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Purpose: This study aimed to develop a coarctation probability model incorporating prenatal cardiac sonographic markers to estimate the probability of an antenatal diagnosis of coarctation of the aorta (CoA). Methods: We reviewed 89 fetuses as a“investigation” cohort with prenatal suspicion for CoA and allotted to three ssubgroups:(1) severe CoA: symptoms onset of CoA and surgery within the first three months (15);(2) mild CoA: surgery within four months-one year (29); (3) false-positive CoA: this outcome does not require surgery (45). Using logistic regression to creat the multi-parametric model, and validated using a “validation” cohort of 86 fetuses with suspected CoA subsequently.Results: The prediction model had optimal criterion >0.25 with sensitivity 97.7% and specificity 59.1%, the area under the receiver operator curve is 0.8 which makes sense in diagnosis of CoA and risk stratification. The risk assessment demonstrated that fetuses with model probability>60% should make an inpatient observation for high risk of CoA whereas <15% should not undergo clinical follow-up imaging.Conclusion: Based on these results, we recommend measuring the model in all fetuses with suspected CoA may improve the accuracy of risk assessment. The objectivity of its parameters may allow its implementation working in multicenter studies of fetal cardiology.

The outcomes of ovarian cancer are complicated and usually unfavorable due to their diagnoses at a late stage.Identifying the efficient prognostic biomarkers to improve the survival of ovarian cancer is urgently warranted. The survival-related pseudogenes retrieved from the Cancer Genome Atlas database were screened by univariate Cox regression analysis and further assessed by least absolute shrinkage and selection operator (LASSO) method. A risk score model based on the prognostic pseudogenes was also constructed. The pseudogene-mRNA regulatory networks were established using correlation analysis, and their potent roles in the ovarian cancer progression were uncovered by functional enrichment analysis. Lastly, ssGSEA and ESTIMATE algorithms was used to evaluate the levels of immune cell infiltrations in cancer tissues and explore their relationship with risk signature. A prediction model of 10pseudogenes including RPL10P6, AC026688.1, FAR2P4, AL391840.2, AC068647.2, FAM35BP, GBP1P1, ARL4AP5, RPS3AP2, and AMD1P1 was established. The 10-pseudogenes signature was demonstrated to be an independent prognostic factor in patient with ovarian cancer in the random set (hazard ratio [HR] = 2.512, 95% confidence interval[CI] = 2.03-3.11, P < 0.001) and total set (HR = 1.71, 95% CI = 1.472-1.988, P < 0.001). When models integrating with age, grade, stage, and risk signature, the Area Under Curve (AUC) of the 1-year, 3-year, 5-year and 10-year Receiver Operating Characteristic curve in the random set and total set were 0.854, 0.824, 0.855, 0.805 and 0.679, 0.697, 0.739, 0.790, respectively. The results of functional enrichment analysis indicated that the underlying mechanisms by which these pseudogenes influence cancer prognosis may involve the immune-related biological processes and signaling pathways. Correlation analysis showed that risk signature was significantly correlated with immune cell infiltration and immune score. We identified a novel 10-pseudogenes signature to predict the survival of patients with ovarian cancer, and that may serve as novel possible prognostic biomarkers and therapeutic targets for ovarian cancer.

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