Revealing sphingolipid metabolism genes as biomarkers for the diagnosis of invasive pituitary adenomas in silico and in vivo.

ABSTRACT:

Objective

The aim of this study is to reveal key genes related to sphingolipid metabolism for distinguishing the invasive characteristics of pituitary adenomas (PA).

Methods

Bulk-RNA sequencing datasets related to the invasive pituitary adenomas (IPA) were retrieved from the GEO database and integrated after batch effect correction. IPA-related genes were identified through weighted gene co-expression network analysis (WGCNA) and differential gene expression (DEGs) analysis. Sphingolipid metabolism-related genes were sourced from the GeneCards database and intersected with IPA-related genes to identify IPA-specific metabolism genes (SMGs). Machine learning, including Lasso and SVM-RFE, was used to identify the key genes incorporated into a diagnostic model. The tumor immune

SUBMITTER: Xu ZJ 

PROVIDER: S-EPMC12715928 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature