Prognostic significance of immune evasion-related genes in clear cell renal cell carcinoma immunotherapy.
Tingxuan HuangYulu PengRuiqi LiuBinglei MaJunlin ChenWensu WeiWeifeng ZhongYang LiuShengjie GuoHui HanFangjian ZhouZhiling ZhangLiru HePei DongPublished in: International immunopharmacology (2024)
Clear cell renal cell carcinoma (ccRCC) represents a prevalent malignancy of the urinary system. Despite the integration of immune checkpoint inhibitors (ICIs) into the treatment paradigm for advanced RCC, resistance to immunotherapy has emerged as a pivotal determinant impacting the clinical outlook of ccRCC. Accumulating evidence underscores the pivotal role of immune evasion-related genes and pathways in enabling tumor escape from host immune surveillance, consequently influencing patients' responsiveness to immunotherapy. Nonetheless, the clinical relevance of immune evasion-related genes in ccRCC patients undergoing immunotherapy remains inadequately understood. In this study, we aggregated RNA sequencing and clinical data from ccRCC patients across three cohorts: the Cancer Genome Atlas (TCGA), CheckMate cohorts, and the JAVELIN Renal 101 trial. Leveraging a curated immune evasion-related gene set from Lawson et al., we employed the LASSO algorithm and Cox regression analysis to identify eight genes (LPAR6, RGS5, NFYC, PCDH17, CENPW, CNOT8, FOXO3, SNRPB) significantly associated with immune therapy prognosis (HR, 3.57; 95 % CI, 2.38-5.35; P<0.001). A predictive algorithm developed utilizing these genes exhibited notable accuracy in forecasting patients' progression-free survival in the training set (AUC, 0.835). Furthermore, stratification of patients by risk score revealed discernible differences in immunotherapy response and tumor microenvironment. In summary, we present a prognostic model intricately linked with immune status and treatment response. For ccRCC patients undergoing immunotherapy, this approach holds promise in aiding clinical decision-making by providing more precise and tailored treatment recommendations.
Keyphrases
- end stage renal disease
- patients undergoing
- chronic kidney disease
- newly diagnosed
- ejection fraction
- prognostic factors
- genome wide
- stem cells
- public health
- machine learning
- young adults
- gene expression
- single cell
- dna methylation
- squamous cell carcinoma
- electronic health record
- cell proliferation
- deep learning
- artificial intelligence
- big data
- drug induced
- copy number
- open label
- squamous cell