Wang Xiaofei, Zhang Pengpeng, Ye Wei, Du Mingjun, Huang Chenjun, Zheng Jianan
Department of Lung Cancer, Tianjin Lung Cancer Center, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.
Department of Thoracic Surgery, The First Affiliated Hospital with Nanjing Medical University, Nanjing, China.
J Cell Mol Med. 2025 Aug;29(15):e70731. doi: 10.1111/jcmm.70731.
The immunoregulatory functions and clinical implications of APOE+ macrophages within the tumour microenvironment of lung adenocarcinoma remain incompletely defined. In this study, single-cell transcriptome analysis revealed distinct subsets of APOE+ macrophages, and subsequent CellChat analyses highlighted that these cells predominantly interact with other components of the tumour microenvironment via MIF-(CD74+CXCR4) and MIF-(CD74+CD44) signalling pathways, thereby contributing to the establishment of an immunosuppressive milieu. Integrating mutation profiles with multiple machine learning techniques, we developed an APOE+ Macrophage-related Risk Model (ARM) through a combination of RSF and Ridge approaches, achieving the highest prognostic accuracy (C-index) among all tested algorithms. The robustness and predictive value of the ARM model were validated across seven public cohorts and three prospective immunotherapy cohorts. Patients classified in the low-ARM group consistently exhibited better prognoses, as demonstrated by survival analyses, ROC curves and PCA discrimination. Additionally, multiplex immunofluorescence analysis in the in-house cohort confirmed significantly decreased infiltration of CD4+, CD8+ and CD20+ immune cells in the high-ARM group, further supporting a more pronounced immunosuppressive microenvironment in these patients. Collectively, our findings not only clarify the critical role of APOE+ macrophages in shaping the immune landscape and prognosis of lung adenocarcinoma, but also provide a validated, practical risk model for individualised patient prognostication and clinical management.
在肺腺癌肿瘤微环境中，APOE+巨噬细胞的免疫调节功能及其临床意义仍未完全明确。在本研究中，单细胞转录组分析揭示了APOE+巨噬细胞的不同亚群，随后的CellChat分析强调，这些细胞主要通过MIF-（CD74+CXCR4）和MIF-（CD74+CD44）信号通路与肿瘤微环境的其他成分相互作用，从而促进免疫抑制环境的形成。通过将突变谱与多种机器学习技术相结合，我们通过随机生存森林（RSF）和岭回归方法的组合开发了一种APOE+巨噬细胞相关风险模型（ARM），在所有测试算法中实现了最高的预后准确性（C指数）。ARM模型的稳健性和预测价值在七个公共队列和三个前瞻性免疫治疗队列中得到了验证。生存分析、ROC曲线和主成分分析鉴别结果表明，低ARM组患者的预后始终较好。此外，内部队列中的多重免疫荧光分析证实，高ARM组中CD4+、CD8+和CD20+免疫细胞的浸润显著减少，进一步支持了这些患者中更明显的免疫抑制微环境。总体而言，我们的研究结果不仅阐明了APOE+巨噬细胞在塑造肺腺癌免疫格局和预后方面的关键作用，还为个体化患者预后评估和临床管理提供了一个经过验证的实用风险模型。
