INVESTIGADORES
MONGE Maria Eugenia
congresos y reuniones científicas
Título:
Metabolomic Analysis of Early-Stage Ovarian Cancer in a Dicer-Pten Double Knockout Mouse Model
Autor/es:
LAURA WINALSKI; CHRISTINA M. JONES; MARÍA EUGENIA MONGE; JAEYEON KIM; MARTIN M. MATZUK; FACUNDO M. FERNÁNDEZ
Lugar:
Nashville
Reunión:
Conferencia; The 66th Southeastern Regional Meeting of the American Chemical Society (SERMACS); 2014
Institución organizadora:
American Chemical Society
Resumen:
Despite the fact ovarian cancer is the deadliest gynecological disease among women, the global five year survival rate among its patients is 44.2%. Early stage ovarian cancer is essentially asymptomatic while late stage symptoms rarely lead to an accurate diagnosis as they can be characteristic of several ailments, including irritable bowel syndrome, ovarian cysts, and kidney complications. Ninety percent of serous ovarian carcinomas (the most common epithelial cancer type) are high-grade and characterized by poorly differentiated or abnormal tumors, rapid expansion, and aggressive behavior. As such, 79% of these tumors remain undetected until the cancer is late-stage ? a major contribution to the low survival rate. A ?Dicer-Pten double-knockout (DKO)? mouse model of high-grade serous ovarian cancer has proved a valuable comparison to human serous ovarian cancer and is currently being studied to identify potential metabolic biomarkers for this aggressive disease. Metabolites were extracted from blood serum using methanol in a 3:1 (v/v) dilution ratio to serum. Metabolite extracts were lyophilized and reconstituted in the initial composition of the chromatographic mobile phase. High resolution mass spectra were acquired in positive electrospray ionization mode for m/z 50-1200. Metabolic features were extracted using MZmine 2.10 software. With 21 metabolites selected from these profiles using genetic algorithms for variable selection, orthogonal projection to latent structures-discriminant analysis (oPLS-DA) discriminated early-stage DKO mice from control mice with 100% accuracy, sensitivity, and specificity. These 21 metabolites are currently being identified using metabolomic databases.
