Noninvasive prenatal testing of common aneuploidies has become routine over the past decade, but testing of monogenic disorders remains a challenge in clinical implementation. Most recent studies have inherent limitations, such as complicated procedures, a lack of versatility, and the need for prior knowledge of parental genotypes or haplotypes. To overcome these limitations, a robust and versatile next‐generation sequencing‐based cell‐free DNA (cfDNA) allelic molecule counting system termed cfDNA barcode‐enabled single‐molecule test (cfBEST) is developed for the noninvasive prenatal diagnosis (NIPD) of monogenic disorders. The accuracy of cfBEST is found to be comparable to that of droplet digital polymerase chain reaction (ddPCR) in detecting low‐abundance mutations in cfDNA. The analytical validity of cfBEST is evidenced by a β‐thalassemia assay, in which a blind validation study of 143 at‐risk pregnancies reveals a sensitivity of 99.19% and a specificity of 99.92% on allele detection. Because the validated cfBEST method can be used to detect maternal‐fetal genotype combinations in cfDNA precisely and quantitatively, it holds the potential for the NIPD of human monogenic disorders.

Ovarian cancer is one of the leading causes of gynecological malignancy-related deaths. The underlying molecular development mechanism has however not been elucidated. In this study, we used bioinformatics to reveal critical molecular and biological processes associated with ovarian cancer. The microarray datasets of miRNA and mRNA expression profiles were downloaded from the Gene Expression Omnibus (GEO) database. Besides, we performed target prediction of the identified differentially expressed miRNAs. The overlapped differentially expressed genes (DEGs) were obtained combined with miRNA targets predicted and the DEGs identified from the mRNA dataset. The Cytoscape software was used to design a regulatory network of miRNA-gene. Moreover, the overlapped DEGs in the network were subjected to enrichment analysis to explore the associated biological processes. The molecular protein-protein interaction (PPI) network was used to identify the key genes among the DEGs of prognostic value for ovarian cancer, and the genes were evaluated via Kaplan-Meier curve analysis. A total of 186 overlapped DEGs were identified. Through miRNA-gene network analysis, we found that miR-195-5p, miR-424-5p, and miR-497-5p highly exhibited targeted association with overlapped DEGs. The three miRNAs are critical in the regulatory network and act as tumor suppressors. The overlapped DEGs were mainly associated with protein metabolism, histogenesis, and development of the reproductive system and ocular tissues. The PPI network identified 10 vital genes that promote tumor progression. Survival analysis found that CEP55 and CCNE1 may be associated with the prognosis of ovarian cancer. These findings provide insights to understand the pathogenesis of ovarian cancer and suggest new candidate biomarkers for early screening of ovarian cancer.