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

Abstract

Tumor recurrence hinders treatment of ovarian cancer.Thepresentstudyaimedtoidentifypotentialbiomarkers for ovarian cancer recurrence prognosis and explore relevant mechanisms. RNA-sequencing 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 non-coding (lncRNAs) (DEGs/DEMs/DELs) were selected between recurrent and non-recurrent 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 protein-protein 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 non-recurrent ovarian cancer samples. Prediction with this gene set had a high accuracy (91.8%). Three DELs (WT1-AS, NBR2 and ZNF883) were highly associated with the prognosis of recurrent ovarian cancer. Predominant DEMs with their targets were hsa-miR-375 (target: RBPMS), hsa-miR-141 (target: RBPMS), and hsa-miR-27b (target: TP53). Highlighted interactions in the ceRNA network were ‘WT1-AS-hsa-miR-375-RBPMS’ and ‘WT1-AS-hsa-miR-27b-TP53’. TP53, RBPMS, hsa-miR-375, hsa-miR-141, hsa-miR-27b, and WT1-AS may be biomarkers for recurrent ovarian cancer. The interactions of ‘WT1-AS-hsa-miR-375-RBPMS’ and ‘WT1-AS-hsa-miR-27b-TP53’ may be potential regulatory mechanisms during cancer recurrence.