Evolution and advancements in genomics and epigenomics in OA research: How far we have come.
data sharing
epigenomics
genomics
methylomics
miRNomics
osteoarthritis
Journal
Osteoarthritis and cartilage
ISSN: 1522-9653
Titre abrégé: Osteoarthritis Cartilage
Pays: England
ID NLM: 9305697
Informations de publication
Date de publication:
28 Feb 2024
28 Feb 2024
Historique:
received:
17
11
2023
revised:
29
01
2024
accepted:
25
02
2024
medline:
2
3
2024
pubmed:
2
3
2024
entrez:
1
3
2024
Statut:
aheadofprint
Résumé
Osteoarthritis (OA) is the most prevalent musculoskeletal disease affecting articulating joint tissues, resulting in local and systemic changes that contribute to increased pain and reduced function. Diverse technological advancements have culminated in the advent of high throughput "omic" technologies, enabling identification of comprehensive changes in molecular mediators associated with the disease. Amongst these technologies, genomics and epigenomics - including methylomics and miRNomics, have emerged as important tools to aid our biological understanding of disease. In this narrative review, we selected articles discussing advancements and application of these technologies to OA biology and pathology. We discuss how genomics, DNA methylomics, and miRNomics have uncovered disease-related molecular markers in the local and systemic tissues or fluids of OA patients. Genomics investigations into the genetic links of OA, including using GWAS, have evolved to identify 100+ genetic susceptibility markers of OA. Epigenomic investigations of gene methylation status have identified the importance of methylation to OA-related catabolic gene expression. Furthermore, miRNomic studies have identified key microRNA signatures in various tissues and fluids related to OA disease. Sharing of standardized, well-annotated omic datasets in curated repositories will be key to enhancing statistical power to detect smaller and targetable changes in the biological signatures underlying OA pathogenesis. Additionally, continued technological developments and analysis methods, including using computational molecular and regulatory networks, are likely to facilitate improved detection of disease-relevant targets, in-turn, supporting precision medicine approaches and new treatment strategies for OA.
Identifiants
pubmed: 38428513
pii: S1063-4584(24)00054-2
doi: 10.1016/j.joca.2024.02.656
pii:
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
Copyright © 2024. Published by Elsevier Ltd.