Advances in Multi-Omics Approaches for Biomarker Identification in Human Rotator Cuff Disease: A Systematic Review.

Authors

Xing Li, Yibin Liu, Rong Zhao, Ziming Zhang, Jichun Li, Kathleen A Derwin, Huajun Wang, Chuangxin Lin, Yang Liu, Jinjin Ma

Published in

Journal of orthopaedic research : official publication of the Orthopaedic Research Society. Volume 44. Issue 9. Pages e70275.

Abstract

Rotator cuff disease is a common musculoskeletal disorder of the shoulder that causes pain and functional disability. Despite its high prevalence and substantial clinical burden, clinically validated biomarkers for diagnosis, disease monitoring, treatment management, and outcome prediction remain lacking. High-throughput omics approaches provide powerful tools for systematically identifying candidate biomarkers by comprehensively characterizing disease-associated molecular alterations. This systematic review evaluates recent advances in multi-omics approaches for biomarker identification in rotator cuff disease, with a particular emphasis on integrating findings across genomics, epigenomics, transcriptomics, and proteomics and metabolomics to facilitate biomarker discovery and clinical translation. A total of 46 studies were included in this review. Together, multi-omics evidence identified recurrent candidate biomarkers, particularly MMP3, MFAP5, IL6, and BMP5, that converged on extracellular matrix remodeling, inflammatory and immune regulation, tissue repair and fibrosis, metabolic reprogramming, mitochondrial dysfunction, and tissue-specific remodeling. Integrated analyses further prioritized recurrent candidate biomarkers supported across multiple omics layers, primarily involved in matrix remodeling, inflammation, immune regulation, angiogenesis, and tissue repair, providing potential targets for diagnosis, prognosis, and therapeutic intervention. Future studies incorporating larger prospective cohorts, standardized analytical pipelines, same-cohort multi-omics integration, and functional validation will be essential for translating these candidate biomarkers into clinically useful tools for early diagnosis, disease monitoring, outcome prediction, and personalized treatment.

PMID:
42760506
Bibliographic data and abstract were imported from PubMed on 19 Sep 2026.

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