Predicting CTLA4 expression and prognosis in clear cell renal cell carcinoma using a pathomics signature of histopathological images and machine learning.

ABSTRACT:

Background

CTLA4, an immune checkpoint, plays an important role in tumor immunotherapy. The purpose of this study was to develop a pathomics signature to evaluate CTLA4 expression and predict clinical outcomes in clear cell renal cell carcinoma (ccRCC) patients.

Methods

A total of 354 patients from the TCGA-KIRC dataset were enrolled in this study. The patients were stratified into two groups based on the level of CTLA4 expression, and overall survival rates were analyzed between groups. Pathological features were identified using machine learning algorithms, and a gradient boosting machine (GBM) was employed to construct the pathomics signatures for predicting prognosis and CTLA4 expression. The predictive performance of the model was subsequently assessed. Enrichment analy

SUBMITTER: Yang X 

PROVIDER: S-EPMC11320204 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature