背景：肾细胞癌（RCC）常伴有静脉系统癌栓，预后极差。目前的肿瘤淋巴结转移 (TNM) 分期和 Mayo 临床分类无法正确识别偏好敏感的治疗。因此，迫切需要开发更好的精准医疗理想模型。方法：在本研究中，我们基于使用多个独立队列的新型计算框架，使用 10 种机器学习算法（101 种组合）开发了 RCC 凝固肿瘤血栓特征。结果：建立的肿瘤血栓凝血相关风险分层 (TTCRRS) 特征包含 10 个预后凝血相关基因 (CRG)。该签名可以预测公共和内部蛋白质队列的生存结果，并且与 129 个已发布的签名相比表现出较高的性能。此外，TTCRRS 特征与一些免疫景观、免疫治疗反应和化疗显着相关。此外，我们还根据 TTCRRS 特征筛选出了枢纽基因、转录因子和小化合物。同时，CYP51A1可以调节RCC的增殖和迁移特性。结论：TTCRRS 特征可以补充传统的解剖 TNM 分期系统和 Mayo 临床分层，为临床医生提供更多的治疗选择。© 作者。

Background: Renal cell carcinoma (RCC) is frequently accompanied by tumor thrombus in the venous system with an extremely dismal prognosis. The current Tumor Node Metastasis (TNM) stage and Mayo clinical classification do not appropriately identify preference-sensitive treatment. Therefore, there is an urgent need to develop a better ideal model for precision medicine. Methods: In this study, we developed a coagulation tumor thrombus signature for RCC with 10 machine-learning algorithms (101 combinations) based on a novel computational framework using multiple independent cohorts. Results: The established tumor thrombus coagulation-related risk stratification (TTCRRS) signature comprises 10 prognostic coagulation-related genes (CRGs). This signature could predict survival outcomes in public and in-house protein cohorts and showed high performance compared to 129 published signatures. Additionally, the TTCRRS signature was significantly related to some immune landscapes, immunotherapy response, and chemotherapy. Furthermore, we also screened out hub genes, transcription factors, and small compounds based on the TTCRRS signature. Meanwhile, CYP51A1 can regulate the proliferation and migration properties of RCC. Conclusions: The TTCRRS signature can complement the traditional anatomic TNM staging system and Mayo clinical stratification and provide clinicians with more therapeutic options.© The author(s).