ArticlePloS one2018
Heterogeneous muscle gene expression patterns in patients with massive rotator cuff tears.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.
- Proteomic trajectories in human rotator cuff degeneration: a systematic review of immunohistochemical studies.Journal of orthopaedic surgery and research · 2026Pooled it
- Paraspinal muscle gene expression across different aetiologies in individuals undergoing surgery for lumbar spine pathology.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2023Article
- Transcriptional time course after rotator cuff repair in 6 month old female rabbits.Frontiers in physiology · 2023Article
- Spatial transcriptomics tools allow for regional exploration of heterogeneous muscle pathology in the pre-clinical rabbit model of rotator cuff tear.Journal of orthopaedic surgery and research · 2022Article
- Paraspinal Muscle Health is Related to Fibrogenic, Adipogenic, and Myogenic Gene Expression in Patients with Lumbar Spine Pathology.BMC musculoskeletal disorders · 2022Article
- Transcriptional Time Course After Rotator Cuff Tear.Frontiers in physiology · 2021Article
- Observational
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
Abstract
Detrimental changes in the composition and function of rotator cuff (RC) muscles are hallmarks of RC disease progression. Previous studies have demonstrated both atrophic and degenerative muscle loss in advanced RC disease. However, the relationship between gene expression and RC muscle pathology remains poorly defined, in large part due to a lack of studies correlating gene expression to tissue composition. Therefore, the purpose of this study was to determine how tissue composition relates to gene expression in muscle biopsies from patients undergoing reverse shoulder arthroplasty (RSA). Gene expression related to myogenesis, atrophy and cell death, adipogenesis and metabolism, inflammation, and fibrosis was measured in 40 RC muscle biopsies, including 31 biopsies from reverse shoulder arthroplasty (RSA) cases that had available histology data and 9 control biopsies from patients with intact RC tendons. After normalization to reference genes, linear regression was used to identify relationships between gene expression and tissue composition. Hierarchical clustering and principal component analysis (PCA) identified unique clusters, and fold-change analysis was used to determine significant differences in expression between clusters. We found that gene expression profiles were largely dependent on muscle presence, with muscle fraction being the only histological parameter that was significantly correlated to gene expression by linear regression. Similarly, samples with histologically-confirmed muscle distinctly segregated from samples without muscle. However, two sub-groups within the muscle-containing RSA biopsies suggest distinct phases of disease, with one group expressing markers of both atrophy and regeneration, and another group not significantly different from either control biopsies or biopsies lacking muscle. In conclusion, this study provides context for the interpretation of gene expression in heterogeneous and degenerating muscle, and provides further evidence for distinct stages of RC disease in humans.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.