Network-Based Analysis Reveals Novel Biomarkers in Peripheral Blood of Patients With Preeclampsia

Development of immune-derived molecular markers for preeclampsia based on multiple machine learning algorithms

Preeclampsia (PE) is a major pregnancy-specific cardiovascular complication posing latent life-threatening risks to mothers and neonates. The contribution of immune dysregulation to PE is not fully understood, highlighting the need to explore molecular markers and their relationship with immune infiltration to potentially inform therapeutic strategies. We used bioinformatics tools to analyze gene expression data from the Gene Expression Omnibus (GEO) database using the GEOquery package in R. Differential expression analysis was performed using the DESeq2 and limma packages, followed by analysis of variance to identify immune-related differentially expressed genes (DEGs). Several machine learning algorithms, including least absolute shrinkage and selection operator (LASSO), bagged trees, and random forest (RF), were used to select immune-related signaling genes closely associated with the occurrence of PE. Our analysis identified 34 immune source-related DEGs. Using the identified PE- and immune source-related genes, we constructed a diagnostic forecasting model employing several ML algorithms. We identified six types of statistically significant immune cells in patients with PE and discovered a strong relationship between biomarkers and immune cells. Moreover, the immune-derived hub genes for PE exhibited strong binding capabilities with drugs, such as alitretinoin, tretinoin, and acitretin. This study presents a robust prediction model for PE that integrates multiple machine learning-derived immune-related biomarkers. Our results indicate that these biomarkers may outperform previously reported molecular signatures in predicting PE and provide insights into the mechanisms underlying immune dysregulation in PE. Further validation in larger cohorts could lead to their clinical application in PE prediction and treatment.

Identifying preeclampsia-associated key module and hub genes via weighted gene co-expression network analysis

Preeclampsia (PE) is a common hypertensive disease in women with pregnancy. With the development of bioinformatics, WGCNA was used to explore specific biomarkers to provide therapy targets efficiently. All samples were obtained from gene expression omnibus (GEO), then we used a package named "WGCNA" to construct a scale-free co-expression network and modules related to PE. Next, the search tool for the retrieval of interacting genes database (STRING) was adopted to structure the protein-protein interaction (PPI) of genes in the hub module. Furthermore, the MCODE plug-in was applied to discern hub clusters of the PPI network. We also utilized clusterprofiler to execute the functional analysis. Finally, hub genes were selected via Venn Plot and confirmed by quantitative real-time polymerase chain reaction. Through the co-expression networks and modules, we ensured the turquoise module was the most significant one related to PE. Functional analysis implied these genes were mainly enriched in the organic hydroxy compound metabolic process and Phosphatidylinositol signal system. Due to connectivity, the PPI network showed that GAPDH and VEGFA were the most conspicuous. Lastly, the Venn Plot screened eight hub genes (LDHA, ENG, OCRL, PIK3CB, FLT1, HK2, PKM, and LEP). LDHA was confirmed to be downregulated in PE tissues (P<0.001). This study revealed vital module and hub genes associated with preeclampsia and indicated that LDHA might be a therapeutic target in the future.

Navigating the Gene Co-Expression Network and Drug Repurposing Opportunities for Brain Disorders Associated with Neurocognitive Impairment

Neurocognitive impairment refers to a spectrum of disorders characterized by a decline in cognitive functions such as memory, attention, and problem-solving, which are often linked to structural or functional abnormalities in the brain. While its exact etiology remains elusive, genetic factors play a pivotal role in disease onset and progression. This study aimed to identify highly correlated gene clusters (modules) and key hub genes shared across neurocognition-impairing diseases, including Alzheimer's disease (AD), Parkinson's disease with dementia (PDD), HIV-associated neurocognitive disorders (HAND), and glioma. Herein, the microarray datasets AD (GSE5281), HAND (GSE35864), glioma (GSE15824), and PD (GSE7621) were used to perform Weighted Gene Co-expression Network Analysis (WGCNA) to identify highly preserved modules across the studied brain diseases. Through gene set enrichment analysis, the shared modules were found to point towards processes including neuronal transcriptional dysregulation, neuroinflammation, protein aggregation, and mitochondrial dysfunction, hallmarks of many neurocognitive disorders. These modules were used in constructing protein-protein interaction networks to identify hub genes shared across the diseases of interest. These hub genes were found to play pivotal roles in processes including protein homeostasis, cell cycle regulation, energy metabolism, and signaling, all associated with brain and CNS diseases, and were explored for their drug repurposing experiments. Drug repurposing based on gene signatures highlighted drugs including Dorzolamide and Oxybuprocaine, which were found to modulate the expression of the hub genes in play and may have therapeutic implications in neurocognitive disorders. While both drugs have traditionally been used for other medical purposes, our study underscores the potential of a combined WGCNA and drug repurposing strategy for searching for new avenues in the simultaneous treatment of different diseases that have similarities in gene co-expression networks.

Pathophysiological impact of CXC and CX3CL1 chemokines in preeclampsia and gestational diabetes mellitus

Diabetes-related pathophysiological alterations and various female reproductive difficulties were common in pregnant women with gestational diabetes mellitus (GDM), who had 21.1 million live births. Preeclampsia (PE), which increases maternal and fetal morbidity and mortality, affects approximately 3%-5% of pregnancies worldwide. Nevertheless, it is unclear what triggers PE and GDM to develop. Therefore, the development of novel moderator therapy approaches is a crucial advancement. Chemokines regulate physiological defenses and maternal-fetal interaction during healthy and disturbed pregnancies. Chemokines regulate immunity, stem cell trafficking, anti-angiogenesis, and cell attraction. CXC chemokines are usually inflammatory and contribute to numerous reproductive disorders. Fractalkine (CX3CL1) may be membrane-bound or soluble. CX3CL1 aids cell survival during homeostasis and inflammation. Evidence reveals that CXC and CX3CL1 chemokines and their receptors have been the focus of therapeutic discoveries for clinical intervention due to their considerable participation in numerous biological processes. This review aims to give an overview of the functions of CXC and CX3CL1 chemokines and their receptors in the pathophysiology of PE and GDM. Finally, we examined stimulus specificity for CXC and CX3CL1 chemokine expression and synthesis in PE and GDM and preclinical and clinical trials of CXC-based PE and GDM therapies.

Potential biomarkers for late-onset and term preeclampsia: A scoping review

Preeclampsia is a progressive, multisystem pregnancy disorder. According to the time of onset or delivery, preeclampsia has been subclassified into early-onset (<34 weeks) and late-onset (≥34 weeks), or preterm (<37 weeks) and term (≥37 weeks). Preterm preeclampsia can be effectively predicted at 11-13 weeks well before onset, and its incidence can be reduced by preventively using low-dose aspirin. However, late-onset and term preeclampsia are more prevalent than early forms and still lack effective predictive and preventive measures. This scoping review aims to systematically identify the evidence of predictive biomarkers reported in late-onset and term preeclampsia. This study was conducted based on the guidance of the Joanna Briggs Institute (JBI) methodology for scoping reviews. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for scoping reviews (PRISMA-ScR) was used to guide the study. The following databases were searched for related studies: PubMed, Web of Science, Scopus, and ProQuest. Search terms contain "preeclampsia," "late-onset," "term," "biomarker," or "marker," and other synonyms combined as appropriate using the Boolean operators "AND" and "OR." The search was restricted to articles published in English from 2012 to August 2022. Publications were selected if study participants were pregnant women and biomarkers were detected in maternal blood or urine samples before late-onset or term preeclampsia diagnosis. The search retrieved 4,257 records, of which 125 studies were included in the final assessment. The results demonstrate that no single molecular biomarker presents sufficient clinical sensitivity and specificity for screening late-onset and term preeclampsia. Multivariable models combining maternal risk factors with biochemical and/or biophysical markers generate higher detection rates, but they need more effective biomarkers and validation data for clinical utility. This review proposes that further research into novel biomarkers for late-onset and term preeclampsia is warranted and important to find strategies to predict this complication. Other critical factors to help identify candidate markers should be considered, such as a consensus on defining preeclampsia subtypes, optimal testing time, and sample types.

Bioinformatics methods in biomarkers of preeclampsia and associated potential drug applications

Background

Preeclampsia is a pregnancy-related condition that causes high blood pressure and proteinuria after 20 weeks of pregnancy. It is linked to increased maternal mortality, organ malfunction, and foetal development limitation. In this view, there is a need critical to identify biomarkers for the early detection of preeclampsia. The objective of this study is to discover critical genes and explore medications for preeclampsia treatment that may influence these genes.

Methods

Four datasets, including GSE10588, GSE25906, GSE48424 and GSE60438 were retrieved from the Gene Expression Omnibus database. The GSE10588, GSE25906, and GSE48424 datasets were then removed the batch effect using the “sva” R package and merged into a complete dataset. The differentially expressed genes (DEGs) were identified using the “limma” R package. The potential small-molecule agents for the treatment of PE was further screened using the Connective Map (CMAP) drug database based on the DEGs. Further, Weight gene Co-expression network (WGNCA) analysis was performed to identified gene module associated with preeclampsia, hub genes were then identified using the logistic regression analysis. Finally, the immune cell infiltration level of genes was evaluated through the single sample gene set enrichment analysis (ssGSEA).

Results

A total of 681 DEGs (376 down-regulated and 305 up-regulated genes) were identified between normal and preeclampsia samples. Then, Dexamethasone, Prednisone, Rimexolone, Piretanide, Trazodone, Buflomedil, Scoulerin, Irinotecan, and Camptothecin drugs were screened based on these DEGs through the CMAP database. Two modules including yellow and brown modules were the most associated with disease through the WGCNA analysis. KEGG analysis revealed that the chemokine signaling pathway, Th1 and Th2 cell differentiation, B cell receptor signalling pathway and oxytocin signalling pathway were significantly enriched in these modules. Moreover, two key genes, PLEK and LEP were evaluated using the univariate and multivariate logistic regression analysis from the hub modules. These two genes were further validated in the external validation cohort GSE60438 and qRT-PCR experiment. Finally, we evaluated the relationship between immune cell and two genes.

Conclusion

In conclusion, the present study investigated key genes associated with PE pathogenesis that may contribute to identifying potential biomarkers, therapeutic agents and developing personalized treatment for PE.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08937-3.