Fibrosis-Associated Signaling Molecules Are Differentially Expressed in Palmar Connective Tissues of Patients with Carpal Tunnel Syndrome and Dupuytren's Disease

Carpal tunnel syndrome

Carpal tunnel syndrome (CTS) is the most common nerve entrapment disorder worldwide. The epidemiology and risk factors, including family burden, for developing CTS are multi-factorial. Despite much research, its intricate pathophysiological mechanism(s) are not fully understood. An underlying subclinical neuropathy may indicate an increased susceptibility to developing CTS. Although surgery is often performed for CTS, clear international guidelines to indicate when to perform non-surgical or surgical treatment, based on stage and severity of CTS, remain to be elucidated. Neurophysiological examination, using electrophysiology or ultrasonography, performed in certain circumstances, should correlate with the history and findings in clinical examination of the person with CTS. History and clinical examination are particularly relevant globally owing to lack of other equipment. Various instruments are used to assess CTS and treatment outcomes as well as the effect of the disorder on quality of life. The surgical treatment options of CTS - open or endoscopic - offer an effective solution to mitigate functional impairments and pain. However, there are risks of post-operative persistent or recurrent symptoms, requiring meticulous diagnostic re-evaluation before any additional surgery. Health-care professionals should have increased awareness about CTS and all its implications. Future considerations of CTS include use of linked national registries to understand risk factors, explore possible screening methods, and evaluate diagnosis and treatment with a broader perspective beyond surgery, including psychological well-being.

Musculoskeletal Diseases: From Molecular Basis to Therapy

Musculoskeletal diseases (MSDs) comprise a plethora of different disorders (more than 150 conditions) affecting the locomotor system [...].

Exploring the potential relationship between frozen shoulder and Dupuytren's disease through bioinformatics analysis and machine learning

Background

Frozen shoulder (FS) and Dupuytren's disease (DD) are two closely related diseases, but the mechanism of their interaction is unknown. Our study sought to elucidate the molecular mechanism of these two diseases through shared gene and protein interactions.

Methods

GSE75152 and GSE140731 data were downloaded from the Gene Expression Omnibus (GEO) database, and shared genes between FS and DD were selected by using R packages. Then, we used Cytoscape software and the STRING database to produce a protein-protein interaction (PPI) network. Important interaction networks and hub genes were selected through MCODE and cytoHubba algorithms. To explore the potential mechanisms of the development of the two diseases, the hub genes were further enriched by GO and KEGG analyses. We predicted the transcription factors (TFs) of hub genes with Transcriptional Regulatory Relationships Unraveled by Sentence-based Text mining (TRRUST). Moreover, we identified candidate genes for FS with DD with cytoHubba and machine learning algorithms. Finally, we analyzed the role of immunocyte infiltration in FS and constructed the relationship between candidate genes and immunocytes in FS.

Results

We identified a total of 321 shared genes. The results of GO and KEGG enrichment of shared genes showed that extracellular matrix and collagen fibril tissue play a certain role in the occurrence and development of disease. According to the importance of genes, we constructed the key PPI network of shared genes and the top 15 hub genes for FS with DD. Then, we predicted that five TFs are related to the hub genes and are highly expressed in the FS group. Machine learning results show that the candidate genes POSTN and COL11A1 may be key for FS with DD. Finally, immune cell infiltration revealed the disorder of immunocytes in FS patients, and expression of candidate genes can affect immunocyte infiltration.

Conclusion

We identified a PPI network, 15 hub genes, and two immune-related candidate genes (POSTN and COL11A1) using bioinformatics analysis and machine learning algorithms. These genes have the potential to serve as diagnostic genes for FS in DD patients. Furthermore, our study reveals disorder of immunocytes in FS.