Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 1034
Function: getPubMedXML
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3152
Function: GetPubMedArticleOutput_2016
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
Background And Aim: Anti-tumor necrosis factor-α (anti-TNF-α) agents have been used for inflammatory bowel disease; however, it has up to 30% nonresponse rate. Identifying molecular pathways and finding reliable diagnostic biomarkers for patient response to anti-TNF-α treatment are needed.
Methods: Publicly available transcriptomic data from inflammatory bowel disease patients receiving anti-TNF-α therapy were systemically collected and integrated. In silico flow cytometry approaches and Metascape were applied to evaluate immune cell populations and to perform gene enrichment analysis, respectively. Genes identified within enrichment pathways validated in neutrophils were tracked in an anti-TNF-α-treated animal model (with lipopolysaccharide-induced inflammation). The receiver operating characteristic curve was applied to all genes to identify the best prediction biomarkers.
Results: A total of 449 samples were retrieved from control, baseline, and after primary anti-TNF-α therapy or placebo. No statistically significant differences were observed between anti-TNF-α treatment responders and nonresponders at baseline in immune microenvironment scores. Neutrophil, endothelial cell, and B-cell populations were higher in baseline nonresponders, and chemotaxis pathways may contribute to the treatment resistance. Genes related to chemotaxis pathways were significantly upregulated in lipopolysaccharide-induced neutrophils, but no statistically significant changes were observed in neutrophils treated with anti-TNF-α. Interleukin 13 receptor subunit alpha 2 (IL13RA2) is the best predictor (receiver operating characteristic curve: 80.7%, 95% confidence interval: 73.8-87.5%), with a sensitivity of 68.13% and specificity of 84.93%, and significantly higher in nonresponders compared with responders (P < 0.0001).
Conclusions: Hyperactive neutrophil chemotaxis influences responses to anti-TNF-α treatment, and IL13RA2 is a potential biomarker to predict anti-TNF-α treatment response.
Download full-text PDF |
Source |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9303672 | PMC |
http://dx.doi.org/10.1111/jgh.15764 | DOI Listing |
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