Identification of metabolism related biomarkers in obesity based on adipose bioinformatics and machine learning

• Published in: Journal of translational medicine Vol. 22; no. 1; pp. 986 - 12
• Main Authors: Wang, Yanping, Wang, Honglin, Yu, Xingrui, Wu, Qinan, Lv, Xinlu, Zhou, Xuelian, Chen, Yong, Geng, Shan
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 31.10.2024; BioMed Central; BMC
• Subjects: Adipose Tissue - metabolism; Analysis; Animals; Anopheles; Bioinformatics analysis; Biological markers; Biomarkers; Biomarkers - metabolism; Care and treatment; Complications and side effects; Computational biology; Computational Biology - methods; Databases, Genetic; Differentially expressed genes (DEGs; Gene expression; Gene Expression Profiling; Gene Expression Regulation; Gene Regulatory Networks; Genes; Genetic aspects; Humans; Machine Learning; Male; Metabolism; Methods; Mice; Mice, Inbred C57BL; Obesity; Obesity - genetics; Obesity - metabolism; Physiological aspects; Type 2 diabetes; China; Taiwan; Bioinformatics analysis; Biomarkers; Metabolism; Differentially expressed genes (DEGs; Machine learning
• ISSN: 1479-5876; 1479-5876
• DOI: 10.1186/s12967-024-05615-8

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.