Identification of Novel Serum Metabolic Biomarkers as Indicators in the Progression of Intravenous Leiomyomatosis: A High Performance Liquid Chromatography-Tandem Mass Spectrometry-Based Study.
HPLC-MS/MS
biomarkers
intravenous leiomyomatosis
metabolomics
progression
Journal
Frontiers in cell and developmental biology
ISSN: 2296-634X
Titre abrégé: Front Cell Dev Biol
Pays: Switzerland
ID NLM: 101630250
Informations de publication
Date de publication:
2021
2021
Historique:
received:
15
04
2021
accepted:
18
06
2021
entrez:
26
7
2021
pubmed:
27
7
2021
medline:
27
7
2021
Statut:
epublish
Résumé
Intravenous leiomyomatosis (IVL) is a rare estrogen-dependent neoplasm. However, identifiable and reliable biomarkers are still not available for clinical application, especially for the diagnosis and prognosis of the disease. In the present study, 30 patients with IVL and 30 healthy controls were recruited. Serum samples were isolated from these participants for further high performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) analysis to study metabolomics alterations and identify differentially expressed metabolites based on orthogonal partial least-squares discriminant analysis (OPLS-DA). Subsequently, lasso regression analysis and a generalized linear regression model were applied to screen out hub metabolites associated with the progression of IVL. First, 16 metabolites in the positive ion mode were determined from the 240 identifiable metabolites at the superclass level, with ten metabolites upregulated in the IVL group and the remaining six metabolites downregulated. Our data further proved that four metabolites [hypoxanthine, acetylcarnitine, glycerophosphocholine, and hydrocortisone (cortisol)] were closely related to the oncogenesis of IVL. Hypoxanthine and glycerophosphocholine might function as protective factors in the development of IVL (OR = 0.19 or 0.02, respectively). Nevertheless, acetylcarnitine and hydrocortisone (cortisol), especially the former, might serve as risk indicators for the disease to promote the development or recurrence of IVL (OR = 18.16 or 2.10, respectively). The predictive accuracy of these hub metabolites was further validated by the multi-class receiver operator characteristic curve analysis (ROC) with the Scikit-learn algorithms. Four hub metabolites were finally determined via comprehensive bioinformatics analysis, and these substances could potentially serve as novel biomarkers in predicting the prognosis or progression of IVL.
Sections du résumé
BACKGROUND
BACKGROUND
Intravenous leiomyomatosis (IVL) is a rare estrogen-dependent neoplasm. However, identifiable and reliable biomarkers are still not available for clinical application, especially for the diagnosis and prognosis of the disease.
METHODS
METHODS
In the present study, 30 patients with IVL and 30 healthy controls were recruited. Serum samples were isolated from these participants for further high performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) analysis to study metabolomics alterations and identify differentially expressed metabolites based on orthogonal partial least-squares discriminant analysis (OPLS-DA). Subsequently, lasso regression analysis and a generalized linear regression model were applied to screen out hub metabolites associated with the progression of IVL.
RESULTS
RESULTS
First, 16 metabolites in the positive ion mode were determined from the 240 identifiable metabolites at the superclass level, with ten metabolites upregulated in the IVL group and the remaining six metabolites downregulated. Our data further proved that four metabolites [hypoxanthine, acetylcarnitine, glycerophosphocholine, and hydrocortisone (cortisol)] were closely related to the oncogenesis of IVL. Hypoxanthine and glycerophosphocholine might function as protective factors in the development of IVL (OR = 0.19 or 0.02, respectively). Nevertheless, acetylcarnitine and hydrocortisone (cortisol), especially the former, might serve as risk indicators for the disease to promote the development or recurrence of IVL (OR = 18.16 or 2.10, respectively). The predictive accuracy of these hub metabolites was further validated by the multi-class receiver operator characteristic curve analysis (ROC) with the Scikit-learn algorithms.
CONCLUSION
CONCLUSIONS
Four hub metabolites were finally determined via comprehensive bioinformatics analysis, and these substances could potentially serve as novel biomarkers in predicting the prognosis or progression of IVL.
Identifiants
pubmed: 34307370
doi: 10.3389/fcell.2021.695540
pmc: PMC8297591
doi:
Types de publication
Journal Article
Langues
eng
Pagination
695540Informations de copyright
Copyright © 2021 Ge, Feng, Zhang, Li and Yu.
Déclaration de conflit d'intérêts
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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