Predictive model for clear cell renal cell carcinoma: a novel model integrating sunitinib resistance and prognosis related genes and clinical factors.

Abstract

Purpose: Sunitinib serves as the primary treatment for clear cell renal cell carcinoma (ccRCC). However, patients frequently develop resistance to it over time. Early prediction of sunitinib resistance and ccRCC prognosis is beneficial for optimizing clinical treatment. Therefore, we aimed to identify key genes associated with ccRCC sunitinib resistance and prognosis and develop a Clinical Gene Prognostic Nomogram Model (CGPNM) to assess drug sensitivity, treatment response, and survival outcomes in patient groups stratified by CGPNM nomogram scores.

Methods: Key genes associated with sunitinib resistance and ccRCC prognosis were identified using weighted gene coexpression network analysis (WGCNA), differential gene expression analysis, and Cox regression analysis. Single-cell RNA sequencing (RNA-seq) analysis and gene set enrichment analysis (GSEA) revealed the expression patterns of these genes, and clinical factors were subsequently incorporated into the gene prognosis model (GPM) to develop a clinically-genetic prognosis nomogram model (CGPNM). Patients were divided into low risk and high risk groups based on the nomogram scores derived. Drug sensitivity, treatment response, and survival outcomes were assessed in patients in different risk groups.

Results: Interferon-induced transmembrane protein 1 (IFITM1), inositol monophosphatase 2 (IMPA2), and potassium calcium-activated channel subfamily N member 3 (KCNN3) are closely linked to sunitinib resistance and prognosis in clear cell renal cell carcinoma (ccRCC), each exhibiting distinct expression patterns: IFITM1 is expressed in lymphocytes, IMPA2 in tumor/epithelial cells, and KCNN3 in endothelial cells. Using these genes, the GPM was developed to predict drug resistance and prognosis in ccRCC patients. Furthermore, CGPNM models developed for key genes and clinical factors showed that patients with low or high CGPNM scores showed significant differences in drug sensitivity to chemotherapy and targeted drugs, and had different responses to chemotherapy, immunotherapy, and targeted therapy.

Conclusion: IFITM1, IMPA2 and KCNN3 emerged as key genes influencing sunitinib resistance and prognosis in ccRCC patients. The CGPNM, integrating genetic and clinical factors, effectively predicts survival and drug sensitivity among ccRCC patients, demonstrating robust performance. This model holds promise as a reliable tool for guiding clinical medication in ccRCC.