Yang Xiaoqun, Li Xiangyun, Xu Haimin, Du Silin, Wang Chaofu, He Hongchao
Department of Pathology, Shanghai Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
University Hospital, Shanghai Jiaotong University, Shanghai, China.
Heliyon. 2024 Jul 18;10(15):e34877. doi: 10.1016/j.heliyon.2024.e34877. eCollection 2024 Aug 15.
CTLA4, an immune checkpoint, plays an important role in tumor immunotherapy. The purpose of this study was to develop a pathomics signature to evaluate CTLA4 expression and predict clinical outcomes in clear cell renal cell carcinoma (ccRCC) patients.
A total of 354 patients from the TCGA-KIRC dataset were enrolled in this study. The patients were stratified into two groups based on the level of CTLA4 expression, and overall survival rates were analyzed between groups. Pathological features were identified using machine learning algorithms, and a gradient boosting machine (GBM) was employed to construct the pathomics signatures for predicting prognosis and CTLA4 expression. The predictive performance of the model was subsequently assessed. Enrichment analysis was performed on diferentially expressed genes related to the pathomics score (PS). Additionally, correlations between PS and TMB, as well as immune infiltration profiles associated with different PS values, were explored. experiments, CTLA4 knockdown was performed to investigate its impact on cell proliferation, migration, invasion, TGF-β signaling pathway, and macrophage polarization.
High expression of CTLA4 was associated with an unfavorable prognosis in ccRCC patients. The pathomics signature displayed good performance in the validation set (AUC = 0.737; < 0.001 in the log-rank test). The PS was positively correlated with CTLA4 expression. We next explored the underlying mechanism and found the associations between the pathomics signature and TGF-β signaling pathways, TMB, and Tregs. Further experiments demonstrated that CTLA4 knockdown inhibited cell proliferation, migration, invasion, TGF-β expression, and macrophage M2 polarization.
High expression of CTLA4 was found to correlate with poor prognosis in ccRCC patients. The pathomics signature established by our group using machine learning effectively predicted both patient prognosis and CTLA4 expression levels in ccRCC cases.
CTLA4作为一种免疫检查点，在肿瘤免疫治疗中发挥着重要作用。本研究旨在开发一种病理组学特征，以评估CTLA4表达并预测透明细胞肾细胞癌（ccRCC）患者的临床结局。
本研究纳入了来自TCGA-KIRC数据集的354例患者。根据CTLA4表达水平将患者分为两组，并分析两组之间的总生存率。使用机器学习算法识别病理特征，并采用梯度提升机（GBM）构建用于预测预后和CTLA4表达的病理组学特征。随后评估该模型的预测性能。对与病理组学评分（PS）相关的差异表达基因进行富集分析。此外，还探讨了PS与肿瘤突变负荷（TMB）之间的相关性，以及与不同PS值相关的免疫浸润谱。通过实验，进行CTLA4敲低以研究其对细胞增殖、迁移、侵袭、TGF-β信号通路和巨噬细胞极化的影响。
CTLA4高表达与ccRCC患者的不良预后相关。病理组学特征在验证集中表现出良好的性能（AUC = 0.737；对数秩检验P < 0.001）。PS与CTLA4表达呈正相关。接下来，我们探索了潜在机制，发现病理组学特征与TGF-β信号通路、TMB和调节性T细胞（Tregs）之间存在关联。进一步的实验表明，CTLA4敲低抑制了细胞增殖、迁移、侵袭、TGF-β表达和巨噬细胞M2极化。
发现CTLA4高表达与ccRCC患者的预后不良相关。我们团队使用机器学习建立的病理组学特征有效地预测了ccRCC病例中的患者预后和CTLA4表达水平。
