Department of Plastic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei, China.
Department of Thyroid and Breast Surgery, Shenzhen Qianhai Shekou Free Trade Zone Hospital, Shenzhen 518067, Guangdong, China.
Aging (Albany NY). 2023 May 4;15(9):3480-3497. doi: 10.18632/aging.204685.
Breast cancer (BC) ranks first in the incidence of tumors in women and remains the most prevalent malignancy in women worldwide. Cancer-associated fibroblasts (CAFs) in the tumor microenvironment (TME) profoundly influence the progression, recurrence, and therapeutic resistance in BC. Here, we intended to establish a risk signature based on screened CAF-associated genes in BC (BCCGs) for patient stratification. Initially, BCCGs were screened by a combination of several CAF gene sets. The identified BCGGs were found to differ significantly in the overall survival (OS) of BC patients. Accordingly, we constructed a prognostic prediction signature of 5 BCCGs, which were independent prognostic factors associated with BC based on univariate and multivariate Cox regression. The risk model divided patients into low- and high-risk groups, accompanied by different OS, clinical features, and immune infiltration characteristics. Receiver operating characteristic (ROC) curves and a nomogram further validated the predictive performance of the prognostic model. Notably, 21 anticancer agents targeting these BCCGs possessed better sensitivity in BC patients. Meanwhile, the elevated expression of the majority of immune checkpoint genes suggested that the high-risk group may benefit more from immune checkpoint inhibitors (ICIs) therapy. Taken together, our well-established model is a robust instrument to precisely and comprehensively predict the prognosis, immune features, and drug sensitivity in BC patients, for combating BC.
乳腺癌（BC）在女性肿瘤发病率中位居第一，仍是全球女性最常见的恶性肿瘤。肿瘤微环境（TME）中的癌症相关成纤维细胞（CAFs）深刻影响着 BC 的进展、复发和治疗耐药性。在这里，我们旨在基于 BC 中筛选出的 CAF 相关基因（BCCGs）建立一个患者分层的风险特征。首先，我们通过几个 CAF 基因集的组合筛选 BCCGs。确定的 BCCGs 在 BC 患者的总生存期（OS）方面存在显著差异。因此，我们根据单因素和多因素 Cox 回归构建了一个由 5 个 BCCGs 组成的预后预测特征，这些 BCCGs 是与 BC 相关的独立预后因素。该风险模型将患者分为低风险和高风险组，同时伴有不同的 OS、临床特征和免疫浸润特征。受试者工作特征（ROC）曲线和诺莫图进一步验证了该预后模型的预测性能。值得注意的是，21 种针对这些 BCCGs 的抗癌药物在 BC 患者中具有更好的敏感性。同时，大多数免疫检查点基因的高表达表明高危组可能更受益于免疫检查点抑制剂（ICI）治疗。总之，我们建立的模型是一种精确而全面地预测 BC 患者预后、免疫特征和药物敏感性的强大工具，有助于对抗 BC。
