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: 3122
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
Context: Excessive production of fibroblast growth factor 23 (FGF23) by a tumor is considered the main pathogenesis in tumor-induced osteomalacia (TIO). Despite its importance to comprehensive understanding of pathogenesis and diagnosis, the regulation of systemic metabolism in TIO remains unclear.
Objective: We aimed to systematically characterize the metabolome alteration associated with TIO.
Methods: By means of liquid chromatography-tandem mass spectrometry-based metabolomics, we analyzed the metabolic profile from 96 serum samples (32 from TIO patients at initial diagnosis, pairwise samples after tumor resection, and 32 matched healthy control (HC) subjects). In order to screen and evaluate potential biomarkers, statistical analyses, pathway enrichment and receiver operating characteristic (ROC) were performed.
Results: Metabolomic profiling revealed distinct alterations between TIO and HC cohorts. Differential metabolites were screened and conducted to functional clustering and annotation. A significantly enriched pathway was found involving arachidonic acid metabolism. A combination of 5 oxylipins, 4-HDoHE, leukotriene B4, 5-HETE, 17-HETE, and 9,10,13-TriHOME, demonstrated a high sensitivity and specificity panel for TIO prediction screened by random forest algorithm (AUC = 0.951; 95% CI, 0.827-1). Supported vector machine modeling and partial least squares modeling were conducted to validate the predictive capabilities of the diagnostic panel.
Conclusion: Metabolite profiling of TIO showed significant alterations compared with HC. A high-sensitivity and high-specificity panel with 5 oxylipins was tested as diagnostic predictor. For the first time, we provide the global profile of metabolomes and identify potential diagnostic biomarkers of TIO. The present work may offer novel insights into the pathogenesis of TIO.
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Source |
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http://dx.doi.org/10.1210/clinem/dgab885 | DOI Listing |
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