Interpretable machine learning driven biomarker identification and validation for prostate cancer.

ABSTRACT:

Background

Prostate cancer (PCa), a common malignancy among men globally, requires the identification of biomarkers for early diagnosis and predicting progression. This study aimed to identify the key genes involved in the occurrence and development of PCa.

Methods

Leveraging data from the Gene Expression Omnibus (GEO) database, this study integrated multi-chip datasets, conducting differential expression analysis and enrichment analysis to pinpoint PCa-related genes. Subsequently, machine learning models were constructed using least absolute shrinkage and selection operator (LASSO) regression, support vector machine (SVM), and random forest (RF) methods. The optimal model was selected for further study and the contribution of related genes was explained using SHapley Additi

SUBMITTER: Yuan J 

PROVIDER: S-EPMC12271943 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature