Identification of predictive biomarkers in ovarian cancer patients using Single-Nucleotide-Polymorphism and microRNA expression data

Ovarian cancer is one of the leading causes of death among gynecologic cancers in developed countries. The early diagnosis of the disease is rare, and survival rates after 5 years are below 35% of the patients. In recent years, the introduction of genomic in cancer medicine has provided new tools for the treatment of ovarian cancer patients, such as identification of diagnostic biomarkers or targeting specific genetic pathways for innovative therapeutic approaches. The aim of this study was the identification of the differences in microRNAs expression comparing samples obtained from three different matrices: ascitic-fluid microvesicles, serum-microvesicles, and primary lesion. The purpose is to assess the differences among the microvesicles secreted into the two different fluids, along with the differences between the vesicles and the primary tumor. Furthermore, specific features in miRNA expression that may be associated with development of relapse or resistance to therapy were extracted and compared with literature results.