摘要
Background:The tumor microenvironment(TME)performs a crucial function in the tumorigenesis and response to immunotherapies of clear cell renal cell carcinoma(ccRCC).However,a lack of recognized pre-clinical TME-based risk models poses a great challenge to investigating the risk factors correlated with prognosis and treatment responses for patients with ccRCC.Methods:Stromal and immune contexture were assessed to calculate the TMErisk score of a large sample of patients with ccRCC from public and real-world cohorts using machine-learning algorithms.Next,analyses for prognostic efficacy,correlations with clinicopathological features,functional enrichment,immune cell distribu-tions,DNA variations,immune response,and heterogeneity were performed and validated.Results:Clinical hub genes,including INAFM2,SRPX,DPYSL3,VSIG4,APLNR,FHL5,A2M,SLFN11,ADAMTS4,IFITM1,NOD2,CCR4,HLA-DQB2,and PLAUR,were identified and incorporated to develop the TMErisk signature.Patients in the TME high risk group(category)exhibited a considerably grim prognosis,and the TMErisk model was shown to independently function as a risk indicator for the overall survival(OS)of ccRCC patients.Expression levels of immune checkpoint genes were substantially increased in TME high risk group,while those of the human leukocyte antigen(HLA)family genes were prominently decreased.In addition,tumors in the TME high group showed significantly high infiltration levels of tumor-infiltrated lymphocytes,including M2 macrophages,CD8+T cells,B cells,and CD4+T cells.In heterogeneity analysis,more frequent somatic mutations,including pro-tumorigenic BAP1 and PBRM1,were observed in the TME high group.Importantly,19.3%of patients receiving immunotherapies in the TME high group achieved complete or partial response compared with those with immune tolerance in the TME low group,suggesting that TMErisk prominently differentiates prognosis and responses to immunotherapy for patients with ccRCC.Conclusions:We first established the TMErisk score of ccRCC using machine-learning al
基金
supported by grants from the National Natural Science Foundation of China(grant numbers:81802525 and 82172817)
the Natural Science Foundation of Shanghai(grant number:20ZR1413100)
Beijing Xisike Clinical Oncology Research Foundation(grant number:Y-HR2020MS-0948)
the National Key Research and Development Project(grant number:2019YFC1316005)
the Shanghai“Science and Technology Innovation Action Plan”Medical Innovation Research Project(grant number:22Y11905100)
the Shanghai Anti-Cancer Association Eyas Project(grant numbers:SACA-CY21A06 and SACA-CY21B01).
