Table_1_Development and validation of an inflammatory response-related signature in triple negative breast cancer for predicting prognosis and immunotherapy.xls

BackgroundInflammation is one of the most important characteristics of tumor tissue. Signatures based on inflammatory response-related genes (IRGs) can predict prognosis and treatment response in a variety of tumors. However, the clear function of IRGs in the triple negative breast cancer (TNBC) still needs to be explored.
MethodsIRGs clusters were discovered via consensus clustering, and the prognostic differentially expressed genes (DEGs) across clusters were utilized to develop a signature using a least absolute shrinkage and selection operator (LASSO). Verification analyses were conducted to show the robustness of the signature. The expression of risk genes was identified by RT-qPCR. Lastly, we formulated a nomogram to improve the clinical efficacy of our predictive tool.
ResultsThe IRGs signature, comprised of four genes, was developed and was shown to be highly correlated with the prognoses of TNBC patients. In contrast with the performance of the other individual predictors, we discovered that the IRGs signature was remarkably superior. Also, the ImmuneScores were elevated in the low-risk group. The immune cell infiltration showed significant difference between the two groups, as did the expression of immune checkpoints.
ConclusionThe IRGs signature could act as a biomarker and provide a momentous reference for individual therapy of TNBC.

背景：炎症是肿瘤组织最为重要的特征之一。基于炎症反应相关基因（inflammatory response-related genes, IRGs）的基因特征可预测多种肿瘤的预后及治疗应答情况。然而，三阴性乳腺癌（triple negative breast cancer, TNBC）中IRGs的确切功能仍有待进一步探索。
方法：本研究通过共识聚类（consensus clustering）识别IRGs聚类，并利用聚类间的预后差异表达基因（differentially expressed genes, DEGs），借助最小绝对收缩和选择算子（least absolute shrinkage and selection operator, LASSO）构建基因特征模型。随后开展验证分析以验证该模型的稳健性；通过RT-qPCR技术检测明确风险基因的表达水平；最后构建列线图以提升该预测工具的临床应用效能。
结果：本研究构建了由4个基因组成的IRGs基因特征模型，该模型与TNBC患者的预后显著相关。相较于其他单一预测指标，该IRGs基因特征模型的预测性能显著更优。此外，低风险组的免疫评分（ImmuneScores）更高，两组间的免疫细胞浸润情况以及免疫检查点的表达水平均存在显著差异。
结论：该IRGs基因特征模型可作为生物标志物，为TNBC的个体化治疗提供重要参考。