ArticleBioscience reports2019
An integrative bioinformatics analysis of microarray data for identifying hub genes as diagnostic biomarkers of preeclampsia.
Article in Bioscience reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 44 citations in OpenAlex.
- Common and recurrent dysregulated molecular network of placental hypoxia and associated vasculogenesis and angiogenesis in fetal growth restriction.Frontiers in endocrinology · 2026Article
- AttBiomarker: unveiling preeclampsia biomarkers and molecular pathways through two-stage gene selection techniques and attention-based CNN with gene regulatory network analysis.Briefings in bioinformatics · 2025Article
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- Development of immune-derived molecular markers for preeclampsia based on multiple machine learning algorithms.Scientific reports · 2025Article
- Identification of hub genes, diagnostic model, and immune infiltration in preeclampsia by integrated bioinformatics analysis and machine learning.BMC pregnancy and childbirth · 2024Article
- Analyzing molecular signatures in preeclampsia and fetal growth restriction: Identifying key genes, pathways, and therapeutic targets for preterm birth.Frontiers in molecular biosciences · 2024Article
- Decreased PPP1R3G in pre-eclampsia impairs human trophoblast invasion and migration via Akt/MMP-9 signaling pathway.Experimental biology and medicine (Maywood, N.J.) · 2023Article
- Prediction Models for Intrauterine Growth Restriction Using Artificial Intelligence and Machine Learning: A Systematic Review and Meta-Analysis.Healthcare (Basel, Switzerland) · 2023Review
- Machine learning applied in maternal and fetal health: a narrative review focused on pregnancy diseases and complications.Frontiers in endocrinology · 2023Review
- Bioinformatics methods in biomarkers of preeclampsia and associated potential drug applications.BMC genomics · 2022Article
- Identifying preeclampsia-associated genes using a control theory method.Briefings in functional genomics · 2022Article
- EZH2 enhances proliferation and migration of trophoblast cell lines by blocking GADD45A-mediated p38/MAPK signaling pathway.Bioengineered · 2022Article
- A transcriptomic analysis of neuropathic pain in the anterior cingulate cortex after nerve injury.Bioengineered · 2022Article
- Article
- Identification of key pathways and core genes involved in atherosclerotic plaque progression.Annals of translational medicine · 2021Article
- Identification of KIF4A and its effect on the progression of lung adenocarcinoma based on the bioinformatics analysis.Bioscience reports · 2021Article
- Using Machine Learning to Predict Complications in Pregnancy: A Systematic Review.Frontiers in bioengineering and biotechnology · 2021Review
- Gene Transcript Alterations in the Spinal Cord, Anterior Cingulate Cortex, and Amygdala in Mice Following Peripheral Nerve Injury.Frontiers in cell and developmental biology · 2021Article
- Differentially Expressed Genes in the Brain of Aging Mice With Cognitive Alteration and Depression- and Anxiety-Like Behaviors.Frontiers in cell and developmental biology · 2020Article
- Comprehensive Analysis of Differently Expressed and Methylated Genes in Preeclampsia.Computational and mathematical methods in medicine · 2020Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Preeclampsia (PE) is a disorder of pregnancy that is characterised by hypertension and a significant amount of proteinuria beginning after 20 weeks of pregnancy. It is closely associated with high maternal morbidity, mortality, maternal organ dysfunction or foetal growth restriction. Therefore, it is necessary to identify early and novel diagnostic biomarkers of PE. In the present study, we performed a multi-step integrative bioinformatics analysis of microarray data for identifying hub genes as diagnostic biomarkers of PE. With the help of gene expression profiles of the Gene Expression Omnibus (GEO) dataset GSE60438, a total of 268 dysregulated genes were identified including 131 up- and 137 down-regulated differentially expressed genes (DEGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of DEGs suggested that DEGs were significantly enriched in disease-related biological processes (BPs) such as hormone activity, immune response, steroid hormone biosynthesis, metabolic pathways, and other signalling pathways. Using the STRING database, we established a protein-protein interaction (PPI) network based on the above DEGs. Module analysis and identification of hub genes were performed to screen a total of 17 significant hub genes. The support vector machines (SVMs) model was used to predict the potential application of biomarkers in PE diagnosis with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.958 in the training set and 0.834 in the test set, suggesting that this risk classifier has good discrimination between PE patients and control samples. Our results demonstrated that these 17 differentially expressed hub genes can be used as potential biomarkers for diagnosis of PE.
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