Development and validation of hierarchical signature for precision individualized therapy based on the landscape associated with necroptosis in clear cell renal cell carcinoma

Background Increasing evidence is showing that necroptosis has unique clinical significance in the occurrence and development of multiple diseases. Here, we systematically evaluate the role of necroptosis in clear cell renal cell carcinoma (ccRCC) and analyze its regulatory patterns. Methods First, we evaluated the expression and enrichment of necroptotic factors in ccRCC using gene set enrichment analysis (GSEA) and survival analysis in the expression profile from The Cancer Genome Atlas (TCGA) to demonstrate the overall mutation of necroptotic pathway genes. Then, we used unsupervised clustering to divide the samples into two subtypes related to necroptosis with significant differences in overall survival (OS) and subsequently detected the differentially expressed genes (DEGs) between them. Based on this, we constructed the necroptosis scoring system (NSS), which also performed outstandingly in hierarchical data. Finally, we analyzed the association between NSS and clinical parameters, immune infiltration, and the efficacy of immunotherapy containing immune checkpoint inhibitors (ICIs), and we suggested potential therapeutic strategies. Results We screened 97 necroptosis-related genes and demonstrated that they were dysregulated in ccRCC. Using Cox analysis and least absolute shrinkage and selection operator (LASSO) regression, a prognostic prediction signature of seven genes was built. Receiver operating characteristic (ROC) curves and Kaplan–Meier (KM) analyses both showed that the model was accurate, and univariate/multivariate Cox analysis showed that as an independent prognostic factor, the higher the risk score, the poorer the survival outcome. Furthermore, the predicted scores based on the signature were observably associated with immune cell infiltration and the mutation of specific genes. In addition, the risk score could potentially predict patients’ responsiveness to different chemotherapy regimens. Specifically, Nivolumab is more effective for patients with higher scores. Conclusion The necroptosis-related signature we constructed can accurately predict the prognosis of ccRCC patients and further provide clues for targeted, individualized therapy.