Related Experiment Video
Updated: May 21, 2025
Measuring 3D In-vivo Shoulder Kinematics using Biplanar Videoradiography
Published on: March 12, 2021
Transcriptomics of Anterior Shoulder Instability: Differences in Gene Expression in the Blood of Patients With and
Joseph W Galvin1, Patrick Rooney2, Alec Egan2
1Department of Orthopedics and Rehabilitation, University of Iowa, Iowa City, Iowa, USA.
Background:
Currently, biomarkers do not have a role in diagnosis or outcome prediction models for patients with recurrent anterior shoulder instability.
Purpose/Hypothesis:
The purpose of this study was to compare gene expression differences in the blood and tissue of patients with anterior shoulder instability with and without significant glenoid bone loss (GBL). The hypothesis was that the severity of GBL would be associated with the expression level of genes in the blood and capsular tissue of patients with anterior shoulder instability.
Study Design:
Descriptive laboratory study.
Methods:
Consecutive patients with anterior shoulder instability undergoing arthroscopic and open shoulder stabilization were prospectively enrolled. Blood and anterior capsular tissue specimens obtained during surgery were compared between patients with significant GBL (≥10%) and patients without significant GBL (<10%). RNA was extracted, and a panel of 277 inflammatory genes was utilized to quantify gene expression at the RNA level using a probe-based RNA quantification platform. Differential expression analysis was performed to identify genes expressed at different levels between patients with and without significant GBL. The expression levels of the subset of genes identified were used to generate a ridge regression model to predict the severity of GBL. Quantitative polymerase chain reaction was performed to confirm probe-based RNA findings.
Results:
A total of 17 patients were included, with a mean age of 26 years. Overall, 7 patients had <10% GBL (mean, 2.3%), and 10 patients had ≥10% GBL (mean, 16.4%). There were 9 genes that were identified as significantly differentially expressed in the blood, and 5 of these (IFIT1, IFIT3, IFI44, PRKCB, and OAS2 with P values of 1 × 10-5, 1 × 10-4, 1 × 10-4, 1 × 10-4, and 6 × 10-4, respectively) were confirmed using nonparametric tests. The authors developed a model using the 5 genes to predict the severity of GBL, and this had an accuracy of 88% (95% CI, 64%-99%). By quantitative polymerase chain reaction, IFIT3 was confirmed to be significantly differentially expressed (P = .012).
Conclusion:
There were significant gene expression differences in the blood of patients with (≥10%) and without (<10%) significant GBL. The differential expression of 5 genes allowed for the development of an accurate predictive model and transcriptomic biomarker to predict the severity of GBL.
Clinical Relevance:
The addition of a blood biomarker to current outcome prediction models may provide increased accuracy in identifying those at risk of failure from arthroscopic Bankart repair.
