Characterizing programmed cell death features in osteoarthritis through integrative multiomics and machine learning analysis.

ABSTRACT:

Background

Programmed cell death (PCD) is an essential biological process in maintaining tissue homeostasis and eliminating damaged or unnecessary cells. Signaling molecules profoundly affect cellular metabolism and are crucial in various diseases; however, their role in osteoarthritis (OA) remains unclear. This study aimed to systematically evaluate the predictive value, genetic alterations, and therapeutic implications of PCD-associated genes in OA.

Methods

We performed multiomics analyses, integrating transcriptomic and single-cell transcriptome data. The biological importance of PCD genes was investigated using differential expression analysis, functional enrichment analysis, pathway analysis, weighted gene co-expression network analysis, and many machine learning models

SUBMITTER: Sun Q 

PROVIDER: S-EPMC12517281 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature