Dataset Information

Identification of novel biomarkers for preeclampsia on the basis of differential expression network analysis.

ABSTRACT: Preeclampsia (PE) is a severe pregnancy complication, which is a leading cause of maternal and fetal mortality. The present study aimed to screen potential biomarkers for the diagnosis and prediction of PE and to investigate the underlying mechanisms of PE development based on the differential expression network (DEN). The microarray datasets E-GEOD-6573 and E-GEOD-48424 were downloaded from the European Bioinformatics Institute database. Differentially expressed genes (DEGs) between the PE and normal groups were screened by Significant Analysis of Microarrays with the cutoff value of a |log2 fold change| of >2, and a false discovery rate of <0.05. The DEN was constructed based on the differential and non-differential interactions observed. In addition, genes with higher connectivity degrees in the DEN were identified on the basis of centrality analysis, while disease genes were also extracted from the DEN. In order to understand the functional roles of genes in DEN, Gene Ontology (GO) and pathway enrichment analyses were performed. The present results indicated that a total of 225 genes were considered as DEGs in the PE group, while 466 nodes and 314 gene interactions were involved in the DEN. Among these 466 nodes, 4 nodes with higher degrees were identified, including ubiquitin C (UBC), small ubiquitin-like modifier 1 (SUMO1), SUMO2 and RAD21 homolog (S. pombe) (RAD21). Notably, UBC was also found to be a disease gene. UBC, RAD21, SUMO2 and SUMO1 were markedly enriched in the regulation of programmed cell death, as well as in the regulation of apoptosis, cell cycle and chromosomal part. In conclusion, based on these results, we suggest that UBC, RAD21, SUMO2 and SUMO1 may be reliable biomarkers for the prediction of the development and progression of PE.

SUBMITTER: Wu Y

PROVIDER: S-EPMC4906647 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature

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Identification of novel biomarkers for preeclampsia on the basis of differential expression network analysis.

Wu Yufang Y Fu Xiuhua X Wang Lin L

Experimental and therapeutic medicine 20160415 1

Preeclampsia (PE) is a severe pregnancy complication, which is a leading cause of maternal and fetal mortality. The present study aimed to screen potential biomarkers for the diagnosis and prediction of PE and to investigate the underlying mechanisms of PE development based on the differential expression network (DEN). The microarray datasets E-GEOD-6573 and E-GEOD-48424 were downloaded from the European Bioinformatics Institute database. Differentially expressed genes (DEGs) between the PE and ...[more]

PMID: 27347039

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Project 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.

Project description:BackgroundPreeclampsia (PE), one of hypertension-related disorders of pregnancy, is a common cause of maternal death worldwide. This study aimed to identify a circRNA-miRNA-mRNA-associated ceRNA network and related pathways in PE.Material and methodsWe downloaded 3 microarray datasets from the Gene Expression Omnibus database, obtained 163 differentially expressed circRNAs (dif-circRNAs) (61 upregulated and 102 downregulated), 39 differentially expressed microRNAs (dif-miRNAs) (22 upregulated and 17 downregulated), and 271 differentially expressed mRNAs (dif-mRNAs) (168 upregulated and 103 downregulated) from placenta tissues of PE. Functional enrichment analysis and protein-protein interaction (PPI) network with module analysis of dif-mRNAs were performed. The regulatory relationship between dif-miRNAs and dif-mRNAs/circRNAs was predicted via related databases. A circRNA-miRNA-mRNA regulatory network was constructed.ResultsA total of 53 pairs were obtained, including 13 circRNAs (10 upregulated and 3 downregulated), 9 miRNAs (3 upregulated and 6 downregulated) and 31 mRNAs (22 upregulated and 9 downregulated). GNB5 and IL2RB were obtained. KEGG enrichment analysis showed that both of them were closely related with the PI3K-Akt signalling pathway. Therefore, ceRNAs might affect the PI3K-Akt signalling pathway via the upregulation of GNB5 by binding to miR-1248 in PE. Meanwhile, hsa_circ_0052661 might upregulate IL2RB by binding miR-4303 to play a role in PE in the same way.ConclusionGNB5 and IL2RB might be key genes involved in the PI3K-Akt signalling pathway in PE, and hsa_circ_0087208, hsa_circ_0035443, hsa_circ_0067557 and hsa_circ_0052661 might regulate these key genes in PE by binding miR-1248 or miR-4303.Key messagesThere is still a lack of predictive and diagnostic factors for preeclampsia, which is a common cause of maternal death worldwide.This study identified a novel circRNA-associated ceRNA network and related pathways in preeclampsia.GNB5 and IL2RB might be key genes in their related circRNA-associated ceRNA network, and probably take an important role in preeclampsia via PI3K-Akt signalling pathway, which made them to be potential markers of preeclampsia.

Project description:Large-scale chromosomal deletions or other non-specific perturbations of the transcriptome can alter the expression of hundreds or thousands of genes, and it is of biological interest to understand which genes are most profoundly affected. We present a method for predicting a gene's expression as a function of other genes thereby accounting for the effect of transcriptional regulation that confounds the identification of genes differentially expressed relative to a regulatory network. The challenge in constructing such models is that the number of possible regulator transcripts within a global network is on the order of thousands, and the number of biological samples is typically on the order of 10. Nevertheless, there are large gene expression databases that can be used to construct networks that could be helpful in modeling transcriptional regulation in smaller experiments.We demonstrate a type of penalized regression model that can be estimated from large gene expression databases, and then applied to smaller experiments. The ridge parameter is selected by minimizing the cross-validation error of the predictions in the independent out-sample. This tends to increase the model stability and leads to a much greater degree of parameter shrinkage, but the resulting biased estimation is mitigated by a second round of regression. Nevertheless, the proposed computationally efficient "over-shrinkage" method outperforms previously used LASSO-based techniques. In two independent datasets, we find that the median proportion of explained variability in expression is approximately 25%, and this results in a substantial increase in the signal-to-noise ratio allowing more powerful inferences on differential gene expression leading to biologically intuitive findings. We also show that a large proportion of gene dependencies are conditional on the biological state, which would be impossible with standard differential expression methods.By adjusting for the effects of the global network on individual genes, both the sensitivity and reliability of differential expression measures are greatly improved.

Project description:Biological and medical sciences are increasingly acknowledging the significance of gene co-expression-networks for investigating complex-systems, phenotypes or diseases. Typically, complex phenotypes are investigated under varying conditions. While approaches for comparing nodes and links in two networks exist, almost no methods for the comparison of multiple networks are available and-to best of our knowledge-no comparative method allows for whole transcriptomic network analysis. However, it is the aim of many studies to compare networks of different conditions, for example, tissues, diseases, treatments, time points, or species. Here we present a method for the systematic comparison of an unlimited number of networks, with unlimited number of transcripts: Co-expression Differential Network Analysis (CoDiNA). In particular, CoDiNA detects links and nodes that are common, specific or different among the networks. We developed a statistical framework to normalize between these different categories of common or changed network links and nodes, resulting in a comprehensive network analysis method, more sophisticated than simply comparing the presence or absence of network nodes. Applying CoDiNA to a neurogenesis study we identified candidate genes involved in neuronal differentiation. We experimentally validated one candidate, demonstrating that its overexpression resulted in a significant disturbance in the underlying gene regulatory network of neurogenesis. Using clinical studies, we compared whole transcriptome co-expression networks from individuals with or without HIV and active tuberculosis (TB) and detected signature genes specific to HIV. Furthermore, analyzing multiple cancer transcription factor (TF) networks, we identified common and distinct features for particular cancer types. These CoDiNA applications demonstrate the successful detection of genes associated with specific phenotypes. Moreover, CoDiNA can also be used for comparing other types of undirected networks, for example, metabolic, protein-protein interaction, ecological and psychometric networks. CoDiNA is publicly available as an R package in CRAN (https://CRAN.R-project.org/package=CoDiNA).

Dataset Information

Identification of novel biomarkers for preeclampsia on the basis of differential expression network analysis.

Publications

Identification of novel biomarkers for preeclampsia on the basis of differential expression network analysis.

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