Machine learning-driven mast cell gene signatures for prognostic and therapeutic prediction in prostate cancer.

ABSTRACT:

Background

The role of Mast cells has not been thoroughly explored in the context of prostate cancer's (PCA) unpredictable prognosis and mixed immunotherapy outcomes. Our research aims to employs a comprehensive computational methodology to evaluate Mast cell marker gene signatures (MCMGS) derived from a global cohort of 1091 PCA patients. This approach is designed to identify a robust biomarker to assist in prognosis and predicting responses to immunotherapy.

Methods

This study initially identified mast cell-associated biomarkers from prostate adenocarcinoma (PRAD) patients across six international cohorts. We employed a variety of machine learning techniques, including Random Forest, Support Vector Machine (SVM), Lasso regression, and the Cox Proportional Hazards Model, to

SUBMITTER: Maimaitiyiming A 

PROVIDER: S-EPMC11336432 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature