Machine learning analysis of gene expression profile reveals a novel diagnostic signature for osteoporosis.

ABSTRACT:

Background

Osteoporosis (OP) is increasingly prevalent with the aging of the world population. It is urgent to identify efficient diagnostic signatures for the clinical application.

Method

We downloaded the mRNA profile of 90 peripheral blood samples with or without OP from GEO database (Number: GSE152073). Weighted gene co-expression network analysis (WGCNA) was used to reveal the correlation among genes in all samples. GO term and KEGG pathway enrichment analysis was performed via the clusterProfiler R package. STRING database was applied to screen the interaction pairs among proteins. Protein-protein interaction (PPI) network was visualized based on Cytoscape, and the key genes were screened using the cytoHubba plug-in. The diagnostic model based on these key genes was co

SUBMITTER: Chen X 

PROVIDER: S-EPMC7958453 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature