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

ABSTRACT:

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 we

SUBMITTER: Wang Y 

PROVIDER: S-EPMC11526509 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature