Background: Preeclampsia (PE) is one of the leading causes of maternal and fetal morbidity and mortality worldwide. Placental oxidative stress has been identified as a major pathway to the development of PE. Ferroptosis is a new form of regulated cell death that is associated with iron metabolism and oxidative stress, and likely mediates PE pathogenesis. The aim of the study was to identify the key molecules involved in ferroptosis to further explore the mechanism of ferroptosis in PE.Methods: Gene expression data and clinical information were downloaded from the GEO database. The limma R package was used to screen differentially expressed genes (DEGs) and intersected with ferroptosis genes. The GO and KEGG pathways were then analyzed. Next, hub genes were identified via weighted gene co-expression network analysis (WGCNA). Receiver operating curves (ROCs) were performed for diagnostic and Pearson’s correlation of hub genes and clinicopathological characteristics. Immunohistochemistry and Western blot analysis were used to verify the expression of hub genes.Results: A total of 3,142 DEGs were identified and 30 ferroptosis-related DEGs were obtained. In addition, ferroptosis-related pathways were enriched by GO and KEGG using DEGs. Two critical modules and six hub genes that were highly related to diagnosis of PE were identified through WGCNA. The analysis of the clinicopathological features showed that NQO1 and SRXN1 were closely correlated with PE characteristics and diagnosis. Finally, Western blot and immunohistochemistry analysis confirmed that the expression of the SRXN1 protein in the placental tissue of patients with PE was significantly elevated, while the expression of NQO1 was significantly decreased.Conclusions: SRXN1 and NQO1 may be key ferroptosis-related proteins in the pathogenesis of PE. The study may provide a theoretical and experimental basis for revealing the pathogenesis of PE and improving the diagnosis of PE.

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Objective: The aim of this study was to use machine learning methods to analyze all available clinical and laboratory data obtained during prenatal screening in early pregnancy to develop predictive models in preeclampsia (PE).Material and Methods: Data were collected by retrospective medical records review. This study used 5 machine learning algorithms to predict the PE: deep neural network (DNN), logistic regression (LR), support vector machine (SVM), decision tree (DT), and random forest (RF). Our model incorporated 18 variables including maternal characteristics, medical history, prenatal laboratory results, and ultrasound results. The area under the receiver operating curve (AUROC), calibration and discrimination were evaluated by cross-validation.Results: Compared with other prediction algorithms, the RF model showed the highest accuracy rate. The AUROC of RF model was 0.86 (95% CI 0.80–0.92), the accuracy was 0.74 (95% CI 0.74–0.75), the precision was 0.82 (95% CI 0.79–0.84), the recall rate was 0.42 (95% CI 0.41–0.44), and Brier score was 0.17 (95% CI 0.17–0.17).Conclusion: The machine learning method in our study automatically identified a set of important predictive features, and produced high predictive performance on the risk of PE from the early pregnancy information.

BackgroundWiskott-Aldrich syndrome protein family member 2 (WASF2) has been shown to play an important role in many types of cancer. Therefore, it is worthwhile to further study expression profile of WASF2 in human cancer, which provides new molecular clues about the pathogenesis of ovarian cancer.MethodsWe used a series of bioinformatics methods to comprehensively analyze the relationship between WASF2 and prognosis, tumor microenvironment (TME), immune infiltration, tumor mutational burden (TMB), microsatellite instability (MSI), and tried to find the potential biological processes of WASF2 in ovarian cancer. Biological behaviors of ovarian cancer cells were investigated through CCK8 assay, scratch test and transwell assay. We also compared WASF2 expression between epithelial ovarian cancer tissues and normal ovarian tissues by using immunohistochemical staining.ResultsIn the present study, we found that WASF2 was abnormally expressed across the diverse cancer and significantly correlated with overall survival (OS) and progression-free interval (PFI). More importantly, the WASF2 expression level also significantly related to the TME. Our results also showed that the expression of WASF2 was closely related to immune infiltration and immune-related genes. In addition, WASF2 expression was associated with TMB, MSI, and antitumor drugs sensitivity across various cancer types. Functional bioinformatics analysis demonstrated that the WASF2 might be involved in several signaling pathways and biological processes of ovarian cancer. A risk factor model was found to be predictive for OS in ovarian cancer based on the expression of WASF2. Moreover, in vitro experiments, it was demonstrated that the proliferative, migratory and invasive capacity of ovarian cancer cells was significantly inhibited due to WASF2 knockdown. Finally, the immunohistochemistry data confirmed that WASF2 were highly expressed in ovarian cancer.ConclusionsOur study demonstrated that WASF2 expression was associated with a poor prognosis and may be involved in the development of ovarian cancer, which might be explored as a potential prognostic marker and new targeted treatments.

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