A RNAi screening to identify novel targets in cisplatin‐resistant ovarian cancer

Abstract

Ovarian cancer ranks 5th in cancer-related deaths among women around the world. According to the American Cancer Society, it is estimated that for 2020, there will be 21,750 newly diagnosed ovarian cancer women and 13,940 related deaths in the US alone. Around 94% of ovarian cancer patients survive five years after diagnosis when the disease is detected at an early stage. However, less than 45% of women patients survive when this disease is diagnosed at advanced stages. Currently, there is no effective therapy for ovarian cancer patients, in particular for those that become resistant to chemotherapy (cisplatin). Thus, we need to identify novel targets for ovarian cancer therapy. We performed RNA deep sequencing studies in cisplatin-sensitive and cisplatin-resistant ovarian cancer cells. Several RNA (messengers and long non-coding) were differentially abundant in cisplatin-resistant versus cisplatin sensitive cells. Many of these RNAs have not been previously studied in ovarian cancer. Therefore, we performed rigorous bioinformatic studies that included data filtering and survival rates (using Kaplan-Meier Plot Analysis) and IPA (Ingenuity Pathway Analysis) to select essential candidate genes that can be used as potential targets for ovarian cancer therapy. Additional data filtering was used to choose a list of candidate genes whose expression correlates with the overall survival and progression-free survival of ovarian cancer patients. Twenty-seven resulting genes from our bioinformatics analysis were further evaluated by performing a small-interfering RNA (siRNA)-mediated silencing and cell growth/proliferation assays in cisplatin-resistant ovarian cancer cell lines. Future experiments will investigate the biological consequences of targeting each of these candidate genes in ovarian cancer cells and ovarian cancer mouse models.