Wei Lengyun, Li Dashuai, Chen Hongjin, Pu Yajing, Wang Qun, Li Jintao, Zhou Meng, Liu Chenfeng, Long Pengpeng
School of Life Science, Anhui Medical University, Hefei 230032, China.
Biology (Basel). 2025 May 13;14(5):539. doi: 10.3390/biology14050539.
Breast cancer (BC) is among the most prevalent malignancies and remains the leading cause of cancer-related mortality in women worldwide. While prior studies have highlighted the associations between insulin resistance (IR) and both tumorigenesis and cancer progression, the prognostic relevance of IR in BC has not been fully elucidated. In this study, we employed a suite of machine learning algorithms and statistical methods to construct a robust prognostic model for BC based on insulin resistance-related genes (IRGs). The model's prognostic value was subsequently validated in four independent validate cohorts, including METABRIC and three GSE datasets. The resulting IR signature, comprising seven hub IRGs (LIFR, EZR, TBC1D4, NSF, RPL5, SAA1, and PGK1), demonstrated high predictive power for overall survival (OS) across public datasets. Notably, a lower insulin resistance risk score (IRRS) was significantly associated with more favorable clinical outcomes, including enhanced responses to neoadjuvant therapy. Based on single-cell RNA sequencing data, we found that the hub genes were more enriched in T cells, B cells, and epithelial cells. Furthermore, we used machine learning methods to perform feature selection and reduction, which generated a clinically applicable scoring system consisting of the seven hub genes for predicting clinical outcomes in BC patients. This novel IR-based prognostic signature offers a valuable tool for stratifying BC patients by risk and tailoring personalized therapeutic strategies, thus enhancing precision oncology in breast cancer care.
乳腺癌（BC）是最常见的恶性肿瘤之一，并且仍然是全球女性癌症相关死亡的主要原因。虽然先前的研究强调了胰岛素抵抗（IR）与肿瘤发生和癌症进展之间的关联，但IR在BC中的预后相关性尚未完全阐明。在本研究中，我们采用了一套机器学习算法和统计方法，基于胰岛素抵抗相关基因（IRG）构建了一个强大的BC预后模型。随后在四个独立的验证队列中验证了该模型的预后价值，包括METABRIC和三个GSE数据集。由此产生的IR特征，包括七个核心IRG（LIFR、EZR、TBC1D4、NSF、RPL5、SAA1和PGK1），在公共数据集中显示出对总生存期（OS）的高预测能力。值得注意的是，较低的胰岛素抵抗风险评分（IRRS）与更有利的临床结果显著相关，包括对新辅助治疗的反应增强。基于单细胞RNA测序数据，我们发现核心基因在T细胞、B细胞和上皮细胞中更为富集。此外，我们使用机器学习方法进行特征选择和降维，生成了一个由七个核心基因组成的临床适用评分系统，用于预测BC患者的临床结果。这种基于IR的新型预后特征为按风险对BC患者进行分层和制定个性化治疗策略提供了一个有价值的工具，从而提高乳腺癌护理中的精准肿瘤学水平。
