Li Xiangyun, Yang Xiaoqun, Yang Xianwei, Xie Xin, Rui Wenbin, He Hongchao
Department of Pathology, Shanghai Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Department of Urology, Shanghai Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Technol Cancer Res Treat. 2024 Jan-Dec;23:15330338241307686. doi: 10.1177/15330338241307686.
Clear cell renal cell carcinoma (ccRCC) is a highly lethal urinary malignancy with poor overall survival (OS) rates. Integrating computer vision and machine learning in pathomics analysis offers potential for enhancing classification, prognosis, and treatment strategies for ccRCC. This study aims to create a pathomics model to predict OS in ccRCC patients. In this study, data from ccRCC patients in the TCGA database were used as a training set, with clinical data serving as a validation set. Pathological features were extracted from H&E-stained slides using PyRadiomics, and a pathomics model was constructed using the non-negative matrix factorization (NMF) algorithm. The model's predictive performance was assessed through Kaplan-Meier (KM) survival curves and Cox regression analysis. Additionally, differential gene expression, gene ontology (GO) enrichment analysis, immune infiltration, and mutational analysis were conducted to investigate the underlying biological mechanisms. A total of 368 pathomics features were extracted from H&E-stained slides of ccRCC patients, and a pathomics model comprising two subtypes (Cluster 1 and Cluster 2) was successfully constructed using the NMF algorithm. KM survival curves and Cox regression analysis revealed that Cluster 2 was associated with worse OS. A total of 76 differential genes were identified between the two subtypes, primarily involving extracellular matrix organization and structure. Immune-related genes, including CTLA4, CD80, and TIGIT, were highly expressed in Cluster 2, while the VHL and PBRM1 genes, along with mutations in the PI3K-Akt, HIF-1, and MAPK signaling pathways, exhibited mutation rates exceeding 40% in both subtypes. The machine learning-based pathomics model effectively predicts the OS of ccRCC patients and differentiates between subtypes. The critical roles of the immune-related gene CTLA4 and the PI3K-Akt, HIF-1, and MAPK signaling pathways offer new insights for further research on the molecular mechanisms, diagnosis, and treatment strategies for ccRCC.
透明细胞肾细胞癌（ccRCC）是一种具有高致死率的泌尿系统恶性肿瘤，总体生存率（OS）较低。将计算机视觉和机器学习整合到病理组学分析中，为改善ccRCC的分类、预后和治疗策略提供了潜力。本研究旨在创建一个病理组学模型来预测ccRCC患者的OS。在本研究中，来自TCGA数据库中ccRCC患者的数据用作训练集，临床数据用作验证集。使用PyRadiomics从苏木精-伊红（H&E）染色的切片中提取病理特征，并使用非负矩阵分解（NMF）算法构建病理组学模型。通过Kaplan-Meier（KM）生存曲线和Cox回归分析评估该模型的预测性能。此外，还进行了差异基因表达、基因本体（GO）富集分析、免疫浸润和突变分析，以探究潜在的生物学机制。从ccRCC患者的H&E染色切片中总共提取了368个病理组学特征，并使用NMF算法成功构建了一个包含两个亚型（Cluster 1和Cluster 2）的病理组学模型。KM生存曲线和Cox回归分析显示，Cluster 2与较差的OS相关。在两个亚型之间总共鉴定出76个差异基因，主要涉及细胞外基质组织和结构。包括CTLA4、CD80和TIGIT在内的免疫相关基因在Cluster 2中高表达，而VHL和PBRM1基因以及PI3K-Akt、HIF-1和MAPK信号通路中的突变在两个亚型中的突变率均超过40%。基于机器学习的病理组学模型能够有效地预测ccRCC患者的OS并区分亚型。免疫相关基因CTLA4以及PI3K-Akt、HIF-1和MAPK信号通路的关键作用，为进一步研究ccRCC的分子机制、诊断和治疗策略提供了新的见解。
