Identification of prognostic coagulation-related signatures in clear cell renal cell carcinoma through integrated multi-omics analysis and machine learning

Clear cell renal cell carcinoma is a threat to public health with high morbidity and mortality. Clinical evidence has shown that cancer-associated thrombosis poses significant challenges to treatments, including drug resistance and difficulties in surgical decision-making in ccRCC. However, the coagulation pathway, one of the core mechanisms of cancer-associated thrombosis, recently found closely related to the tumor microenvironment and immune-related pathway, is rarely researched in ccRCC. Therefore, we integrated bulk RNA-seq data, DNA mutation and methylation data, single-cell data, and proteomic data to perform a comprehensive analysis of coagulation-related genes in ccRCC. First, we demonstrated the importance of the coagulation-related gene set by consensus clustering. Based on machine learning, we identified 5 coagulation signature genes and verified their clinical value in TCGA, ICGC, and E-MTAB-1980 databases. It's also demonstrated that the specific expression patterns of coagulation signature genes driven by CNV and methylation were closely correlated with pathways including apoptosis, immune infiltration, angiogenesis, and the construction of extracellular matrix. Moreover, we identified two types of tumor cells in single-cell data by machine learning, and the coagulation signature genes were differentially expressed in two types of tumor cells. Besides, the signature genes were proven to influence immune cells especially the differentiation of T cells. And their protein level was also validated.