A risk signature based on necroptotic-process-related genes predicts prognosis and immune therapy response in kidney cell carcinoma.

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However, the molecular mechanisms and prognosis prediction of necroptotic-process-related genes in RCC are still unclear. In this study, we first identified the necroptotic process prognosis-related genes (NPRGss) by analyzing the kidney renal clear cell carcinoma (KIRC) data in The Cancer Genome Atlas (TCGA, n=607). We systematically analyzed the expression alteration, clinical relevance, and molecular mechanisms of NPRGss in renal clear cell carcinoma. We constructed an NPRGs risk signature utilizing the least absolute shrinkage and selection operator (LASSO) Cox regression analysis on the basis of the expression of seven NPRGss. We discovered that the overall survival (OS) of KIRC patients differed significantly in high- or low-NPRGs-risk groups. The univariate/multivariate Cox regression revealed that the NPRGs risk signature was an independent prognosis factor in RCC. The gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were used to explore the molecular mechanisms of NPRGss. Immune-/metabolism-related pathways showed differential enrichment in high-/low-NPRGs-risk groups. The E-MTAB-1980, TCGA-KIRP, GSE78220, the cohort of Alexandra et al., and IMvigor210 cohort datasets were respectively used as independent validation cohorts of NPRGs risk signature. The patients in high- or low-NPRGs-risk groups showed different drug sensitivity, immune checkpoint expression, and immune therapy response. Finally, we established a nomogram based on the NPRGs risk signature, stage, grade, and age for eventual clinical translation; the nomogram possesses an accurate and stable prediction effect. The signature could predict patients' prognosis and therapy response, which provides the foundation for further clinical therapeutic strategies for RCC patients.", "claims": [{"id": "claim-method", "statement": "Then, the LASSO-penalized Cox (LASSO-Cox) regression analysis was carried out to rule out genes with an overfitting tendency and construct a prognosis-related signature with the glmnet R-package.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36189275", "locator": "sections[8] \"Construction and validation of a NPRGss risk signature\" paragraph 2", "quote": "Then, the LASSO-penalized Cox (LASSO-Cox) regression analysis was carried out to rule out genes with an overfitting tendency and construct a prognosis-related signature with the glmnet R-package."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. 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