Potential Protein Biomarkers for Preeclampsia

Machine learning and bioinformatics framework integration reveal potential characteristic genes related to immune cell infiltration in preeclampsia

Introduction: Preeclampsia is a disease that affects both the mother and child, with serious consequences. Screening the characteristic genes of preeclampsia and studying the placental immune microenvironment are expected to explore specific methods for the treatment of preeclampsia and gain an in-depth understanding of the pathological mechanism of preeclampsia. Methods: We screened for differential genes in preeclampsia by using limma package. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, disease ontology enrichment, and gene set enrichment analyses were performed. Analysis and identification of preeclampsia biomarkers were performed by using the least absolute shrinkage and selection operator regression model, support vector machine recursive feature elimination, and random forest algorithm. The CIBERSORT algorithm was used to analyze immune cell infiltration. The characteristic genes were verified by RT-qPCR. Results: We identified 73 differential genes, which mainly involved in reproductive structure and system development, hormone transport, etc. KEGG analysis revealed emphasis on cytokine-cytokine receptor interactions and interleukin-17 signaling pathways. Differentially expressed genes were dominantly concentrated in endocrine system diseases and reproductive system diseases. Our findings suggest that LEP, SASH1, RAB6C, and FLT1 can be used as placental markers for preeclampsia and they are associated with various immune cells. Conclusion: The differentially expressed genes in preeclampsia are related to inflammatory response and other pathways. Characteristic genes, LEP, SASH1, RAB6C, and FLT1 can be used as diagnostic and therapeutic targets for preeclampsia, and they are associated with immune cell infiltration. Our findings contribute to the pathophysiological mechanism exploration of preeclampsia. In the future, the sample size needs to be expanded for data analysis and validation, and the immune cells need to be further validated.

Single-cell RNA-seq reveals developmental deficiencies in both the placentation and the decidualization in women with late-onset preeclampsia

Preeclampsia (PE) is a leading cause of maternal and fetal morbidity and mortality. Although increasing lines of evidence suggest that both the placenta and the decidua likely play roles in the pathogenesis of PE, the molecular mechanism of PE remains elusive partly because of the heterogeneity nature of the maternal-fetal interface. In this study, we perform single-cell RNA-seq on the placenta and the decidual from patients with late-onset PE (LOPE) and women in normal pregnancy. Analyses of single-cell transcriptomes reveal that in LOPE, there are likely a global development deficiency of trophoblasts with impaired invasion of extravillous trophoblasts (EVT) and increased maternal immune rejection and inflammation in the placenta, while there are likely insufficient decidualization of decidual stromal cells (DSC), increased inflammation, and suppressed regulatory functions of decidual immune cells. These findings improve our understanding of the molecular mechanisms of PE.

COVID-19 and Preeclampsia: Overlapping Features in Pregnancy

Coronavirus disease 2019 (COVID-19) is a global respiratory disease with unique features that have placed all medical professionals in an alarming situation. Preeclampsia is a hypertensive disorder of pregnancy affecting 8%-10% of India's pregnant population. Assuming that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) enters host cells through the angiotensin-converting enzyme 2 (ACE2) receptor, the resulting symptoms are due to vasoconstriction, caused by disturbances in the renin-angiotensin system (RAS). Other features of preeclampsia include endothelial dysfunction due to placental ischemia, leading to imbalances in angiogenic and antiangiogenic factors which result in increased blood pressure, proteinuria, altered hepatic enzymes, renal failure, and thrombocytopenia, amongst others. The increased prevalence of preeclampsia that was seen among mothers with SARS-CoV-2 infection might be due to misdiagnosis, as COVID-19 and preeclampsia have coincidental medical features. The major similarities of SARS-CoV-2-infected and preeclamptic women are a rise in pro-inflammatory cytokines, and increased serum ferritin and thrombocytopenia. Therefore, differential diagnosis might be difficult in pregnant women with COVID-19 who present with hypertension and proteinuria, thrombocytopenia, or elevated liver enzymes. The most promising markers for earlier diagnosis of preeclampsia is soluble endoglin (sEng), pregnancy-associated plasma protein-A (PAPP-A), soluble fms-like tyrosine kinase 1 (sFlt-1), and placental growth factor (PlGF). Due to placental hypoxia, sFlt-1 will be overproduced, thus inhibiting PlGF, and this alteration will be observed in the circulation five weeks or more before the onset of symptoms. The sFlt-1/PlGF ratio may also be modified via infectious states, but unregulated levels of those mediators are related to placental insufficiency. Hence, pregnant women with COVID-19 may develop a preeclampsia-like syndrome that might be differentiated properly by angiogenic markers to avoid unnecessary interventions and induced preterm labor.

Integrated Learning: Screening Optimal Biomarkers for Identifying Preeclampsia in Placental mRNA Samples

Preeclampsia (PE) is a maternal disease that causes maternal and child death. Treatment and preventive measures are not sound enough. The problem of PE screening has attracted much attention. The purpose of this study is to screen placental mRNA to obtain the best PE biomarkers for identifying patients with PE. We use Limma in the R language to screen out the 48 differentially expressed genes with the largest differences and used correlation-based feature selection algorithms to reduce the dimensionality and avoid attribute redundancy arising from too many mRNA samples participating in the classification. After reducing the mRNA attributes, the mRNA samples are sorted from large to small according to information gain. In this study, a classifier model is designed to identify whether samples had PE through mRNA in the placenta. To improve the accuracy of classification and avoid overfitting, three classifiers, including C4.5, AdaBoost, and multilayer perceptron, are used. We use the majority voting strategy integrated with the differentially expressed genes and the genes filtered by the best subset method as comparison methods to train the classifier. The results show that the classification accuracy rate has increased from 79% to 82.2%, and the number of mRNA features has decreased from 48 to 13. This study provides clues for the main PE biomarkers of mRNA in the placenta and provides ideas for the treatment and screening of PE.