Identification of potential cell death-related biomarkers for diagnosis and treatment of osteoporosis.

ABSTRACT:

Background

This study aimed to identify potential biomarkers for the diagnosis and treatment of osteoporosis (OP).

Methods

Data sets were downloaded from the Gene Expression Omnibus database, and differentially programmed cell death-related genes were screened. Functional analyses were performed to predict the biological processes associated with these genes. Least absolute shrinkage and selection operator (LASSO), support vector machine (SVM), and random forest (RF) machine learning algorithms were used to screen for characteristic genes, and receiver operating characteristics were used to evaluate the diagnosis of disease characteristic gene values. Gene set enrichment analysis (GSEA) and single-sample GSEA were conducted to analyze the correlation between characteristic g

SUBMITTER: Li M 

PROVIDER: S-EPMC10964579 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature