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Research
Xin Huang
Department of Cardiovascular Medicine, First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
Corresponding Author
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Background: Metabolic syndrome is a common and complicated metabolic disorder and defined as a clustering of metabolic risk factors such as insulin resistance or diabetes, obesity, hypertension, and hyperlipidemia. However, its early diagnosis is limited because the lack of definitive clinical diagnostic biomarkers. In present study, we aim to select several candidate gene as a blood-based clinically applicable transcriptomics signature for metabolic syndrome.
Method: 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. Then, based on selected hub gene signature, we performed logistic regression analysis and subsequently established a web nomogram calculator for metabolic syndrome risk to detect the diagnostic value of this hub gene signature. Finally, Receiver Operating Characteristic curve analysis, calibration curve analysis, Hosmer-Lemeshow good of fit test and decision curve analysis showed the classification and calibration performance as well as potential clinical benefit of this hub gene signature.
Results: Through weighted gene co-expression network analysis, protein-protein interaction network analysis, we identified 2 gene modules and 51 hub genes associated with metabolic syndrome. Then, we subsequently performed further feature selection via LASSO regression and random forest method. Finally, a 9-hub-gene signature with high diagnostic value and a web nomogram calculator for metabolic syndrome risk (https://xjtulgz.shinyapps.io/DynNomapp/) were developed. 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.
Conclusions: The blood-based 9-hub-gene signature identified in present study and the web nomogram calculator for metabolic syndrome risk are possible to accurately achieve the noninvasive screening or diagnosis of MetS considering the excellent classification ability, calibration and potential clinical benefits.
Keywords
metabolic syndrome, WGCNA, diagnostic biomarkers, bioinformatics, machine learning
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This is a list of supplementary files associated with this preprint. Click to download.
- TableS1.xlsx
Table S1 List of MetS-associated high-throughput transcriptomics subjects from GEO database.
- TableS2.xlsx
Table S2 List of 51 hub genes identified by WGCNA and PPI network analysis.
- TableS3.xlsx
Table S3 Cell-free differentially expressed genes (DEGs) expression data in plasma sample of metabolic syndrome group compared with control group.
- TableS4.xlsx
Table S4 Selected hub gene expression level in plasma sample of metabolic syndrome group compared with control group.
- FigureS1.tif
Figure S1 Receiver Operating Characteristic curves of every single gene feature in the 9-hub-gene signature.
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A new preprint design is coming!
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This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Research
Xin Huang
Department of Cardiovascular Medicine, First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 23 Feb, 2021
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Translational Medicine
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Metabolic syndrome is a common and complicated metabolic disorder and defined as a clustering of metabolic risk factors such as insulin resistance or diabetes, obesity, hypertension, and hyperlipidemia. However, its early diagnosis is limited because the lack of definitive clinical diagnostic biomarkers. In present study, we aim to select several candidate gene as a blood-based clinically applicable transcriptomics signature for metabolic syndrome.
Method: 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. Then, based on selected hub gene signature, we performed logistic regression analysis and subsequently established a web nomogram calculator for metabolic syndrome risk to detect the diagnostic value of this hub gene signature. Finally, Receiver Operating Characteristic curve analysis, calibration curve analysis, Hosmer-Lemeshow good of fit test and decision curve analysis showed the classification and calibration performance as well as potential clinical benefit of this hub gene signature.
Results: Through weighted gene co-expression network analysis, protein-protein interaction network analysis, we identified 2 gene modules and 51 hub genes associated with metabolic syndrome. Then, we subsequently performed further feature selection via LASSO regression and random forest method. Finally, a 9-hub-gene signature with high diagnostic value and a web nomogram calculator for metabolic syndrome risk (https://xjtulgz.shinyapps.io/DynNomapp/) were developed. 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.
Conclusions: The blood-based 9-hub-gene signature identified in present study and the web nomogram calculator for metabolic syndrome risk are possible to accurately achieve the noninvasive screening or diagnosis of MetS considering the excellent classification ability, calibration and potential clinical benefits.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
This is a list of supplementary files associated with this preprint. Click to download.
- TableS1.xlsx
Table S1 List of MetS-associated high-throughput transcriptomics subjects from GEO database.
- TableS2.xlsx
Table S2 List of 51 hub genes identified by WGCNA and PPI network analysis.
- TableS3.xlsx
Table S3 Cell-free differentially expressed genes (DEGs) expression data in plasma sample of metabolic syndrome group compared with control group.
- TableS4.xlsx
Table S4 Selected hub gene expression level in plasma sample of metabolic syndrome group compared with control group.
- FigureS1.tif
Figure S1 Receiver Operating Characteristic curves of every single gene feature in the 9-hub-gene signature.
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