Identification of a prognostic chemoresistance-related gene signature associated with immune microenvironment in breast cancer

Breast cancer is the most common form of cancer among women globally, and chemoresistance is a major challenge to disease treatment that is associated with a poor prognosis. This study was formulated to identify a reliable prognostic biosignature capable of predicting the survival of patients with chemoresistant breast cancer (CRBC) and evaluating the associated tumor immune microenvironment. Through a series of protein-protein interaction and weighted correlation network analyses, genes that were significantly associated with breast cancer chemoresistance were identified. Moreover, univariate Cox regression and lasso-penalized Cox regression analyses were employed to generate a prognostic model, and the prognostic utility of this model was then assessed using time-dependent receiver operating characteristic (ROC) and Kaplan-Meier survival curves. Finally, The CIBERSORT and ESTIMATE algorithms were additionally leveraged to assess relationships between the tumor immune microenvironment and patient prognostic signatures. Overall, a multigenic prognostic biosignature capable of predicting CRBC patient risk was successfully developed based on bioinformatics analysis and in vitro experiments. This biosignature was able to stratify CRBC patients into high- and low-risk subgroups. ROC curves also revealed that this biosignature achieved high diagnostic efficiency, and multivariate regression analyses indicated that this risk signature was an independent risk factor linked to CRBC patient outcomes. In addition, this signature was associated with the infiltration of the tumor microenvironment by multiple immune cell types. In conclusion, the chemoresistance-associated prognostic gene signature developed herein was able to effectively evaluate the prognosis of CRBC patients and to reflect the overall composition of the tumor immune microenvironment.

乳腺癌是全球女性最常见的恶性肿瘤，化疗耐药是疾病治疗的重大难题，且与不良预后密切相关。本研究旨在筛选出可靠的预后生物特征标记，用于预测化疗耐药性乳腺癌（chemoresistant breast cancer, CRBC）患者的生存情况，并评估其相关的肿瘤免疫微环境。通过一系列蛋白质相互作用分析与加权相关网络分析，本研究筛选出与乳腺癌化疗耐药显著相关的基因。此外，采用单因素Cox回归与LASSO惩罚Cox回归分析构建预后模型，并通过时间依赖性受试者工作特征（receiver operating characteristic, ROC）曲线及Kaplan-Meier生存曲线评估该模型的预后效能。最后，本研究进一步利用CIBERSORT与ESTIMATE算法，分析肿瘤免疫微环境与患者预后特征之间的关联。综上，本研究通过生物信息学分析与体外实验，成功构建了可预测CRBC患者风险的多基因预后生物特征标记。该标记可将CRBC患者划分为高风险与低风险两个亚组。ROC曲线分析显示，该标记具有较高的诊断效能；多因素回归分析表明，该风险特征是与CRBC患者预后相关的独立危险因素。此外，该特征与肿瘤微环境中多种免疫细胞的浸润水平显著相关。综上，本研究构建的化疗耐药相关预后基因特征，可有效评估CRBC患者的预后，并反映肿瘤免疫微环境的整体构成情况。