David Cottrill
The Molecular and Cellular Biology graduate program presents an MCB 7410 Seminar featuring David Cottrill discussing “The Use of Network Medicine to Predict Molecular Signatures of Ovarian Diseases” on Tuesday, Dec. 5, at 4:35 p.m. in Porter 104.
Refreshments are provided.
Cottrill is a graduate student in Biological Sciences.
Abstract: Ovarian cancer (OC), endometriosis (EM), and polycystic ovarian syndrome (PCOS) are disorders of the female reproductive system that will effect from 3% (ovarian cancer) to as high as 20% of reproductive aged women worldwide1. Due to their etiological relationship, reports of a link between these diseases can be tracked back to 1925 when John Sampson identified ovarian carcinoma cells derived from endometrial lesions located on the afflicted ovary2. Though this connection has been extensively described, a molecular basis has yet to be identified. Network medicine is the systems biology based use of genome-scale biological networks, high throughput data sets, and topological analysis methods to provide insight into the molecular mechanisms behind various disease processes. In the present study, Kori, et al. utilized this approach to analyze and integrate transcriptome, protein-protein interaction, transcriptional control and metabolomic data from OC, EM, and PCOS. Their analysis identified several pathways, which upon further investigation were found to be associated with cancer development in all three diseases. Those shared pathways identified were related to MAPK signaling, cell cycle, and apoptosis. These data provide a molecular basis for the tendency of both EM and PCOS to progress to OC, and indicate a shared pathogenesis for all three diseases. Through the use of already available data, this study provided candidate molecular signatures for 3 related ovarian diseases that may be used as a guide for future research into the molecular mechanisms of their pathogenesis. They may also reduce the time needed to develop diagnostic tools and therapeutic strategies, improving treatment of these ovarian diseases. This study demonstrates that using a network medicine approach to integrate and analyze existing large data sets can provide a molecular roadmap to improve research efficiency and aid in the development of clinical tools and treatment strategies.
References
- Kori, et (2016) Molecular signatures of ovarian diseases: Insights from network medicine perspective. Systems Biology in Reproductive Medicine. 62(4): 266-282
- Sampson, (1925) Endometrial Carcinoma of the ovary, arising in the endometrial tissue in that organ. Arch Surg. 1925;10(1):1–72.
Comments