IDENTIFICATION OF NOVEL BIOMARKERS FOR EARLY DETECTION OF METABOLIC DISORDERS

Abstract

Metabolic disease such as obesity and type 2 diabetes mellitus is a major health concern in the world and thus there is the necessity to have dependable biomarkers in order to detect this disease early. This paper sought to determine biomarkers in terms of gene expression that relates to metabolic disorders using publicly available transcriptomic data. GSE15653 microarray data was located and analyzed in the Gene Expression Omnibus (GEO) database and the limma package in R identified 184 significant differentially expressed genes (DEGs), including 102 up-regulated and 82 down-regulated genes (|human|>The microarray data (GSE15653) was deposited in the Gene Expression Omnibus DEGs were further analyzed through functional enrichment analysis and were mainly found to be related to the pathways of inflammatory response, lipid metabolism, and insulin signaling. The construction of a protein protein interaction (PPI) network was carried out and the degree centrality of key hub genes were identified, namely IL6, TNF, AKT1, PPARG, and SLC2A4. The receiver operating characteristic (ROC) curve analysis was shown to have excellent diagnostic ability with area under the curve (AUC) levels between 0.91 and 0.84. These results indicate that the identified genes can be potentially used as biomarkers to detect metabolic disorders at an early stage and gain enhancements in their molecular pathogenesis. They should be further experimentally validated to ensure their clinical usefulness.