Identification of metabolism related biomarkers in obesity based on adipose bioinformatics and machine learning.
Bioinformatics analysis
Biomarkers
Differentially expressed genes (DEGs
Machine learning
Metabolism
Journal
Journal of translational medicine
ISSN: 1479-5876
Titre abrégé: J Transl Med
Pays: England
ID NLM: 101190741
Informations de publication
Date de publication:
31 Oct 2024
31 Oct 2024
Historique:
received:
10
05
2024
accepted:
18
08
2024
medline:
1
11
2024
pubmed:
1
11
2024
entrez:
1
11
2024
Statut:
epublish
Résumé
Obesity has emerged as a growing global public health concern over recent decades. Obesity prevalence exhibits substantial global variation, ranging from less than 5% in regions like China, Japan, and Africa to rates exceeding 75% in urban areas of Samoa. To examine the involvement of metabolism-related genes. Gene expression datasets GSE110729 and GSE205668 were accessed from the GEO database. DEGs between obese and lean groups were identified through DESeq2. Metabolism-related genes and pathways were detected using enrichment analysis, WGCNA, Random Forest, and XGBoost. The identified signature genes were validated by real-time quantitative PCR (qRT-PCR) in mouse models. A total of 389 genes exhibiting differential expression were discovered, showing significant enrichment in metabolic pathways, particularly in the propanoate metabolism pathway. The orangered4 module, which exhibited the highest correlation with propanoate metabolism, was identified using Weighted Correlation Network Analysis (WGCNA). By integrating the DEGs, WGCNA results, and machine learning methods, the identification of two metabolism-related genes, Storkhead Box 1 (STOX1), NACHT and WD repeat domain-containing protein 2(NWD2) was achieved. These signature genes successfully distinguished between obese and lean individuals. qRT-PCR analysis confirmed the downregulation of STOX1 and NWD2 in mouse models of obesity. This study has analyzed the available GEO dataset in order to identify novel factors associated with obesity metabolism and found that STOX1 and NWD2 may serve as diagnostic biomarkers.
Sections du résumé
BACKGROUND
BACKGROUND
Obesity has emerged as a growing global public health concern over recent decades. Obesity prevalence exhibits substantial global variation, ranging from less than 5% in regions like China, Japan, and Africa to rates exceeding 75% in urban areas of Samoa.
AIM
OBJECTIVE
To examine the involvement of metabolism-related genes.
METHODS
METHODS
Gene expression datasets GSE110729 and GSE205668 were accessed from the GEO database. DEGs between obese and lean groups were identified through DESeq2. Metabolism-related genes and pathways were detected using enrichment analysis, WGCNA, Random Forest, and XGBoost. The identified signature genes were validated by real-time quantitative PCR (qRT-PCR) in mouse models.
RESULTS
RESULTS
A total of 389 genes exhibiting differential expression were discovered, showing significant enrichment in metabolic pathways, particularly in the propanoate metabolism pathway. The orangered4 module, which exhibited the highest correlation with propanoate metabolism, was identified using Weighted Correlation Network Analysis (WGCNA). By integrating the DEGs, WGCNA results, and machine learning methods, the identification of two metabolism-related genes, Storkhead Box 1 (STOX1), NACHT and WD repeat domain-containing protein 2(NWD2) was achieved. These signature genes successfully distinguished between obese and lean individuals. qRT-PCR analysis confirmed the downregulation of STOX1 and NWD2 in mouse models of obesity.
CONCLUSION
CONCLUSIONS
This study has analyzed the available GEO dataset in order to identify novel factors associated with obesity metabolism and found that STOX1 and NWD2 may serve as diagnostic biomarkers.
Identifiants
pubmed: 39482740
doi: 10.1186/s12967-024-05615-8
pii: 10.1186/s12967-024-05615-8
doi:
Substances chimiques
Biomarkers
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
986Subventions
Organisme : 2023 Municipal Education Commission Science
ID : (KJQN202300460
Organisme : Postdoctoral Science Foundation of China
ID : 2020M683263
Organisme : Natural Science Foundation Project of Chongqing, Chongqing Science and Technology Commission
ID : cstc2021jcyj-msxmX0353
Informations de copyright
© 2024. The Author(s).
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