A nine-hub-gene signature of metabolic syndrome identified using machine learning algorithms and integrated bioinformatics

Abstract

Early risk assessments and interventions for metabolic syndrome (MetS) are limited because of a lack of effective biomarkers. In the present study, several candidate genes were selected as a blood-based transcriptomic signature for MetS. We collected so far the largest MetS-associated peripheral blood high-throughput transcriptomics data and put forward a novel feature selection strategy by combining weighted gene co-expression network analysis, protein-protein interaction network analysis, LASSO regression and random forest approaches. Two gene modules and 51 hub genes as well as a 9-hub-gene signature associated with metabolic syndrome were identified. Then, based on this 9-hub-gene signature, we performed logistic analysis and subsequently established a web nomogram calculator for metabolic syndrome risk (https://xjtulgz.shinyapps.io/DynNomapp/). This 9-hub-gene signature showed excellent classification and calibration performance (AUC = 0.968 in training set, AUC = 0.883 in internal validation set, AUC = 0.861 in external validation set) as well as ideal potential clinical benefit.