Leveraging diverse cell-death patterns to predict the prognosis, immunotherapy and drug sensitivity of clear cell renal cell carcinoma

Background: Programmed cell death (PCD) on the prognosis, tumor microenvironment characteristics and immunotherapy response of patients with clear cell renal cell carcinoma (ccRCC) have not been fully elucidated. Methods: The PCD-related signature (PRS) was constructed using the least absolute shrinkage and selection operator regression (LASSO) method to evaluate the PCD characteristics of ccRCC. The E-MTAB-1980 dataset was used as an external validation set. PCD-related clusters were constructed using non-negative matrix factorization (NMF). The different algorithms were used for the investigation of the immune infiltration scores. The Cancer Immunome Atlas (TCIA) was used to download immunotherapy data for ccRCC. The Genomics of Drug Sensitivity in Cancer (GDSC) database was employed to analyze the differences in drug sensitivity of the models. Single cell sequencing data, the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and qRT-PCR were used to check for differences in protein and mRNA expression of PRGs between cancerous and paracancerous tissue. Results: PRS may be utilized to distinguish patients' prognosis, immune characteristics, tumor mutation burden, immunotherapy response, and drug sensitivity. Five genes were found to play crucial roles in the promotion of cancer and three genes in the suppression of cancer. qRT-PCR and CPTAC indicated that five genes were overexpressed and three genes were underexpressed in the ccRCC tissues. Conclusion: Overall, by synthesising different cell death patterns, we have established a novel PCD model that can accurately predict the clinical prognosis, mutational and immune characteristics of ccRCC.