Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 144
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 144
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 212
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3106
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
Patients with multiple sclerosis (MS) face a heightened risk of developing chronic obstructive pulmonary disease (COPD). Despite this widely reported association, the pathogenic contributors and processes that may favor the development of COPD in MS patients have yet to be identified. Recent studies have suggested peripheral blood leukocytes as a potential link between COPD and autoimmune disorders. Therefore, this study aimed to unveil shared molecular signatures between MS and COPD using blood transcriptomes. To this end, gene expression datasets obtained from MS and COPD blood specimens were retrieved from the Gene Expression Omnibus (GEO) database. By integrating datasets belonging to each disorder, differentially expressed genes (DEGs) were determined for each disease. Then, the protein-protein interaction (PPI) network was constructed for shared DEGs between MS and COPD. Subsequently, the network was analyzed to identify hub genes and key regulatory miRNAs. The integrated data for MS encompassed 51 samples (28 from MS patients and 23 from controls), and the integrated data for COPD included 450 samples (275 from COPD patients and 175 from controls). A total of 246 genes were found to exhibit identical directions of expression in both MS and COPD. By applying a high confidence threshold (0.7), a PPI network with 74 nodes was constructed. TP53, H4C6, SNRPE, and RPS11 were identified as hub genes according to the degree measure. In addition, 8 miRNAs were identified as key regulators, each interacting with 6 mRNAs. Among these miRNAs, miR-218-5p and miR-142-5p have been previously reported to contribute to the pathogenesis of these diseases, and here they were identified as key regulators of the shared PPI network, suggesting a potential epigenetic link between MS and COPD. In conclusion, the results highlighted the potential role of peripheral blood leucocytes as a bridge between MS and COPD. These findings broaden our understanding of pathogenic contributors linking MS and COPD. While this transcriptomics study identified multiple key players, such as TP53, miR-218-5p, and miR-142-5p, the assessment of their therapeutic efficacy demands further experimental studies.
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http://dx.doi.org/10.1007/s00438-024-02215-5 | DOI Listing |
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