Zhao Yunxia, Li Li, Yuan Shuqi, Meng Zixin, Xu Jiayi, Cai Zhaogen, Zhang Yijing, Zhang Xiaonan, Wang Tao
Department of Pathophysiology, Bengbu Medical University, Longzihu, Bengbu, Anhui, China.
Department of Pathology, Bengbu Medical University, Anqing 116 Hospital, Anqing, Anhui, China.
Front Immunol. 2025 Apr 16;16:1583103. doi: 10.3389/fimmu.2025.1583103. eCollection 2025.
Breast cancer is the most common malignancy in women globally, with significant heterogeneity affecting prognosis and treatment. RNA-binding proteins play vital roles in tumor progression, yet their prognostic potential remains unclear. This study introduces an Artificial Intelligence-Assisted RBP Signature (AIRS) model to improve prognostic accuracy and guide personalized treatment.
Data from 14 BC cohorts (9,000+ patients) were analyzed using 108 machine learning model combinations. The AIRS model, built on three key RBP genes (PGK1, MPHOSPH10, MAP2K6), stratified patients into high- and low-risk groups. Genomic alterations, single-cell transcriptomics, tumor microenvironment characteristics, and drug sensitivity were assessed to uncover AIRS-associated mechanisms.
The AIRS model demonstrated superior prognostic performance, surpassing 106 established signatures. High AIRS scores correlated with elevated tumor mutational burden, specific copy number alterations, and an immune-suppressive TME. Single-cell analysis revealed functional heterogeneity in epithelial cells, linking high AIRS scores to pathways like transcription factor binding. Regulatory network analysis identified key transcription factors such as MYC. Low AIRS scores predicted better responses to immune checkpoint inhibitors, while drug sensitivity analysis highlighted panobinostat and paclitaxel as potential therapies for high-risk patients.
The AIRS model offers a robust tool for BC prognosis and treatment stratification, integrating genomic, transcriptomic, and single-cell data. It provides actionable insights for personalized therapy, paving the way for improved clinical outcomes. Future studies should validate findings across diverse populations and expand functional analyses.
乳腺癌是全球女性中最常见的恶性肿瘤，具有显著的异质性，影响预后和治疗。RNA结合蛋白在肿瘤进展中发挥着至关重要的作用，但其预后潜力仍不明确。本研究引入了一种人工智能辅助的RBP特征（AIRS）模型，以提高预后准确性并指导个性化治疗。
使用108种机器学习模型组合分析了来自14个乳腺癌队列（9000多名患者）的数据。基于三个关键的RBP基因（PGK1、MPHOSPH10、MAP2K6）构建的AIRS模型将患者分为高风险和低风险组。评估基因组改变、单细胞转录组学、肿瘤微环境特征和药物敏感性，以揭示与AIRS相关的机制。
AIRS模型表现出卓越的预后性能，超过了106种已建立的特征。高AIRS评分与肿瘤突变负担增加、特定的拷贝数改变和免疫抑制性肿瘤微环境相关。单细胞分析揭示了上皮细胞中的功能异质性，将高AIRS评分与转录因子结合等途径联系起来。调控网络分析确定了关键的转录因子，如MYC。低AIRS评分预测对免疫检查点抑制剂有更好的反应，而药物敏感性分析突出了帕比司他和紫杉醇作为高风险患者的潜在治疗方法。
AIRS模型为乳腺癌的预后和治疗分层提供了一个强大的工具，整合了基因组、转录组和单细胞数据。它为个性化治疗提供了可操作的见解，为改善临床结果铺平了道路。未来的研究应在不同人群中验证研究结果，并扩大功能分析。
