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.

BackgroundThe aim of this study was to explore the source and morphology of a small supernumerary marker chromosome (sSMC) from karyotype analysis of a patient with a unique case of mosaic Turner syndrome. The study findings will provide technical reference and genetic counseling for similar cases.Case PresentationA female patient with 46,X,+mar karyotype was diagnosed by genetic karyotype analysis. Genetic methods including fluorescence in situ hybridization (FISH) and copy number variation sequencing (CNV-seq) based on low-depth whole-genome sequencing were used to explore the source and morphology of sSMC. FISH technology showed that 56.5% of the cells were X and 43.5% of the cells were XY. CNV-seq detection found that the sSMC was chrY, implying that the patient's karyotype was mos 45,X[58.6%]/46,XY[41.4%]. Retrospective karyotype analysis indicated that the female patient's sSMC was inherited from her father's small chrY. Customized FISH probe of Yq12 microdeletion was positive, indicating that the sSMC was a del(Y)(q12). Based on the results of genetic diagnosis, the specialist doctor gave a comprehensive genetic consultation and ordered regular follow-up examinations.ConclusionsThe findings of the current study showed that the chromosome description of the unique Turner case was mos 45,X[56.5%]/46,X,del(Y)(q12)[43.5%]. FISH technology played a key role in diagnosis of mosaicism. The terminal deletion of mosaic chrY provided a scientific and an accurate explanation for masculinity failure and abnormal sexual development of the current case.

Background: Ovarian cancer was one of the leading causes of death in gynecological malignancies, of which molecular mechanism hadn’t been elucidated clearly yet. Our research aimed to reveal the potential key molecular and biological processes of ovarian cancer by means of bioinformatics.Methods: The microarray sets of miRNA and mRNA expression profiles were downloaded from the GEO database. The target prediction was performed on the differentially expressed miRNAs identified and the overlapped differentially expressed genes (DEGs) were obtained combined with miRNA and mRNA datasets. The regulatory network of miRNA-gene was further constructed by cytoscape software. The overlapped DEGs in the network were analyzed to explore the biological processes involved by enrichment analysis. The molecular protein-protein interaction (PPI) network was used to identify key genes among the DEGs.Results: A total of 167 overlapped DEGs were identified. The miRNA-gene network analysis found that miR-29c-3p, miR-1271-5p, and miR-133b, existed the most extensive targeting relationship with overlapped DEGs, being three key miRNAs of the regulatory network, and played the role of tumor suppressor. The GO enrichment showed that the overlapped DEGs were mainly involved in process named extracellular related organization, embryonic organ development, postsynaptic specialization, collagen trimer and DNA−binding transcription activator et al. The KEGG pathway analysis showed that these DEGs were involved in protein digestion and absorption and relaxin signaling pathway. The PPI network identified 10 key genes, playing the role in promoting tumor.Conclusion: The methodology used and identification of key molecules in our study contributed to understanding the pathogenesis of ovarian cancer and providing new candidate biomarkers for early screening of ovarian cancer.