Identification of LOC101927355 as a Novel Biomarker for Preeclampsia

Hierarchical lncRNA regulatory network in early-onset severe preeclampsia

BACKGROUND

Recent studies have shown that several long non-coding RNAs (lncRNAs) in the placenta are associated with preeclampsia (PE). However, the extent to which lncRNAs may contribute to the pathological progression of PE is unclear.

RESULTS

Here, we report a hierarchical regulatory network involved in early-onset severe PE (EOSPE). We have carried out transcriptome sequencing on the placentae from patients and normal subjects to identify the differentially expressed genes (DEGs), including some lncRNAs (DElncRNAs). We then constructed a high-quality hierarchical regulatory network of lncRNAs, transcription factors (TFs), and target DEGs, containing 1851 lncRNA-TF interactions and 6901 TF-promoter interactions. The lncRNA-to-target regulatory interactions were further validated by the triplex structures between the DElncRNAs and the promoters of the target DEGs. The DElncRNAs in the regulatory network were clustered into 3 clusters, one containing DElncRNAs correlated with the blood pressure, including FLNB-AS1 with targeting 27.89% (869/3116) DEGs in EOSPE. We further demonstrated that FLNB-AS1 could bind the transcription factor JUNB to regulate a series members of the HIF-1 signaling pathway in trophoblast cells.

CONCLUSIONS

Our results suggest that the differential expression of lncRNAs may perturb the lncRNA-TF-DEG hierarchical regulatory network, leading to the dysregulation of many genes involved in EOSPE. Our study provides a new strategy and a valuable resource for studying the mechanism underlying gene dysregulation in EOSPE patients.

First Trimester Placental Biomarkers for Pregnancy Outcomes

The placenta plays a key role in several adverse obstetrical outcomes, such as preeclampsia, intrauterine growth restriction and gestational diabetes mellitus. The early identification of at-risk pregnancies could significantly improve the management, therapy and prognosis of these pregnancies, especially if these at-risk pregnancies are identified in the first trimester. The aim of this review was to summarize the possible biomarkers that can be used to diagnose early placental dysfunction and, consequently, at-risk pregnancies. We divided the biomarkers into proteins and non-proteins. Among the protein biomarkers, some are already used in clinical practice, such as the sFLT1/PLGF ratio or PAPP-A; others are not yet validated, such as HTRA1, Gal-3 and CD93. In the literature, many studies analyzed the role of several protein biomarkers, but their results are contrasting. On the other hand, some non-protein biomarkers, such as miR-125b, miR-518b and miR-628-3p, seem to be linked to an increased risk of complicated pregnancy. Thus, a first trimester heterogeneous biomarkers panel containing protein and non-protein biomarkers may be more appropriate to identify and discriminate several complications that can affect pregnancies.

Linking genotype to trophoblast phenotype in preeclampsia and HELLP syndrome associated with STOX1 genetic variants

Preeclampsia is a major hypertensive pregnancy disorder with a 50% heritability. The first identified gene involved in the disease is STOX1, a transcription factor, whose variant Y153H predisposes to the disease. Two rare mutations were also identified in Colombian women affected by the hemolysis, elevated liver enzyme, low platelet syndrome, a complication of preeclampsia (T188N and R364X). Here, we explore the effects of these variants in trophoblast cell models (BeWo) where STOX1 was previously invalidated. We firstly showed that STOX1 knockout alters response to oxidative stress, cell proliferation, and fusion capacity. Then, we showed that mutant versions of STOX1 trigger alterations in gene profiles, growth, fusion, and oxidative stress management. The results also reveal alterations of the STOX interaction with DNA when the mutations affected the DNA-binding domain of STOX1 (Y153H and T188N). We also reveal here that a major contributor of these effects appears to be the E2F3 transcription factor.

A review on the role of long non-coding RNA prostate androgen-regulated transcript 1 (PART1) in the etiology of different disorders

LncRNA prostate androgen-regulated transcript 1 (PART1) is an important lncRNA in the carcinogenesis whose role has been firstly unraveled in prostate cancer. Expression of this lncRNA is activated by androgen in prostate cancer cells. In addition, this lncRNA has a role in the pathogenesis intervertebral disc degeneration, myocardial ischemia-reperfusion injury, osteoarthritis, osteoporosis and Parkinson's disease. Diagnostic role of PART1 has been assessed in some types of cancers. Moreover, dysregulation of PART1 expression is regarded as a prognostic factor in a variety of cancers. The current review provides a concise but comprehensive summary of the role of PART1 in different cancers and non-malignant disorders.

Potential genetic biomarkers predict adverse pregnancy outcome during early and mid-pregnancy in women with systemic lupus erythematosus

BACKGROUND

Effectively predicting the risk of adverse pregnancy outcome (APO) in women with systemic lupus erythematosus (SLE) during early and mid-pregnancy is a challenge. This study was aimed to identify potential markers for early prediction of APO risk in women with SLE.

METHODS

The GSE108497 gene expression dataset containing 120 samples (36 patients, 84 controls) was downloaded from the Gene Expression Omnibus database. Weighted gene co-expression network analysis (WGCNA) was performed, and differentially expressed genes (DEGs) were screened to define candidate APO marker genes. Next, three individual machine learning methods, random forest, support vector machine-recursive feature elimination, and least absolute shrinkage and selection operator, were combined to identify feature genes from the APO candidate set. The predictive performance of feature genes for APO risk was assessed using area under the receiver operating characteristic curve (AUC) and calibration curves. The potential functions of these feature genes were finally analyzed by conventional gene set enrichment analysis and CIBERSORT algorithm analysis.

RESULTS

We identified 321 significantly up-regulated genes and 307 down-regulated genes between patients and controls, along with 181 potential functionally associated genes in the WGCNA analysis. By integrating these results, we revealed 70 APO candidate genes. Three feature genes, SEZ6, NRAD1, and LPAR4, were identified by machine learning methods. Of these, SEZ6 (AUC = 0.753) showed the highest in-sample predictive performance for APO risk in pregnant women with SLE, followed by NRAD1 (AUC = 0.694) and LPAR4 (AUC = 0.654). After performing leave-one-out cross validation, corresponding AUCs for SEZ6, NRAD1, and LPAR4 were 0.731, 0.668, and 0.626, respectively. Moreover, CIBERSORT analysis showed a positive correlation between regulatory T cell levels and SEZ6 expression (P < 0.01), along with a negative correlation between M2 macrophages levels and LPAR4 expression (P < 0.01).

CONCLUSIONS

Our preliminary findings suggested that SEZ6, NRAD1, and LPAR4 might represent the useful genetic biomarkers for predicting APO risk during early and mid-pregnancy in women with SLE, and enhanced our understanding of the origins of pregnancy complications in pregnant women with SLE. However, further validation was required.