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Network-Based Analysis Reveals Novel Biomarkers in Peripheral Blood of Patients With Preeclampsia
WGCNA is a potent systems biology approach that explains the connection of gene expression based on a microarray database, which facilitates the discovery of disease therapy targets or potential biomarkers. Preeclampsia is a kind of pregnancy-induced hypertension caused by complex factors. The disea...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9243560/ https://www.ncbi.nlm.nih.gov/pubmed/35782866 http://dx.doi.org/10.3389/fmolb.2022.757203 |
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author | Lin, Jing Meng, Yu Song, Meng-Fan Gu, Wei |
author_facet | Lin, Jing Meng, Yu Song, Meng-Fan Gu, Wei |
author_sort | Lin, Jing |
collection | PubMed |
description | WGCNA is a potent systems biology approach that explains the connection of gene expression based on a microarray database, which facilitates the discovery of disease therapy targets or potential biomarkers. Preeclampsia is a kind of pregnancy-induced hypertension caused by complex factors. The disease’s pathophysiology, however, remains unknown. The focus of this research is to utilize WGCNA to identify susceptible modules and genes in the peripheral blood of preeclampsia patients. Obtain the whole gene expression data of GSE48424 preeclampsia patients and normal pregnant women from NCBI’s GEO database. WGCNA is used to construct a gene co-expression network by calculating correlation coefficients between modules and phenotypic traits, screening important modules, and filtering central genes. To identify hub genes, we performed functional enrichment analysis, pathway analysis, and protein-protein interaction (PPI) network construction on key genes in critical modules. Then, the genetic data file GSE149437 and clinical peripheral blood samples were used as a validation cohort to determine the diagnostic value of these key genes. Nine gene co-expression modules were constructed through WGCNA analysis. Among them, the blue module is significantly related to preeclampsia and is related to its clinical severity. Thirty genes have been discovered by using the intersection of the genes in the blue module and the DEGs genes as the hub genes. It was found that HDC, MS4A2, and SLC18A2 scored higher in the PPI network and were identified as hub genes. These three genes were also differentially expressed in peripheral blood validation samples. Based on the above three genes, we established the prediction model of peripheral blood markers of preeclampsia and drew the nomogram and calibration curve. The ROC curves were used in the training cohort GSE48424 and the validation cohort GSE149437 to verify the predictive value of the above model. Finally, it was confirmed in the collected clinical peripheral blood samples that MS4A2 was differentially expressed in the peripheral blood of early-onset and late-onset preeclampsia, which is of great significance. This study provides a new biomarker and prediction model for preeclampsia. |
format | Online Article Text |
id | pubmed-9243560 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-92435602022-07-01 Network-Based Analysis Reveals Novel Biomarkers in Peripheral Blood of Patients With Preeclampsia Lin, Jing Meng, Yu Song, Meng-Fan Gu, Wei Front Mol Biosci Molecular Biosciences WGCNA is a potent systems biology approach that explains the connection of gene expression based on a microarray database, which facilitates the discovery of disease therapy targets or potential biomarkers. Preeclampsia is a kind of pregnancy-induced hypertension caused by complex factors. The disease’s pathophysiology, however, remains unknown. The focus of this research is to utilize WGCNA to identify susceptible modules and genes in the peripheral blood of preeclampsia patients. Obtain the whole gene expression data of GSE48424 preeclampsia patients and normal pregnant women from NCBI’s GEO database. WGCNA is used to construct a gene co-expression network by calculating correlation coefficients between modules and phenotypic traits, screening important modules, and filtering central genes. To identify hub genes, we performed functional enrichment analysis, pathway analysis, and protein-protein interaction (PPI) network construction on key genes in critical modules. Then, the genetic data file GSE149437 and clinical peripheral blood samples were used as a validation cohort to determine the diagnostic value of these key genes. Nine gene co-expression modules were constructed through WGCNA analysis. Among them, the blue module is significantly related to preeclampsia and is related to its clinical severity. Thirty genes have been discovered by using the intersection of the genes in the blue module and the DEGs genes as the hub genes. It was found that HDC, MS4A2, and SLC18A2 scored higher in the PPI network and were identified as hub genes. These three genes were also differentially expressed in peripheral blood validation samples. Based on the above three genes, we established the prediction model of peripheral blood markers of preeclampsia and drew the nomogram and calibration curve. The ROC curves were used in the training cohort GSE48424 and the validation cohort GSE149437 to verify the predictive value of the above model. Finally, it was confirmed in the collected clinical peripheral blood samples that MS4A2 was differentially expressed in the peripheral blood of early-onset and late-onset preeclampsia, which is of great significance. This study provides a new biomarker and prediction model for preeclampsia. Frontiers Media S.A. 2022-06-16 /pmc/articles/PMC9243560/ /pubmed/35782866 http://dx.doi.org/10.3389/fmolb.2022.757203 Text en Copyright © 2022 Lin, Meng, Song and Gu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Molecular Biosciences Lin, Jing Meng, Yu Song, Meng-Fan Gu, Wei Network-Based Analysis Reveals Novel Biomarkers in Peripheral Blood of Patients With Preeclampsia |
title | Network-Based Analysis Reveals Novel Biomarkers in Peripheral Blood of Patients With Preeclampsia |
title_full | Network-Based Analysis Reveals Novel Biomarkers in Peripheral Blood of Patients With Preeclampsia |
title_fullStr | Network-Based Analysis Reveals Novel Biomarkers in Peripheral Blood of Patients With Preeclampsia |
title_full_unstemmed | Network-Based Analysis Reveals Novel Biomarkers in Peripheral Blood of Patients With Preeclampsia |
title_short | Network-Based Analysis Reveals Novel Biomarkers in Peripheral Blood of Patients With Preeclampsia |
title_sort | network-based analysis reveals novel biomarkers in peripheral blood of patients with preeclampsia |
topic | Molecular Biosciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9243560/ https://www.ncbi.nlm.nih.gov/pubmed/35782866 http://dx.doi.org/10.3389/fmolb.2022.757203 |
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