A nine-hub-gene signature of metabolic syndrome identified using machine learning algorithms and integrated bioinformatics. Issue 1 (1st January 2021)

• Record Type: Journal Article
• Authors: Liu, Guanzhi; Luo, Sen; Lei, Yutian; Wu, Jianhua; Huang, Zhuo; Wang, Kunzheng; Yang, Pei; Huang, Xin
• Is Part Of: Bioengineered. Volume 12:Issue 1(2021)
• Page Start: 5727
• Page End: 5738
• Publication Date: 2021-01-01
• Subjects: Machine learning -- metabolic syndrome -- bioinformatics -- biomarkers -- gene hub; Biomedical engineering -- Periodicals; Biotechnology -- Periodicals; Microbiology -- Periodicals; 660.6
• DOI: 10.1080/21655979.2021.1968249
• Languages: English
• ISSNs: 2165-5987

Abstract

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.