Prediction of Differentially Expressed Genes and a Diagnostic Signature of Preeclampsia via Integrated Bioinformatics Analysis

Huang, Shan; Cai, Shuangming; Li, Huibin; Zhang, Wenni; Xiao, Huanshun; Yu, Danfeng; Zhong, Xuan; Tao, Pei; Luo, Yiping

doi:10.1155/2022/5782637

Cited by 6 publications

(6 citation statements)

References 36 publications

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“…Our data supported the hypothesis that serine/threonine kinase may affect the EOPE patient and their offspring. Both previous studies and our results demonstrated that gonadotropin regulation and secretion (29,30), Cytokine-cytokine receptor interaction (31), the activation of the Ras signaling pathway and MAPK signaling pathway (32), TGF-b signaling pathway (33), HIF-1 signaling pathway (34, 35), and 2-Oxocarboxylic acid metabolism (36,37) are critical pathways for PE development.…”

Section: Discussionsupporting

confidence: 71%

Identification of key genes in the pathogenesis of preeclampsia via bioinformatic analysis and experimental verification

Gao

Liu

et al. 2023

Front. Endocrinol.

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BackgroundPreeclampsia (PE) is the primary cause of perinatal maternal-fetal mortality and morbidity. The exact molecular mechanisms of PE pathogenesis are largely unknown. This study aims to identify the hub genes in PE and explore their potential molecular regulatory network.MethodsWe downloaded the GSE148241, GSE190971, GSE74341, and GSE114691 datasets for the placenta and performed a differential expression analysis to identify hub genes. We performed Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Disease Ontology (DO), Gene Set Enrichment Analysis (GSEA), and Protein–Protein Interaction (PPI) Analysis to determine functional roles and regulatory networks of differentially expressed genes (DEGs). We then verified the DEGs at transcriptional and translational levels by analyzing the GSE44711 and GSE177049 datasets and our clinical samples, respectively.ResultsWe identified 60 DEGs in the discovery phase, consisting of 7 downregulated genes and 53 upregulated genes. We then identified seven hub genes using Cytoscape software. In the verification phase, 4 and 3 of the seven genes exhibited the same variation patterns at the transcriptional level in the GSE44711 and GSE177049 datasets, respectively. Validation of our clinical samples showed that CADM3 has the best discriminative performance for predicting PEConclusionThese findings may enhance the understanding of PE and provide new insight into identifying potential therapeutic targets for PE.

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Section: Discussionsupporting

confidence: 71%

Identification of key genes in the pathogenesis of preeclampsia via bioinformatic analysis and experimental verification

Gao

Liu

et al. 2023

Front. Endocrinol.

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“…PAPPA-2 is highly expressed in the placenta (Wang et al, 2009;Sifakis et al, 2018) and has been reported to be upregulated in cases of severe PE (Macintire et al, 2014;Kramer et al, 2016), although it was not validated as differentially expressed between EOPE and nPTB samples in our study. In addition, two genes that two of the predictive CpGs mapped to and that were significantly differentially expressed in our independent cohort, SYDE1 and FLNB, have also been reported as potentially involved in FGR and PE in some studies (Tejera et al, 2013;Lo et al, 2017;Leavey et al, 2018;Huang et al, 2022). In addition, the model demonstrated a high specificity in both validation and exploratory groups, where 171 of 195 normotensive samples were predicted correctly, adding confidence to the utility of eoPred.…”

Section: Discussionsupporting

confidence: 61%

eoPred: Predicting the placental phenotype of early-onset preeclampsia using DNA methylation

Fernández-Boyano,

Inkster,

Yuan

et al. 2023

Preprint

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Background: A growing body of literature has reported molecular and histological changes in the human placenta in association with preeclampsia (PE). Placental DNA methylation (DNAme) and transcriptomic patterns have revealed molecular subgroups of PE that are associated with placental histopathology and clinical phenotypes of the disease. However, the heterogeneity of PE both across and within subtypes, whether defined clinically or molecularly, complicates the study of this disease. PE is most strongly associated with placental pathology and adverse fetal and maternal outcomes when it develops early in pregnancy. We focused on placentae from pregnancies affected by preeclampsia that were delivered before 34 weeks of gestation to develop eoPred, a predictor of the DNAme signature associated with the placental phenotype of early-onset preeclampsia (EOPE). Results: Public data from 83 placental samples (HM450K), consisting of 42 EOPE and 41 normotensive preterm birth (nPTB) cases, was used to develop eoPred - a supervised model that relies on a highly discriminative 45 CpG DNAme signature of EOPE in the placenta. The performance of eoPred was assessed using cross-validation (AUC=0.95) and tested in an independent validation cohort (n=49, AUC=0.725). A subset of fetal growth restriction (FGR) and late-PE cases showed a similar DNAme profile at the 45 predictive CpGs, consistent with the overlap in placental pathology between these conditions. The relationship between the EOPE probability generated by eoPred and various phenotypic variables was also assessed, revealing that it is associated with gestational age, and it is not driven by cell composition differences. Conclusions: eoPred relies on a 45 CpG DNAme signature to predict EOPE, and it can be used in a discrete or continuous manner. Using this classifier should 1) improve the consistency of future placental DNAme studies of PE and placental insufficiency, 2) facilitate identifying cases of EOPE in public data sets and 3) importantly, standardize the placental diagnosis to allow better cross-cohort comparisons. Lastly, classification of cases with eoPred should be useful for testing associations between placental pathology and genetic or environmental variables.

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“…PAPPA-2 is highly expressed in the placenta (Wang et al, 2009;Sifakis et al, 2018) and has been reported to be upregulated in cases of severe PE (Macintire et al, 2014;Kramer et al, 2016). In addition, two predictive CpGs mapped to each of SYDE1 and FLNB, which have also been reported as potentially involved in FGR and PE (Tejera et al, 2013;Lo et al, 2017;Leavey et al, 2018;Huang et al, 2022). In addition, the model demonstrated a high specificity in both validation and exploratory groups, where 171 of 195 normotensive samples were predicted correctly, adding confidence to the utility of eoPred.…”

Section: Discussionmentioning

confidence: 99%

eoPred: predicting the placental phenotype of early-onset preeclampsia using public DNA methylation data

Fernández-Boyano,

Inkster,

Yuan

et al. 2023

Front. Genet.

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Background: A growing body of literature has reported molecular and histological changes in the human placenta in association with preeclampsia (PE). Placental DNA methylation (DNAme) and transcriptomic patterns have revealed molecular subgroups of PE that are associated with placental histopathology and clinical phenotypes of the disease. However, the clinical and molecular heterogeneity of PE both across and within subtypes complicates the study of this disease. PE is most strongly associated with placental pathology and adverse fetal and maternal outcomes when it develops early in pregnancy. We focused on placentae from pregnancies affected by preeclampsia that were delivered before 34 weeks of gestation to develop eoPred, a predictor of the DNAme signature associated with the placental phenotype of early-onset preeclampsia (EOPE).Results: Public data from 83 placental samples (HM450K), consisting of 42 EOPE and 41 normotensive preterm birth (nPTB) cases, was used to develop eoPred—a supervised model that relies on a highly discriminative 45 CpG DNAme signature of EOPE in the placenta. The performance of eoPred was assessed using cross-validation (AUC = 0.95) and tested in an independent validation cohort (n = 49, AUC = 0.725). A subset of fetal growth restriction (FGR) and late-PE cases showed a similar DNAme profile at the 45 predictive CpGs, consistent with the overlap in placental pathology between these conditions. The relationship between the EOPE probability generated by eoPred and various phenotypic variables was also assessed, revealing that it is associated with gestational age, and it is not driven by cell composition differences.Conclusion: eoPred relies on a 45-CpG DNAme signature to predict a homogeneous placental phenotype of EOPE in a discrete or continuous manner. Using this classifier should 1) aid in the study of placental insufficiency and improve the consistency of future placental DNAme studies of PE, 2) facilitate identifying the placental phenotype of EOPE in public data sets and 3) importantly, standardize the placental diagnosis of EOPE to allow better cross-cohort comparisons. Lastly, classification of cases with eoPred will be useful for investigating the relationship between placental pathology and genetic or environmental variables.

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Prediction of Differentially Expressed Genes and a Diagnostic Signature of Preeclampsia via Integrated Bioinformatics Analysis

Cited by 6 publications

References 36 publications

Identification of key genes in the pathogenesis of preeclampsia via bioinformatic analysis and experimental verification

Identification of key genes in the pathogenesis of preeclampsia via bioinformatic analysis and experimental verification

eoPred: Predicting the placental phenotype of early-onset preeclampsia using DNA methylation

eoPred: predicting the placental phenotype of early-onset preeclampsia using public DNA methylation data

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