期刊
JOURNAL OF CLINICAL ENDOCRINOLOGY & METABOLISM
卷 107, 期 5, 页码 1383-1391出版社
ENDOCRINE SOC
DOI: 10.1210/clinem/dgab885
关键词
TIO; metabolomics; diagnostic biomarkers; PUFAs; arachidonic acid pathway
资金
- Beijing Municipal Natural Science Foundation [7214246]
- National Key R&D Program of China [2018YFA0800801]
- National Natural Science Foundation of China [81970757]
Using liquid chromatography-tandem mass spectrometry-based metabolomics, this study characterized the metabolome alterations associated with tumor-induced osteomalacia (TIO). The analysis revealed distinct metabolic profiles between TIO patients and healthy controls, with significant enrichment in the arachidonic acid metabolism pathway. The study also identified a panel of 5 oxylipins that showed high sensitivity and specificity in predicting TIO, providing potential diagnostic biomarkers for TIO.
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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