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Identification and analysis of key genes in adipose tissue for human obesity based on bioinformatics
Yuchen Hua1, Danyingzhu Xie2, Yugang Zhang2
1The Second School of Clinical Medicine, Southern Medical University, No.1023, South Shatai Road, Baiyun District, Guangzhou, Guangdong 510515, China.
Background:
Obesity is a complex condition that is affected by a variety of factors, including the environment, behavior, and genetics. However, the genetic mechanisms underlying obesity remains poorly elucidated. Therefore, our study aimed at identifying key genes for human obesity using bioinformatics analysis.
Methods:
The microarray datasets of adipose tissue in humans were downloaded from the Gene Expression Omnibus (GEO) database. After the selection of differentially expressed genes (DEGs), we used Lasso regression and Support Vector Machine (SVM) algorithm to further identify the feature genes. Moreover, immune cell infiltration analysis, gene set variation analysis (GSVA), GeneCards database and transcriptional regulation analysis were conducted to study the potential mechanisms by which the feature genes may impact obesity. We utilized receiver operating characteristic (ROC) curve to analysis the diagnostic efficacy of feature genes. Finally, we verified the feature genes in cell experiments and animal experiments. The statistical analyses in validation experiments were conducted using SPSS version 28.0, and the graph were generated using GraphPad Prism 9.0 software. The bioinformatics analyses were conducted using R language (version 4.2.2), with a significance threshold of p < 0.05 used.
Results:
199 DEGs were selected using Limma package, and subsequently, 5 feature genes (EGR2, NPY1R, GREM1, BMP3 and COL8A1) were selected through Lasso regression and SVM algorithm. Through various bioinformatics analyses, we found some signaling pathways by which feature genes influence obesity and also revealed the crucial role of these genes in the immune microenvironment, as well as their strong correlations with obesity-related genes. Additionally, ROC curve showed that all the feature genes had good predictive and diagnostic efficiency in obesity. Finally, after validation through in vitro experiments, EGR2, NPY1R and GREM1 were identified as the key genes.
Conclusions:
This study identified EGR2, GREM1 and NPY1R as the potential key genes and potential diagnostic biomarkers for obesity in humans. Moreover, EGR2 was discovered as a key gene for obesity in human adipose tissue for the first time, which may provide novel targets for diagnosing and treating obesity.
