Identification of metabolism related biomarkers in obesity based on adipose bioinformatics and machine learning Article Swipe
Yanping Wang
,
Honglin Wang
,
Xingrui Yu
,
Qinan Wu
,
Xinlu Lv
,
Xuelian Zhou
,
Yong Chen
,
Shan Geng
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1186/s12967-024-05615-8
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1186/s12967-024-05615-8
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 To examine the involvement of metabolism-related genes. 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 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 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.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1186/s12967-024-05615-8
- OA Status
- gold
- Cited By
- 3
- References
- 27
- Related Works
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- OpenAlex ID
- https://openalex.org/W4403947252
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4403947252Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1186/s12967-024-05615-8Digital Object Identifier
- Title
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Identification of metabolism related biomarkers in obesity based on adipose bioinformatics and machine learningWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-10-31Full publication date if available
- Authors
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Yanping Wang, Honglin Wang, Xingrui Yu, Qinan Wu, Xinlu Lv, Xuelian Zhou, Yong Chen, Shan GengList of authors in order
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https://doi.org/10.1186/s12967-024-05615-8Publisher landing page
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1186/s12967-024-05615-8Direct OA link when available
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Identification (biology), Bioinformatics, Adipose tissue, Obesity, Computational biology, Computer science, Medicine, Data science, Biology, Internal medicine, BotanyTop concepts (fields/topics) attached by OpenAlex
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3Total citation count in OpenAlex
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2025: 3Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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