Identification and experimental validation of key genes in osteoarthritis based on machine learning algorithms and single-cell sequencing analysis.

ABSTRACT:

Purpose

Osteoarthritis (OA) is a prevalent cause of disability in older adults. Identifying diagnostic markers for OA is essential for elucidating its mechanisms and facilitating early diagnosis.

Methods

We analyzed 53 synovial tissue samples (n = 30 for OA, n = 23 for the control group) from two datasets in the Gene Express Omnibus (GEO) database. We identified differentially expressed genes (DEGs) between the groups and applied dimensionality reduction using six machine learning algorithms to pinpoint characteristic genes (key genes). We classified the OA samples into subtypes based on these key genes and explored the differences in biological functions and immune characteristics among subtypes, as well as the roles of the key genes. Additionally, we constructed a protein-

SUBMITTER: Yu E 

PROVIDER: S-EPMC11402953 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature