PDF(Pubmed)
Abstract：
UNASSIGNED: This study aimed to delineate the clear cell renal cell carcinoma (ccRCC) intrinsic subtypes through unsupervised clustering of radiomics and transcriptomics data and to evaluate their associations with clinicopathological features, prognosis, and molecular characteristics.
UNASSIGNED: Using a retrospective dual-center approach, we gathered transcriptomic and clinical data from ccRCC patients registered in The Cancer Genome Atlas and contrast-enhanced computed tomography images from The Cancer Imaging Archive and local databases. Following the segmentation of images, radiomics feature extraction, and feature preprocessing, we performed unsupervised clustering based on the \"CancerSubtypes\" package to identify distinct radiotranscriptomic subtypes, which were then correlated with clinical-pathological, prognostic, immune, and molecular characteristics.
UNASSIGNED: Clustering identified three subtypes, C1, C2, and C3, each of which displayed unique clinicopathological, prognostic, immune, and molecular distinctions. Notably, subtypes C1 and C3 were associated with poorer survival outcomes than subtype C2. Pathway analysis highlighted immune pathway activation in C1 and metabolic pathway prominence in C2. Gene mutation analysis identified VHL and PBRM1 as the most commonly mutated genes, with more mutated genes observed in the C3 subtype. Despite similar tumor mutation burdens, microsatellite instability, and RNA interference across subtypes, C1 and C3 demonstrated greater tumor immune dysfunction and rejection. In the validation cohort, the various subtypes showed comparable results in terms of clinicopathological features and prognosis to those observed in the training cohort, thus confirming the efficacy of our algorithm.
UNASSIGNED: Unsupervised clustering based on radiotranscriptomics can identify the intrinsic subtypes of ccRCC, and radiotranscriptomic subtypes can characterize the prognosis and molecular features of tumors, enabling noninvasive tumor risk stratification.
UNASSIGNED: Using a retrospective dual-center approach, we gathered transcriptomic and clinical data from ccRCC patients registered in The Cancer Genome Atlas and contrast-enhanced computed tomography images from The Cancer Imaging Archive and local databases. Following the segmentation of images, radiomics feature extraction, and feature preprocessing, we performed unsupervised clustering based on the \"CancerSubtypes\" package to identify distinct radiotranscriptomic subtypes, which were then correlated with clinical-pathological, prognostic, immune, and molecular characteristics.
UNASSIGNED: Clustering identified three subtypes, C1, C2, and C3, each of which displayed unique clinicopathological, prognostic, immune, and molecular distinctions. Notably, subtypes C1 and C3 were associated with poorer survival outcomes than subtype C2. Pathway analysis highlighted immune pathway activation in C1 and metabolic pathway prominence in C2. Gene mutation analysis identified VHL and PBRM1 as the most commonly mutated genes, with more mutated genes observed in the C3 subtype. Despite similar tumor mutation burdens, microsatellite instability, and RNA interference across subtypes, C1 and C3 demonstrated greater tumor immune dysfunction and rejection. In the validation cohort, the various subtypes showed comparable results in terms of clinicopathological features and prognosis to those observed in the training cohort, thus confirming the efficacy of our algorithm.
UNASSIGNED: Unsupervised clustering based on radiotranscriptomics can identify the intrinsic subtypes of ccRCC, and radiotranscriptomic subtypes can characterize the prognosis and molecular features of tumors, enabling noninvasive tumor risk stratification.
摘要：
■本研究旨在通过对影像组学和转录组学数据的无监督聚类来描绘透明细胞肾细胞癌（ccRCC）固有亚型，并评估其与临床病理特征的关联。预后,和分子特征。
■使用回顾性双中心方法，我们收集了癌症基因组图谱中登记的ccRCC患者的转录组和临床数据,以及癌症成像档案和当地数据库中的对比增强计算机断层扫描图像.在图像分割之后，影像组学特征提取，和功能预处理，我们基于“CancerSubtypes”包执行无监督聚类，以识别不同的放射性转录组学亚型，然后与临床病理相关，预后，免疫，和分子特征。
■聚类确定了三个子类型，C1，C2和C3，每个都显示出独特的临床病理，预后，免疫，和分子区别。值得注意的是,C1和C3亚型的生存结局比C2亚型差.路径分析强调了C1中的免疫途径激活和C2中的代谢途径突出。基因突变分析确定VHL和PBRM1是最常见的突变基因，在C3亚型中观察到更多突变基因。尽管类似的肿瘤突变负担，微卫星不稳定，和跨亚型的RNA干扰，C1和C3表现出更大的肿瘤免疫功能障碍和排斥反应。在验证队列中，各种亚型在临床病理特征和预后方面与训练队列中观察到的结果相当，从而证实了我们算法的有效性。
■基于放射性转录组学的无监督聚类可以识别ccRCC的内在亚型，和放射转录组亚型可以表征肿瘤的预后和分子特征，实现非侵入性肿瘤风险分层。
■使用回顾性双中心方法，我们收集了癌症基因组图谱中登记的ccRCC患者的转录组和临床数据,以及癌症成像档案和当地数据库中的对比增强计算机断层扫描图像.在图像分割之后，影像组学特征提取，和功能预处理，我们基于“CancerSubtypes”包执行无监督聚类，以识别不同的放射性转录组学亚型，然后与临床病理相关，预后，免疫，和分子特征。
■聚类确定了三个子类型，C1，C2和C3，每个都显示出独特的临床病理，预后，免疫，和分子区别。值得注意的是,C1和C3亚型的生存结局比C2亚型差.路径分析强调了C1中的免疫途径激活和C2中的代谢途径突出。基因突变分析确定VHL和PBRM1是最常见的突变基因，在C3亚型中观察到更多突变基因。尽管类似的肿瘤突变负担，微卫星不稳定，和跨亚型的RNA干扰，C1和C3表现出更大的肿瘤免疫功能障碍和排斥反应。在验证队列中，各种亚型在临床病理特征和预后方面与训练队列中观察到的结果相当，从而证实了我们算法的有效性。
■基于放射性转录组学的无监督聚类可以识别ccRCC的内在亚型，和放射转录组亚型可以表征肿瘤的预后和分子特征，实现非侵入性肿瘤风险分层。
