ArticleMetabolites2022
The Combination of Bioinformatics Analysis and Untargeted Metabolomics Reveals Potential Biomarkers and Key Metabolic Pathways in Asthma.
Article in Metabolites, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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Who cites it
7 citing papers in PubMed, 15 citations in OpenAlex.
- Multi-omics integration identifies associations between large high-density lipoprotein and lung function, asthma, and COPD: evidence from genetic and population-based analyses.Respiratory research · 2026Article
- Multi-omics insights into dietary zinc-mediated reprogramming of the gut-nasal ecosystem in allergic rhinitis.Frontiers in allergy · 2026Article
- Bioinformatics analysis combined with untargeted metabolomics reveals lipid metabolism-related genes and their biological markers in chronic spontaneous urticaria.Frontiers in genetics · 2025Article
- Differential metabolic responses of mouse Leydig and spermatogonia cells to radiofrequency electromagnetic field exposure.Frontiers in public health · 2025Article
- Employing a synergistic bioinformatics and machine learning framework to elucidate biomarkers associating asthma with pyrimidine metabolism genes.Respiratory research · 2024Article
- The combination of machine learning and untargeted metabolomics identifies the lipid metabolism -related gene CH25H as a potential biomarker in asthma.Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2023Article
- Untargeted Plasma Lipidomics Reveal Perturbed Metabolites of Glycerophospholipids, and Sphingolipids in Moderate-to-Severe Acne.Clinical, cosmetic and investigational dermatology · 2023Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Asthma is a complex chronic airway inflammatory disease that seriously impacts patients' quality of life. As a novel approach to exploring the pathogenesis of diseases, metabolomics provides the potential to identify biomarkers of asthma host susceptibility and elucidate biological pathways. The aim of this study was to screen potential biomarkers and biological pathways so as to provide possible pharmacological therapeutic targets for asthma. In the present study, we merged the differentially expressed genes (DEGs) of asthma in the GEO database with the metabolic genes obtained by Genecard for bioinformatics analysis and successfully screened out the metabolism-related hub genes (HIF1A, OCRL, NNMT, and PER1). Then, untargeted metabolic techniques were utilized to reveal HDM-induced metabolite alterations in 16HBE cells. A total of 45 significant differential metabolites and 5 differential metabolic pathways between the control group and HDM group were identified based on the OPLS-DA model. Finally, three key metabolic pathways, including glycerophospholipid metabolism, galactose metabolism, and alanine, aspartate, and glutamate metabolism, were screened through the integrated analysis of bioinformatics data and untargeted metabolomics data. Taken together, these findings provide valuable insights into the pathophysiology and targeted therapy of asthma and lay a foundation for further research.
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Registered trials
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