Machine learning-derived natural killer cell signature predicts prognosis and therapeutic response in clear cell renal cell carcinoma.

ABSTRACT:

Background

Natural killer cells, interconnected with patient prognosis and treatment response, play a pivotal role in the tumor immune microenvironment and may serve as potential novel predictive biomarkers for renal cell carcinoma.

Methods

Clear cell renal cell carcinoma transcriptome data and the corresponding clinical data were obtained from the Cancer Genome Atlas (TCGA) database. Single-cell sequencing data were sourced from the Gene Expression Omnibus (GEO) database. A risk model was established by integrating ten different machine learning algorithms, which resulted in 101 combined models. The model with the highest average C-index was selected for further analysis, and was assessed using nomogram, time-dependent receiver operating characteristics (ROC) and Kaplan-Mei

SUBMITTER: Luo J 

PROVIDER: S-EPMC11600779 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature