Identification of hub genes and candidate herbal treatment in obesity through integrated bioinformatic analysis and reverse network pharmacology

Abstract

Abstract Obesity is a global epidemic elevating the risk of various metabolic disorders. As there is a lack of effective drugs to treat obesity, we combined bioinformatics and reverse network pharmacology in this study to identify effective herbs to treat obesity. We identified 1011 differentially expressed genes (DEGs) of adipose tissue after weight loss by analyzing five expression profiles (GSE103766, GSE35411, GSE112307, GSE43471, and GSE35710) from the Gene Expression Omnibus (GEO) database. We identified 27 hub genes from the protein–protein interaction (PPI) network by performing MCODE using the Search Tool for the Retrieval of Interacting Genes (STRING) database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses revealed that these hub genes have roles in the extracellular matrix–receptor interaction, cholesterol metabolism, PI3K-Akt signaling pathway, etc. Ten herbs (Aloe, Portulacae Herba, Mori Follum, Silybum Marianum, Phyllanthi Fructus, Pollen Typhae, Ginkgo Semen, Leonuri Herba, Eriobotryae Folium, and Litseae Fructus) targeting the nine hub genes (COL1A1, MMP2, MMP9, SPP1, DNMT3B, MMP7, CETP, COL1A2, and MUC1) using six ingredients were identified as the key herbs. Quercetin and (-)-epigallocatechin-3-gallate were determined to be the key ingredients. Lastly, Ingredients-Targets, Herbs-Ingredients-Targets, and Herbs-Taste-Meridian Tropism networks were constructed using Cytoscape to elucidate this complex relationship. This study could help identify promising therapeutic targets and drugs to treat obesity.