库、Cistrome Cancer数据库与TIMER数据库获取。采用单变量Cox回归分析，鉴定乳腺癌中与预后相关的免疫差异表达基因（differentially expressed genes, DEGs，下称PIRDEGs）。基于TCGA数据库中508例样本的训练集，通过经1000次迭代的最小绝对收缩与选择算子（least absolute shrinkage and selection operator, LASSO）模型，并结合逐步Cox比例风险回归模型，构建了包含15个PIRDEGs的乳腺癌预后免疫特征。采用TCGA数据库中505例样本的测试集，通过Kaplan-Meier生存分析对该特征的预后预测能力进行独立验证。
结果：本研究共筛选得到466个PIRDEGs与80个TFs，并构建了包含15个PIRDEGs的基因特征模型。利用该模型计算乳腺癌患者的风险评分，结果显示该模型在训练集与测试集中均表现出较高的预后预测精度，且可作为乳腺癌患者的独立预后因素。
结论：本研究表明，PIRDEG特征可作为预测乳腺癌患者总生存期（overall survival, OS）的候选预后生物标志物。