Prediction of candidate RNA signatures for recurrent ovarian cancer prognosis by the construction of an integrated competing endogenous RNA network.

Tumor recurrence hinders treatment of ovarian cancer. The present study aimed to identify potential biomarkers for ovarian cancer recurrence prognosis and explore relevant mechanisms. RNA­sequencing of data from the TCGA database and GSE17260 dataset was carried out. Samples of the data were grouped according to tumor recurrence information. Following data normalization, differentially expressed genes/micro RNAs (miRNAs)/long non­coding (lncRNAs) (DEGs/DEMs/DELs) were selected between recurrent and non­recurrent samples. Their correlations with clinical information were analyzed to identify prognostic RNAs. A support vector machine classifier was used to find the optimal gene set with feature genes that could conclusively distinguish different samples. A protein­protein interaction (PPI) network was established for DEGs using relevant protein databases. An integrated 'lncRNA/miRNA/mRNA' competing endogenous RNA (ceRNA) network was constructed to reveal potential regulatory relationships among different RNAs. We identified 36 feature genes (e.g. TP53 and RBPMS) for the classification of recurrent and non­recurrent ovarian cancer samples. Prediction with this gene set had a high accuracy (91.8%). Three DELs (WT1­AS, NBR2 and ZNF883) were highly associated with the prognosis of recurrent ovarian cancer. Predominant DEMs with their targets were hsa­miR­375 (target: RBPMS), hsa­miR­141 (target: RBPMS), and hsa­miR­27b (target: TP53). Highlighted interactions in the ceRNA network were 'WT1­AS­hsa­miR­375­RBPMS' and 'WT1­AS­-hsa­miR­27b­TP53'. TP53, RBPMS, hsa­miR­375, hsa­miR­141, hsa­miR­27b, and WT1­AS may be biomarkers for recurrent ovarian cancer. The interactions of 'WT1­AS­hsa­-miR­375­RBPMS' and 'WT1­AS­hsa­miR­27b­TP53' may be potential regulatory mechanisms during cancer recurrence.

A network-pathway based module identification for predicting the prognosis of ovarian cancer patients.

BACKGROUND: This study aimed to screen multiple genes biomarkers based on gene expression data for predicting the survival of ovarian cancer patients. METHODS: Two microarray data of ovarian cancer samples were collected from The Cancer Genome Atlas (TCGA) database. The data in the training set were used to construct Reactome functional interactions network, which then underwent Markov clustering, supervised principal components, Cox proportional hazard model to screen significantly prognosis related modules. The distinguishing ability of each module for survival was further evaluated by the testing set. Gene Ontology (GO) functional and pathway annotations were performed to identify the roles of genes in each module for ovarian cancer. RESULTS: The network based approach identified two 7-gene functional interaction modules (31: DCLRE1A, EXO1, KIAA0101, KIN, PCNA, POLD3, POLD2; 35: DKK3, FABP3, IRF1, AIM2, GBP1, GBP2, IRF2) that are associated with prognosis of ovarian cancer patients. These network modules are related to DNA repair, replication, immune and cytokine mediated signaling pathways. CONCLUSIONS: The two 7-gene expression signatures may be accurate predictors of clinical outcome in patients with ovarian cancer and has the potential to develop new therapeutic strategies for ovarian cancer patients.

Association of obestatin with blood pressure in the third trimesters of pregnancy.

Obestatin is a recently discovered 23-amino acid peptide encoded by the same gene that encodes ghrelin. It has been reported that there is a significant negative correlation between the plasma ghrelin concentration and systemic blood pressure in patients with pregnancy-induced hypertension. We investigated the plasma concentration of obestatin in 18 non-pregnant women, 18 normal pregnant women, and 15 patients with pregnancy-induced hypertension. The plasma concentrations of obestatin in these 3 groups of women were 63.4+/-9.5pg/ml, 38.1+/-6.3pg/ml, and 46.0+/-9.3pg/ml, respectively. In non-pregnant women, there was no correlation between the plasma obestatin concentration and the mean arterial pressure. However, there was a positive correlation between the plasma obestatin concentration and the mean arterial pressure in normal pregnant women and pregnant women with pregnancy-induced hypertension. These results suggest that obestatin may have some potential role in the regulation of blood pressure in normal pregnant women and women with pregnancy-induced hypertension.

Corticotropin-releasing hormone acts on CRH-R1 to inhibit the spontaneous contractility of non-labouring human myometrium at term.

AIMS: Corticotropin-releasing hormone (CRH) has been implicated in the mechanisms controlling human parturition. The aims of the present study were to explore effects of CRH on contractility of human term myometrium and compare these effects in labouring and non-labouring myometrial strips. MAIN METHODS: The cumulative effects of CRH (10(-10) to 10(-7) mol/l) on the spontaneous contractility of labouring and non-labouring myometrial samples were evaluated using isometric tension recordings. KEY FINDINGS: CRH exhibited a concentration-dependent relaxant effect on spontaneous contractions in non-labouring term myometrium. This effect was mediated principally via a reduction in the amplitude rather than any changes in the frequency of contractions. The CRH-induced inhibitory effect on contractility could be blocked by pre-treatment with a CRH-R1 antagonist antalarmin, but not by pre-treatment with the CRH-R2 antagonist astressin 2B. CRH had no effect on spontaneous contractions in the labouring myometrium, as no change in either the amplitude or the frequency was observed. SIGNIFICANCE: Our findings indicate that CRH acts on CRH-R1 to inhibit spontaneous contractions in term myometrium from women who were not undergoing labour, but not those who were undergoing labour, supporting the hypothesis that CRH exerts dual effect on myometrium during pregnancy.