{"title":"Exploring gene expression signatures in preeclampsia and identifying hub genes through bioinformatic analysis.","authors":"Hamdan Z Hamdan","doi":"10.1016/j.placenta.2024.12.008","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>Preeclampsia (PE) is a multisystem disease that affects women during the pregnancy. Its pathogenicity remains unclear, and no definitive screening test can predict its occurrence so far. The aim of this study is to identify the critical genes that are involved in the pathogenicity of PE by applying integrated bioinformatic methods and to investigate the genes' diagnostic capability.</p><p><strong>Methods: </strong>Datasets that investigated PE have been downloaded from Gene Expression Omnibus (GEO) datasets. Differential gene expression, weighted gene co-expression analysis (WGCNA), protein-protein interaction (PPI) network construction, and finally, the calculation of area under the curve and Receiver operating characteristic curve (ROC) analysis were done for the potential hub genes. The results generated from the GSE186257 dataset (discovery cohort) were validated in the GSE75010 dataset (validation cohort). Following validation of the hub-genes, a multilayer regulatory network was constructed to include the up-stream regulatory elements (transcription factors and miRNAs) of the validated hub-genes.</p><p><strong>Results: </strong>WGCNA revealed six modules that were significantly correlated with PE. A total of 231 differentially expressed genes (DEGs) were identified. DEGs were intersected with the WGCNA modules' genes, totalling 55 genes. These shared genes were used to construct the PPI network; subsequently, four genes, namely FLT1, HTRA4, LEP and PAPPA2, were identified as hub-genes for PE in the discovery cohort. The expressional of these four hub genes were validated in the validation cohort and found to be highly expressed. ROC analysis in both datasets revealed that all these genes had a significant PE diagnostic ability. The regulatory network showed that FLT1 gene is the most connected and regulated gene among the validated hub-genes.</p><p><strong>Discussion: </strong>This integrated analysis revealed that FLT1, LEP, HTRA4 and PAPPA2 may be strongly involved in the pathogenicity of PE and act as promising biomarkers and potential therapeutic targets for PE.</p>","PeriodicalId":20203,"journal":{"name":"Placenta","volume":"159 ","pages":"93-106"},"PeriodicalIF":3.0000,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Placenta","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.placenta.2024.12.008","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"DEVELOPMENTAL BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Introduction: Preeclampsia (PE) is a multisystem disease that affects women during the pregnancy. Its pathogenicity remains unclear, and no definitive screening test can predict its occurrence so far. The aim of this study is to identify the critical genes that are involved in the pathogenicity of PE by applying integrated bioinformatic methods and to investigate the genes' diagnostic capability.
Methods: Datasets that investigated PE have been downloaded from Gene Expression Omnibus (GEO) datasets. Differential gene expression, weighted gene co-expression analysis (WGCNA), protein-protein interaction (PPI) network construction, and finally, the calculation of area under the curve and Receiver operating characteristic curve (ROC) analysis were done for the potential hub genes. The results generated from the GSE186257 dataset (discovery cohort) were validated in the GSE75010 dataset (validation cohort). Following validation of the hub-genes, a multilayer regulatory network was constructed to include the up-stream regulatory elements (transcription factors and miRNAs) of the validated hub-genes.
Results: WGCNA revealed six modules that were significantly correlated with PE. A total of 231 differentially expressed genes (DEGs) were identified. DEGs were intersected with the WGCNA modules' genes, totalling 55 genes. These shared genes were used to construct the PPI network; subsequently, four genes, namely FLT1, HTRA4, LEP and PAPPA2, were identified as hub-genes for PE in the discovery cohort. The expressional of these four hub genes were validated in the validation cohort and found to be highly expressed. ROC analysis in both datasets revealed that all these genes had a significant PE diagnostic ability. The regulatory network showed that FLT1 gene is the most connected and regulated gene among the validated hub-genes.
Discussion: This integrated analysis revealed that FLT1, LEP, HTRA4 and PAPPA2 may be strongly involved in the pathogenicity of PE and act as promising biomarkers and potential therapeutic targets for PE.
期刊介绍：
Placenta publishes high-quality original articles and invited topical reviews on all aspects of human and animal placentation, and the interactions between the mother, the placenta and fetal development. Topics covered include evolution, development, genetics and epigenetics, stem cells, metabolism, transport, immunology, pathology, pharmacology, cell and molecular biology, and developmental programming. The Editors welcome studies on implantation and the endometrium, comparative placentation, the uterine and umbilical circulations, the relationship between fetal and placental development, clinical aspects of altered placental development or function, the placental membranes, the influence of paternal factors on placental development or function, and the assessment of biomarkers of placental disorders.
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