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

Gaosheng Yao ¹ Junshang Dai ^2,3 Juan Lin ⁴ Zhiping Tan ¹ Lei Dai ¹ Wei Chen ¹ Junhang Luo ^1,5* Jinhuan Wei ^6*

• ¹ Department of Urology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
• ² Department of Obstetrics and Gynaecology, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong Region, China
• ³ Department of Obstetrics and Gynecology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong Province, China
• ⁴ Department of Pediatrics, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
• ⁵ Institute of Precision Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong Province, China
• ⁶ The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China

Background: Increasing evidences have showed that necroptosis has a unique clinical significance in the occurrence and development of multiple diseases. Here, we systematically evaluated the role of necroptosis in clear cell renal cell carcinoma (ccRCC) and analyzed 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), and demonstrated 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, efficacy of immunotherapy containing immune checkpoint inhibitors (ICIs), and suggested potential therapeutic strategies.We screened 97 necroptosis-related genes and demonstrated that they were dysregulated in ccRCC. Through Cox analysis and the Least Absolute Shrinkage and Selection Operator (LASSO) regression, a prognostic prediction signature including 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. Besides, the predicted scores based on the signature were observably associated with immune-cell infiltration and mutation of specific genes. In addition, the risk score could potentially predict the patients' responsiveness to different chemotherapy regimens. In specific, Nivolumab is more effective for patients with higher scores.The necroptosis-related signature we constructed can accurately predict the prognosis of ccRCC patients, and further providing clues for targeted, individualized therapy.