Ovarian cancer (OC) is the most lethal gynecological malignancy worldwide. Standard therapeutic approaches for OC involve surgery, chemotherapy and radiotherapy but, despite the initial efficacy, 5-year survival rates remain unfavorable. Around 70% of OC patients develops disease recurrence and needs subsequent treatment lines, which are rarely curative but rather palliative. OC relapsing is mainly due to the expansion of cancer stem cells (CSCs), having the ability of self-renew and resistance to conventional therapies. Non-coding RNAs (ncRNAs) and epigenetic drivers have been shown to be essential in the OC biology and represent potential targets for anticancer treatments since they can be finely tuned and reprogrammed. In the era of "network medicine", we will integrate the high-throughput gene expression and epigenetic profile data, freely available in different tumor-related platforms (TCGA and GEO), with the OC histopathological and molecular features. The nodes and edges of this network-based approach will be crucial for the elucidation of the complex interactions between the OC-associated ncRNAs and epigenetic modifications, and for the identification of novel therapeutic targets and biomarkers. So, we will validate the expression of the identified genes and methylation/acetylation profiles in OC samples in order to define their diagnostic/prognostic value. We will also evaluate the antitumoral activity of selected agents (epi-drugs or ncRNAs) in OC cellular models, as single agents or in combination with radiation therapy, focusing on their effects on CSC-related pathways. Our understanding of the molecular networks that govern OC development and drug resistance will provide new insights for personalized combinatorial therapies, which might be translated into more efficient and less toxic treatments for OC patients.
