Identification and validation of potential prognostic and predictive miRNAs of epithelial ovarian cancer

• DOI: 10.1371/journal.pone.0207319
• Publication Date: 2018-11-26T19:39:19Z
• Authors: Kira Philipsen Prahm; Claus Høgdall; Mona Aarenstrup K...; Ib Jarle Christensen; Guy Wayne Novotny; Estrid Høgdall
• Keywords: Adult; Science; Carcinoma, Ovarian Epithelial; MicroRNAs/biosynthesis; Neoplasm/biosynthesis; Disease-Free Survival; Databases; Ovarian Neoplasms/genetics; 03 medical and health sciences; 0302 clinical medicine; 80 and over; Humans; RNA, Neoplasm; Aged; Oligonucleotide Array Sequence Analysis; Aged, 80 and over; Ovarian Neoplasms; Nucleic Acid; Gene Expression Profiling; Carcinoma; Q; R; Middle Aged; Ovarian Epithelial/genetics; 3. Good health; Survival Rate; Carcinoma, Ovarian Epithelial/genetics; MicroRNAs; RNA; Medicine; Female; Databases, Nucleic Acid; RNA, Neoplasm/biosynthesis; Research Article

Background Ovarian cancer is the leading cause of death by gynecologic cancers in Western world. The aim study was to identify microRNAs (miRNAs) associated with prognosis and/or resistance chemotherapy among patients epithelial ovarian cancer. Methods Using information from Pelvic Mass Study we identified a cohort women Tumor tissues were then collected and analyzed global miRNA microarrays. MiRNA profiling linked survival time progression using Cox proportional-hazards regression models. Logistic models used for analysis chemotherapy. Our results validated external datasets retrieved NCBI Gene Expression Omnibus database. Results A total 197 included microarray analysis. In multivariate analyses number miRNAs significantly correlated overall (miR-1183 (HR: 1.42, 95% CI:1.17–1.74, p = 0.0005), miR-126-3p 1.38, CI:1.11–1.71, 0.0036), (miR-139-3p 1.48, CI: 1.13–1.94, 0.0047), miR-802 0.48, 0.29–0.78, 0.0035)), free (miR-23a-5p (HR:1.32, CI:1.09–1.61, 0.004), miR-23a-3p (HR:1.70, CI:1.15–2.51, 0.0074), 0.29–0.80, 0.0048)), (miR-1234 0.26, 0.11–0.64, 0.003)). few our training cohort, cohorts similar results. Conclusion Eight as significant predictors survival, progression, resistance. these datasets. Inter-platform inter-laboratory variations may have influence on ability compare reproduce use potential markers relapse warrants further investigation.