Summary of project PR001457

This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench,, where it has been assigned Project ID PR001457. The data can be accessed directly via it's Project DOI: 10.21228/M8D133 This work is supported by NIH grant, U2C- DK119886.


Project ID: PR001457
Project DOI:doi: 10.21228/M8D133
Project Title:Machine Learning Reveals Lipidome Dynamics in a Mouse Model of Ovarian Cancer
Project Summary:Ovarian cancer (OC) is one of the deadliest cancers affecting the female reproductive system. It presents little or no symptoms at the early stages, and typically unspecific symptoms at later stages. Of the OC subtypes, high-grade serous carcinoma (HGSC) is responsible for most OC deaths. However, very little is known about the metabolic course of this disease. In this longitudinal study, we investigated the temporal course of lipidome changes in a Dicer-Pten Double-Knockout (DKO) HGSC mouse model using machine and statistical learning approaches. Early progression of HGSC was marked by increased levels of phosphatidylcholines and phosphatidylethanolamines. In contrast, later stages were marked by more diverse lipids alterations, including fatty acids and their derivatives, triglycerides, ceramides, hexosylceramides, sphingomyelins, lysophosphatidylcholines, and phosphatidylinositols. These alterations provided evidence of perturbations in cell membrane stability, proliferation, and survival and candidates for early-stage and prognostic markers in humans.
Institute:Georgia Institute of Technology
Department:Chemistry and Biochemistry
Laboratory:Fernandez group
Last Name:Sah
First Name:Samyukta
Address:901 Atlantic Dr NW, Atlanta, GA, 30332, USA

Summary of all studies in project PR001457

Study IDStudy TitleSpeciesInstituteAnalysis
(* : Contains Untargted data)
(* : Contains raw data)
ST002276 Machine Learning Reveals Lipidome Dynamics in a Mouse Model of Ovarian Cancer Mus musculus Georgia Institute of Technology MS* 2022-09-28 1 459 Uploaded data (111.1G)*