Integrated analysis of multiple microarray studies to identify novel gene signatures in preeclampsia

Comparison of immune-related gene signatures and immune infiltration features in early- and late-onset preeclampsia

BACKGROUND

Preeclampsia, a severe pregnancy syndrome, is widely accepted divided into early- and late-onset preeclampsia (EOPE and LOPE) based on the onset time of preeclampsia, with distinct pathophysiological origins. However, the molecular mechanism especially immune-related mechanisms for EOPE and LOPE is currently obscure. In the present study, we focused on placental immune alterations between EOPE and LOPE and search for immune-related biomarkers that could potentially serve as potential therapeutic targets through bioinformatic analysis.

METHODS

The gene expression profiling data was obtained from the Gene Expression Omnibus database. ESTIMATE algorithm and Gene Set Enrichment Analysis were employed to evaluate the immune status. The intersection of differentially expressed genes in GSE74341 series and immune-related genes set screened differentially expressed immune-related genes. Protein-protein interaction network and random forest were used to identify hub genes with a validation by a quantitative real-time PCR. Kyoto Encyclopedia of Genes and Genomes pathways, Gene Ontology and gene set variation analysis were utilized to conduct biological function and pathway enrichment analyses. Single-sample gene set enrichment analysis and CIBERSORTx tools were employed to calculate the immune cell infiltration score. Correlation analyses were evaluated by Pearson correlation analysis. Hub genes-miRNA network was performed by the NetworkAnalyst online tool.

RESULTS

Immune score and stromal score were all lower in EOPE samples. The immune system-related gene set was significantly downregulated in EOPE compared to LOPE samples. Four hub differentially expressed immune-related genes (IL15, GZMB, IL1B and CXCL12) were identified based on a protein-protein interaction network and random forest. Quantitative real-time polymerase chain reaction validated the lower expression levels of four hub genes in EOPE compared to LOPE samples. Immune cell infiltration analysis found that innate and adaptive immune cells were apparent lacking in EOPE samples compared to LOPE samples. Cytokine-cytokine receptor, para-inflammation, major histocompatibility complex class I and T cell co-stimulation pathways were significantly deficient and highly correlated with hub genes. We constructed a hub genes-miRNA regulatory network, revealing the correlation between hub genes and hsa-miR-374a-5p, hsa-miR-203a-3p, hsa-miR-128-3p, hsa-miR-155-3p, hsa-miR-129-2-3p and hsa-miR-7-5p.

CONCLUSIONS

The innate and adaptive immune systems were severely impaired in placentas of EOPE. Four immune-related genes (IL15, GZMB, IL1B and CXCL12) were closely correlated with immune-related pathogenesis of EOPE. The result of our study may provide a new basis for discriminating between EOPE and LOPE and acknowledging the role of the immune landscape in the eventual interference and tailored treatment of EOPE.

A cross-sectional analysis of syncytiotrophoblast membrane extracellular vesicles-derived transcriptomic biomarkers in early-onset preeclampsia

Background

Preeclampsia (PE) is a pregnancy-specific hypertensive disorder affecting 2%-8% of pregnancies worldwide. Biomarker(s) for the disorder exists, but while these have excellent negative predictive value, their positive predictive value is poor. Extracellular vesicles released by the placenta into the maternal circulation, syncytiotrophoblast membrane extracellular vesicles (STB-EVs), have been identified as being involved in PE with the potential to act as liquid biopsies.

Objective

The objective of this study was to identify the difference in the transcriptome of placenta and STB-EVs between preeclampsia and normal pregnancy (NP) and mechanistic pathways.

Methods/study design

We performed RNA-sequencing on placental tissue, medium/large and small STB-EVs from PE (n = 6) and NP (n = 6), followed by bioinformatic analysis to identify targets that could be used in the future for EV-based diagnostic tests for preeclampsia. Some of the identified biomarkers were validated with real-time polymerase chain reactions.

Results

Our analysis identified a difference in the transcriptomic STB-EV cargo between PE and NP. We then identified and verified the differential expression of FLNB, COL17A1, SLC45A4, LEP, HTRA4, PAPP-A2, EBI3, HSD17B1, FSTL3, INHBA, SIGLEC6, and CGB3. Our analysis also identified interesting mechanistic processes via an in silico prediction of STB-EV-based mechanistic pathways.

Conclusions

In this study, using comprehensive profiling of differentially expressed/carried genes of three linked sample subtypes in PE, we identified potential biomarkers and mechanistic gene pathways that may be important in the pathophysiology of PE and could be further explored in future studies.

Transcriptomics-Based Subphenotyping of the Human Placenta Enabled by Weighted Correlation Network Analysis in Early-Onset Preeclampsia With and Without Fetal Growth Restriction

BACKGROUND

Placental disorders contribute to pregnancy complications, including preeclampsia and fetal growth restriction (FGR), but debate regarding their specific pathobiology persists. Our objective was to apply transcriptomics with weighted gene correlation network analysis to further clarify the placental dysfunction in these conditions.

METHODS

We performed RNA sequencing with weighted gene correlation network analysis using human placental samples (n=30), separated into villous tissue and decidua basalis, and clinically grouped as follows: (1) early-onset preeclampsia (EOPE)+FGR (n=7); (2) normotensive, nonanomalous preterm FGR (n=5); (2) EOPE without FGR (n=8); (4) spontaneous idiopathic preterm birth (n=5) matched for gestational age; and (5) uncomplicated term births (n=5). Our data was compared with RNA sequencing data sets from public databases (GSE114691, GSE148241, and PRJEB30656; n=130 samples).

RESULTS

We identified 14 correlated gene modules in our specimens, of which most were significantly correlated with birthweight and maternal blood pressure. Of the 3 network modules consistently predictive of EOPE±FGR across data sets, we prioritized a coexpression gene group enriched for hypoxia-response and metabolic pathways for further investigation. Cluster analysis based on transcripts from this module and the glycolysis/gluconeogenesis metabolic pathway consistently distinguished a subset of EOPE±FGR samples with an expression signature suggesting modified tissue bioenergetics. We demonstrated that the expression ratios of LDHA/LDHB and PDK1/GOT1 could be used as surrogate indices for the larger panels of genes in identifying this subgroup.

CONCLUSIONS

We provide novel evidence for a molecular subphenotype consistent with a glycolytic metabolic shift that occurs more frequently but not universally in placental specimens of EOPE±FGR.

Placental Transcriptome Profiling in Subtypes of Diabetic Pregnancies Is Strongly Confounded by Fetal Sex

The placenta is a temporary organ with a unique structure and function to ensure healthy fetal development. Placental dysfunction is involved in pre-eclampsia (PE), fetal growth restriction, preterm birth, and gestational diabetes mellitus (GDM). A diabetic state affects maternal and fetal health and may lead to functional alterations of placental metabolism, inflammation, hypoxia, and weight, amplifying the fetal stress. The placental molecular adaptations to the diabetic environment and the adaptive spatio-temporal consequences to elevated glucose or insulin are largely unknown (2). We aimed to identify gene expression signatures related to the diabetic placental pathology of placentas from women with diabetes mellitus. Human placenta samples (n = 77) consisting of healthy controls, women with either gestational diabetes mellitus (GDM), type 1 or type 2 diabetes, and women with GDM, type 1 or type 2 diabetes and superimposed PE were collected. Interestingly, gene expression differences quantified by total RNA sequencing were mainly driven by fetal sex rather than clinical diagnosis. Association of the principal components with a full set of clinical patient data identified fetal sex as the single main explanatory variable. Accordingly, placentas complicated by type 1 and type 2 diabetes showed only few differentially expressed genes, while possible effects of GDM and diabetic pregnancy complicated by PE were not identifiable in this cohort. We conclude that fetal sex has a prominent effect on the placental transcriptome, dominating and confounding gene expression signatures resulting from diabetes mellitus in settings of well-controlled diabetic disease. Our results support the notion of placenta as a sexual dimorphic organ.

Identification of Key Genes and Pathways Associated with Preeclampsia by a WGCNA and an Evolutionary Approach

Preeclampsia (PE) is the serious obstetric-related disease characterized by newly onset hypertension and causes damage to the kidneys, brain, liver, and more. To investigate genes with key roles in PE’s pathogenesis and their contributions, we used a microarray dataset of normotensive and PE patients and conducted a weighted gene co-expression network analysis (WGCNA). Cyan and magenta modules that are highly enriched with differentially expressed genes (DEGs) were revealed. By using the molecular complex detection (MCODE) algorithm, we identified five significant clusters in the cyan module protein–protein interaction (PPI) network and nine significant clusters in the magenta module PPI network. Our analyses indicated that (i) human accelerated region (HAR) genes are enriched in the magenta-associated C6 cluster, and (ii) positive selection (PS) genes are enriched in the cyan-associated C3 and C5 clusters. We propose these enriched HAR and PS genes, i.e., EIF4E, EIF5, EIF3M, DDX17, SRSF11, PSPC1, SUMO1, CAPZA1, PSMD14, and MNAT1, including highly connected hub genes, HNRNPA1, RBMX, PRKDC, and RANBP2, as candidate key genes for PE’s pathogenesis. A further clarification of the functions of these PPI clusters and key enriched genes will contribute to the discovery of diagnostic biomarkers for PE and therapeutic intervention targets.

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 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.

Identification and Verification of Potential Hub Genes in Amphetamine-Type Stimulant (ATS) and Opioid Dependence by Bioinformatic Analysis

Objective: Amphetamine-type stimulant (ATS) and opioid dependencies are chronic inflammatory diseases with similar symptoms and common genomics. However, their coexpressive genes have not been thoroughly investigated. We aimed to identify and verify the coexpressive hub genes and pathway involved in the pathogenesis of ATS and opioid dependencies.

Methods: The microarray of ATS- and opioid-treatment mouse models was obtained from the Gene Expression Omnibus database. GEO2R and Venn diagram were performed to identify differentially expressed genes (DEGs) and coexpressive DEGs (CDEGs). Functional annotation and protein–protein interaction network detected the potential functions. The hub genes were screened using the CytoHubba and MCODE plugin with different algorithms, and further validated by receiver operating characteristic analysis in the GSE15774 database. We also validated the hub genes mRNA levels in BV2 cells using qPCR.

Result: Forty-four CDEGs were identified between ATS and opioid databases, which were prominently enriched in the PI3K/Akt signaling pathway. The top 10 hub genes were mainly enriched in apoptotic process (CD44, Dusp1, Sgk1, and Hspa1b), neuron differentiation, migration, and proliferation (Nr4a2 and Ddit4), response to external stimulation (Fos and Cdkn1a), and transcriptional regulation (Nr4a2 and Npas4). Receiver operating characteristic (ROC) analysis found that six hub genes (Fos, Dusp1, Sgk1, Ddit4, Cdkn1a, and Nr4a2) have an area under the curve (AUC) of more than 0.70 in GSE15774. The mRNA levels of Fos, Dusp1, Sgk1, Ddit4, Cdkn1a, PI3K, and Akt in BV2 cells and GSE15774 with METH and heroin treatments were higher than those of controls. However, the Nr4a2 mRNA levels increased in BV2 cells and decreased in the bioinformatic analysis.

Conclusions: The identification of hub genes was associated with ATS and opioid dependencies, which were involved in apoptosis, neuron differentiation, migration, and proliferation. The PI3K/Akt signaling pathway might play a critical role in the pathogenesis of substance dependence.

G-Protein Coupled Receptor Dysregulation May Play Roles in Severe Preeclampsia-A Weighted Gene Correlation Network Analysis of Placental Gene Expression Profile

Preeclampsia is one of the major hypertensive diseases of pregnancy. Genetic factors contribute to abnormal placentation. The inadequate transformation of cytotrophoblasts causes failure of maternal spiral arteries’ remodeling and results in narrow, atherotic-prone vessels, leading to relative placental ischemia. This study aims to explore the possibility of identifying dysregulated gene networks that may offer a potential target in the possible prevention of preeclampsia. We performed a weighted gene correlated network analysis (WGCNA) on a subset of gene expression profiles of placental tissues from severe preeclamptic pregnancies. We identified a gene module (number of genes = 402, GS = 0.35, p = 0.02) enriched for several G-protein-coupled receptor (GPCR)-related genes with significant protein–protein molecular interaction (number of genes = 38, FDR = 0.0007) that may play key roles in preeclampsia. Some genes are noted to play key roles in preeclampsia, including LPAR4/5, CRLR, NPY, TACR1/2, and SFRP4/5, whose functions generally relate to angiogenesis and vasodilation or vasoconstriction. Other upregulated genes, including olfactory and orexigenic genes, serve limited functions in the disease pathogenesis. Altogether, this study shows the utility of WGCNA in exploring possible new gene targets, and additionally reinforces the feasibility of targeting GPCRs that may offer intervention against development and disease progression among severe preeclampsia patients.

ceRNA Network and Functional Enrichment Analysis of Preeclampsia by Weighted Gene Coexpression Network Analysis

BACKGROUND

Preeclampsia (PE) is a multisystemic syndrome which has short- and long-term risk to mothers and children and has pluralistic etiology.

OBJECTIVE

This study is aimed at constructing a competitive endogenous RNA (ceRNA) network for pathways most related to PE using a data mining strategy based on weighted gene coexpression network analysis (WGCNA).

METHODS

We focused on pathways involving hypoxia, angiogenesis, and epithelial mesenchymal transition according to the gene set variation analysis (GSVA) scores. The gene sets of these three pathways were enriched by gene set enrichment analysis (GSEA). WGCNA was used to study the underlying molecular mechanisms of the three pathways in the pathogenesis of PE by analyzing the relationship among pathways and genes. The soft threshold power (β) and topological overlap matrix allowed us to obtain 15 modules, among which the red module was chosen for the downstream analysis. We chose 10 hub genes that satisfied ∣log2Fold Change | >2 and had a higher degree of connectivity within the module. These candidate genes were subsequently confirmed to have higher gene significance and module membership in the red module. Coexpression networks were established for the hub genes to unfold the connection between the genes in the red module and PE. Finally, ceRNA networks were constructed to further clarify the underlying molecular mechanism involved in the occurrence of PE. 56 circRNAs, 17 lncRNAs, and 20 miRNAs participated in the regulation of the hub genes. Coagulation factor II thrombin receptor (F2R) and lumican (LUM) were considered the most relevant genes, and ceRNA networks of them were constructed.

CONCLUSION

The microarray data mining process based on bioinformatics methods constructed lncRNA and miRNA networks for ten hub genes that were closely related to PE and focused on ceRNAs of F2R and LUM finally. The results of our study may provide insight into the mechanisms underlying PE occurrence.

Differences in epidemiology of patients with preeclampsia between China and the US (Review)

Preeclampsia (PE) is a complex complication that occurs during pregnancy. Studies indicated that morbidity from PE exhibits marked variations among geographical areas. Disparities in the incidence of PE between China and the US may be due to differences in ethnicity and genetic susceptibility, maternal age, sexual culture, body mass index, diet, exercise, multiple pregnancies and educational background. These epidemiological differences may give rise to differences between the two countries in terms of diagnostic and therapeutic criteria for PE. PE may be largely attributed to susceptibility genes and lifestyles, such as diet, body mass index and cultural norms regarding sexual relationships. The epidemiologic differences of patients with PE between the two countries indicated that appropriate prevention plans for PE require to be developed according to local conditions.

Identification of Hub Genes Associated with the Pathogenesis of Intracranial Aneurysm via Integrated Bioinformatics Analysis

BACKGROUND

At present, the pathogenesis of intracranial aneurysms (IA) remains unclear, which significantly hinders the development of novel strategies for the clinical treatment. In this study, bioinformatics methods were used to identify the potential hub genes and pathways associated with the pathogenesis of IA.

METHODS

The gene expression datasets of patients with intracranial aneurysm were downloaded from the Gene Expression Database (GEO), and the different data sets were integrated by the robust rank aggregation (RRA) method to identify the differentially expressed genes between patients with intracranial aneurysm and the controls. The functional enrichment analyses of the significant differentially expressed genes (DEGs) were performed and the protein-protein interaction (PPI) network was constructed; thereafter, the hub genes were screened by cytoHubba plug-in of Cytoscape, and finally sequencing dataset GSE122897 was used to verify the hub genes.

RESULTS

The GSE15629, GSE75436, GSE26969, and GSE6551 expression profiles have been included in this study, including 34 intracranial aneurysm samples and 26 control samples. The four datasets obtained 136 significant DEGs (45 up-regulated, 91 down-regulated). Enrichment analysis showed that the extracellular matrix structural constituent and the ECM-receptor interaction were closely related to the occurrence of IA. It was finally determined that eight hub genes associated with the development of IA, including VCAN, COL1A1, COL11A1, COL5A1, COL5A2, POSTN, THBS2, and CDH2.

CONCLUSION

The discovery of potential hub genes and pathways could enhance the understanding of the molecular mechanisms associated with the development of IA. These hub genes may be potential therapeutic targets for the management and new biomarker for the diagnosis of IA.