ArticleBioengineered2021
A nine-hub-gene signature of metabolic syndrome identified using machine learning algorithms and integrated bioinformatics.
Article in Bioengineered, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Bioinformatics analysis identifies key secretory protein-encoding differentially expressed genes in adipose tissue of metabolic syndrome.Adipocyte · 2025Article
- Bioinformatics analysis combined with untargeted metabolomics reveals lipid metabolism-related genes and their biological markers in chronic spontaneous urticaria.Frontiers in genetics · 2025Article
- A comprehensive and visualized analysis of relationship between ferroptosis and tumor using bibliometrics and bioinformatics.American journal of cancer research · 2023Article
- Bioinformatic analysis of related immune cell infiltration and key genes in the progression of osteonecrosis of the femoral head.Frontiers in immunology · 2023Article
- Constructing a prognostic risk model for Alzheimer's disease based on ferroptosis.Frontiers in aging neuroscience · 2023Article
- Validating methods for testing natural molecules on molecular pathways of interest in silico and in vitro.Journal of preventive medicine and hygiene · 2022Article
- The shared biomarkers and pathways of systemic lupus erythematosus and metabolic syndrome analyzed by bioinformatics combining machine learning algorithm and single-cell sequencing analysis.Frontiers in immunology · 2022Article
- "Big Data" Approaches for Prevention of the Metabolic Syndrome.Frontiers in genetics · 2022Review
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.