Deciphering the tumour microenvironment of clear cell renal cell carcinoma: Prognostic insights from programmed death genes using machine learning.

ABSTRACT: Clear cell renal cell carcinoma (ccRCC), a prevalent kidney cancer form characterised by its invasiveness and heterogeneity, presents challenges in late-stage prognosis and treatment outcomes. Programmed cell death mechanisms, crucial in eliminating cancer cells, offer substantial insights into malignant tumour diagnosis, treatment and prognosis. This study aims to provide a model based on 15 types of Programmed Cell Death-Related Genes (PCDRGs) for evaluating immune microenvironment and prognosis in ccRCC patients. ccRCC patients from the TCGA and arrayexpress cohorts were grouped based on PCDRGs. A combination model using Lasso and SuperPC was constructed to identify prognostic gene features. The arrayexpress cohort validated the model, confirming its robustness. Immune microenvironment

SUBMITTER: Tu H 

PROVIDER: S-EPMC11249822 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature