The Second School of Clinical Medicine, Southern Medical University, No.1023, South Shatai Road, Baiyun District, Guangzhou, Guangdong 510515, China.
Department of Endocrinology, Zhujiang Hospital, Southern Medical University, 253 Industrial Avenue, Guangzhou, Guangdong Province 510282, China.
Gene. 2023 Dec 20;888:147755. doi: 10.1016/j.gene.2023.147755. Epub 2023 Aug 31.
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.
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.
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.
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.
肥胖是一种复杂的病症，受多种因素影响，包括环境、行为和遗传等。然而，肥胖的遗传机制仍未得到充分阐明。因此，我们的研究旨在通过生物信息学分析鉴定人类肥胖的关键基因。
从基因表达综合数据库（GEO）中下载人类脂肪组织的微阵列数据集。在选择差异表达基因（DEGs）后，我们使用 Lasso 回归和支持向量机（SVM）算法进一步鉴定特征基因。此外，还进行了免疫细胞浸润分析、基因集变异分析（GSVA）、GeneCards 数据库和转录调控分析，以研究特征基因影响肥胖的潜在机制。我们利用受试者工作特征（ROC）曲线分析特征基因的诊断效能。最后，我们在细胞实验和动物实验中验证了特征基因。验证实验中的统计分析使用 SPSS 版本 28.0 进行，图形使用 GraphPad Prism 9.0 软件生成。生物信息学分析使用 R 语言（版本 4.2.2）进行，使用 p<0.05 作为显著性阈值。
使用 Limma 包选择了 199 个 DEGs，然后通过 Lasso 回归和 SVM 算法选择了 5 个特征基因（EGR2、NPY1R、GREM1、BMP3 和 COL8A1）。通过各种生物信息学分析，我们发现了特征基因影响肥胖的一些信号通路，还揭示了这些基因在免疫微环境中的关键作用，以及它们与肥胖相关基因的强相关性。此外，ROC 曲线表明，所有特征基因在肥胖症中都具有良好的预测和诊断效率。最后，通过体外实验验证后，确定 EGR2、NPY1R 和 GREM1 为关键基因。
本研究鉴定了 EGR2、GREM1 和 NPY1R 作为人类肥胖的潜在关键基因和潜在诊断生物标志物。此外，首次发现 EGR2 是人类脂肪组织肥胖的关键基因，这可能为肥胖的诊断和治疗提供新的靶点。
