Identification of metabolic biomarkers in idiopathic pulmonary arterial hypertension using targeted metabolomics and bioinformatics analysis

Pulmonary arterial hypertension (PAH) is a life-threatening disease with a poor prognosis, and metabolic abnormalities play a critical role in its development. This study used metabolomics, machine learning algorithms and bioinformatics to screen for potential metabolic biomarkers associated with th...

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Vydáno v:Scientific reports Ročník 14; číslo 1; s. 25283 - 15
Hlavní autoři: Yang, Chuang, Liu, Yi-Hang, Zheng, Hai-Kuo
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Nature Publishing Group UK 25.10.2024
Nature Publishing Group
Nature Portfolio
Témata:
631/114
692/308
692/4019
692/53
692/699
Adult
Algorithms
Biochemical pathways
Bioinformatics
Biomarkers
Biomarkers - blood
Biosynthesis
Case-Control Studies
Computational Biology - methods
Diagnosis
Familial Primary Pulmonary Hypertension - blood
Familial Primary Pulmonary Hypertension - genetics
Familial Primary Pulmonary Hypertension - metabolism
Female
High-performance liquid chromatography
Humanities and Social Sciences
Humans
Hypertension
Idiopathic pulmonary arterial hypertension
Learning algorithms
Liquid chromatography
Machine Learning
Male
Mass spectrometry
Mass spectroscopy
Metabolism
Metabolites
Metabolome
Metabolomics
Metabolomics - methods
Middle Aged
multidisciplinary
Phenotypes
Pulmonary hypertension
Science
Science (multidisciplinary)
Spermine
Tryptophan
Biomarkers
Idiopathic pulmonary arterial hypertension
Metabolomics
Biochemical pathways
Machine learning
ISSN:2045-2322, 2045-2322
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Shrnutí:Pulmonary arterial hypertension (PAH) is a life-threatening disease with a poor prognosis, and metabolic abnormalities play a critical role in its development. This study used metabolomics, machine learning algorithms and bioinformatics to screen for potential metabolic biomarkers associated with the diagnosis of PAH. In this study, plasma samples were collected from 17 patients diagnosed with idiopathic pulmonary arterial hypertension (IPAH) and 20 healthy controls. Plasma metabolomic profiling was performed by high-performance liquid chromatography-mass spectrometry. Gene profiles of PAH patients were obtained from the GEO database. Key differentially expressed metabolites (DEMs) and metabolism-related genes were subsequently identified using machine learning algorithms. Twenty differential plasma metabolites associated with IPAH were identified (VIP score > 1 and p  < 0 0.05), and enrichment analysis revealed the arginine biosynthesis pathway as the most altered pathway. Using machine learning models, including least absolute shrinkage and selection operator (LASSO), random forest (RF) and support vector machine (SVM), we extracted key metabolites that correlated with clinical phenotypes. Our results suggested that five metabolites, kynurenine, homoserine, tryptophan, AMP, and spermine, are potential biomarkers for IPAH. Bioinformatics analysis also identified 3 metabolism-related genes, MAPK6, SLC7A11 and CDC42BPA, that are strongly correlated with pulmonary hypertension, demonstrating strong predictive power and clinical relevance. Our findings revealed some key genes associated with metabolism in PH, and provided crucial information about complex metabolic reprogramming signals and may lead to the identification of useful metabolic biomarkers for the diagnosis of PAH.
Bibliografie:ObjectType-Article-1
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-76514-7