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.

“Big Data” Approaches for Prevention of the Metabolic Syndrome

“…An earlier study proved the feasibility of applying machine learning algorithms to simultaneously process metabolomics, lipidomics, and clinical data with a random forest algorithm ( Acharjee et al, 2016 ). A later study further supported the strength of machine learning processing omics data in the realm of MetS, collecting one of the most extensive transcriptomics data to discover nine hub-gene features (SPTAN1, KCTD7, PSMD1, FZD1, KLHL9, PTTG1, TSPAN14, P2RY2, and CXCR5) with excellent classification ability ( Liu et al, 2021 ). With improved coverage of omics data over different populations, machine learning could potentially standardize molecular screening markers for MetS while deepening the understanding of its biological mechanism.…”

Constructing a prognostic risk model for Alzheimer’s disease based on ferroptosis

“…In the current studies on AD model building, few studies have validated the screened genes through in vitro experiments, mostly through another dataset or GC patients ( Chen W. et al, 2021 ; Liu et al, 2021 ; Chen et al, 2022 ). Chen W. et al (2021) successfully constructed an AD prediction model using the ADNI database and combining clinical and imaging histological features, however, it was not validated by in vitro experiments.…”

The shared biomarkers and pathways of systemic lupus erythematosus and metabolic syndrome analyzed by bioinformatics combining machine learning algorithm and single-cell sequencing analysis

“…Metabolic syndrome (MetS), also known as insulin resistance (IR) syndrome, is one of the metabolic disorders with a high risk of negative cardiovascular outcomes, including obesity, hypertension, dyslipidemia, and impaired glucose tolerance (IGT) (7,8). Recent studies indicated that genes like CTRP7 and SPTAN1 are associated with the occurrence of MetS (9,10). In addition, various serological indicators were proved to be the potential biomarkers for the diagnosis of MetS (11)(12)(13).…”