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Medical AI Inquiry: You was designed to assist in medical decision-making based on the provided user questions. It is crucial to provide accurate and comprehensive information to receive a well-informed response.  Labels: BACKGROUND, RESULTS. Meshes: CD8-Positive T-Lymphocytes, Carcinoma, Renal Cell, Computer Simulation, Gene Expression Regulation, Neoplastic, Humans, Immunotherapy, Lymphocytes, Tumor-Infiltrating, Neoplasm Proteins, Nucleotide Motifs, Prognosis, Proteomics, RNA, Messenger, Tumor Microenvironment.
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Does tumor immune microenvironment characterization in clear cell renal cell carcinoma identify prognostic and immunotherapeutically relevant messenger RNA signatures? Context: Tumor-infiltrating immune cells have been linked to prognosis and response to immunotherapy; however, the levels of distinct immune cell subsets and the signals that draw them into a tumor, such as the expression of antigen presenting machinery genes, remain poorly characterized. Here, we employ a gene expression-based computational method to profile the infiltration levels of 24 immune cell populations in 19 cancer types. We compare cancer types using an immune infiltration score and a T cell infiltration score and find that clear cell renal cell carcinoma (ccRCC) is among the highest for both scores. Using immune infiltration profiles as well as transcriptomic and proteomic datasets, we characterize three groups of ccRCC tumors: T cell enriched, heterogeneously infiltrated, and non-infiltrated. We observe that the immunogenicity of ccRCC tumors cannot be explained by mutation load or neo-antigen load, but is highly correlated with MHC class I antigen presenting machinery expression (APM). We explore the prognostic value of distinct T cell subsets and show in two cohorts that Th17 cells and CD8
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Our analysis sheds light on the immune infiltration patterns of 19 human cancers and unravels mRNA signatures with prognostic utility and immunotherapeutic biomarker potential in ccRCC. Final Decision: yes