Machine learning-based endoplasmic reticulum-related diagnostic biomarker and immune microenvironment landscape for osteoarthritis.

ABSTRACT:

Background

Osteoarthritis (OA) is the most common degenerative joint disease worldwide. Further improving the current limited understanding of osteoarthritis has positive clinical value.

Methods

OA samples were collected from GEO database and endoplasmic reticulum related genes (ERRGs) were identified. The WGCNA network was further built to identify the crucial gene module. Based on the expression profiles of characteristic ERRGs, LASSO algorithm was used to select key factors according to the minimum λ value. Random forest (RF) algorithm was used to calculate the importance of ERRGs. Subsequently, overlapping genes based on LASSO and RF algorithms were identified as ERRGs-related diagnostic biomarkers. In addition, OA specimens were also collected and performed qRT-PCR quan

SUBMITTER: Liu T 

PROVIDER: S-EPMC10968715 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature