Identification of serum miR-1246 and miR-150-5p as novel diagnostic biomarkers for high-grade serous ovarian cancer

• Published in: Scientific reports Vol. 13; no. 1; p. 19287
• Main Authors: Niemira, Magdalena, Erol, Anna, Bielska, Agnieszka, Zeller, Anna, Skwarska, Anna, Chwialkowska, Karolina, Kuzmicki, Mariusz, Szamatowicz, Jacek, Reszec, Joanna, Knapp, Pawel, Moniuszko, Marcin, Kretowski, Adam
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 07.11.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 631/67/1517; 692/53/2421; Apoptosis; Biomarkers; Body mass index; Humanities and Social Sciences; Metastasis; MicroRNAs; multidisciplinary; Oncology; Ovarian cancer; Science; Science (multidisciplinary); Womens health
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-023-45317-7

Epithelial ovarian cancer (EOC) is one of the leading cancers in women, with high-grade serous ovarian cancer (HGSOC) being the most common and lethal subtype of this disease. A vast majority of HGSOC are diagnosed at the late stage of the disease when the treatment and total recovery chances are low. Thus, there is an urgent need for novel, more sensitive and specific methods for early and routine HGSOC clinical diagnosis. In this study, we performed miRNA expression profiling using the NanoString miRNA assay in 34 serum samples from patients with HGSOC and 36 healthy women. We identified 13 miRNAs that were differentially expressed (DE). For additional exploration of expression patterns correlated with HGSOC, we performed weighted gene co-expression network analysis (WGCNA). As a result, we showed that the module most correlated with tumour size, nodule and metastasis contained 8 DE miRNAs. The panel including miR-1246 and miR-150-5p was identified as a signature that could discriminate HGSOC patients with AUCs of 0.98 and 1 for the training and test sets, respectively. Furthermore, the above two-miRNA panel had an AUC = 0.946 in the verification cohorts of RT-qPCR data and an AUC = 0.895 using external data from the GEO public database. Thus, the model we developed has the potential to markedly improve the diagnosis of ovarian cancer.