Identification of immune microenvironment subtypes and clinical risk biomarkers for osteoarthritis based on a machine learning model.

ABSTRACT:

Background

Osteoarthritis (OA) is a degenerative disease with a high incidence worldwide. Most affected patients do not exhibit obvious discomfort symptoms or imaging findings until OA progresses, leading to irreversible destruction of articular cartilage and bone. Therefore, developing new diagnostic biomarkers that can reflect articular cartilage injury is crucial for the early diagnosis of OA. This study aims to explore biomarkers related to the immune microenvironment of OA, providing a new research direction for the early diagnosis and identification of risk factors for OA.

Methods

We screened and downloaded relevant data from the Gene Expression Omnibus (GEO) database, and the immune microenvironment-related genes (Imr-DEGs) were identified using the ImmPort data set b

SUBMITTER: Li B 

PROVIDER: S-EPMC11524973 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature