Ouyang Wenhao, Peng Qing, Lai Zijia, Huang Hong, Huang Zhenjun, Xie Xinxin, Lin Ruichong, Wang Zehua, Yao Herui, Yu Yunfang
Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Department of Medicine Oncology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
Clinical Medicine College, Guangdong Medical University, Zhanjiang, China.
Heliyon. 2024 Mar 3;10(5):e27151. doi: 10.1016/j.heliyon.2024.e27151. eCollection 2024 Mar 15.
The development of immune checkpoint inhibitors (ICIs) has significantly advanced cancer treatment. However, their efficacy is not consistent across all patients, underscoring the need for personalized approaches. In this study, we examined the relationship between activated CD4 memory T cell expression and ICI responsiveness. A notable correlation was observed between increased activated CD4 memory T cell expression and better patient survival in various cohorts. Additionally, the chemokine CXCL13 was identified as a potential prognostic biomarker, with higher expression levels associated with improved outcomes. Further analysis highlighted CXCL13's role in influencing the Tumor Microenvironment, emphasizing its relevance in tumor immunity. Using these findings, we developed a deep learning model by the Multi-Layer Aggregation Graph Neural Network method. This model exhibited promise in predicting ICI treatment efficacy, suggesting its potential application in clinical practice.
免疫检查点抑制剂（ICIs）的发展显著推动了癌症治疗。然而，它们对所有患者的疗效并不一致，这凸显了个性化治疗方法的必要性。在本研究中，我们研究了活化的CD4记忆T细胞表达与ICI反应性之间的关系。在各个队列中，观察到活化的CD4记忆T细胞表达增加与患者更好的生存率之间存在显著相关性。此外，趋化因子CXCL13被确定为一种潜在的预后生物标志物，其较高的表达水平与更好的预后相关。进一步分析突出了CXCL13在影响肿瘤微环境中的作用，强调了其在肿瘤免疫中的相关性。利用这些发现，我们通过多层聚合图神经网络方法开发了一种深度学习模型。该模型在预测ICI治疗疗效方面显示出前景，表明其在临床实践中的潜在应用价值。
