Summary of Study ST002276
This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org, 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.
See: https://www.metabolomicsworkbench.org/about/howtocite.php
This study contains a large results data set and is not available in the mwTab file. It is only available for download via FTP as data file(s) here.
| Study ID | ST002276 |
| Study Title | Machine Learning Reveals Lipidome Dynamics in a Mouse Model of Ovarian Cancer |
| Study 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 |
| ssah9@gatech.edu | |
| Phone | 5746780124 |
| Submit Date | 2022-09-01 |
| Raw Data Available | Yes |
| Raw Data File Type(s) | raw(Thermo) |
| Analysis Type Detail | LC-MS |
| Release Date | 2022-09-28 |
| Release Version | 1 |
Select appropriate tab below to view additional metadata details:
Project:
| 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 |
| Email: | ssah9@gatech.edu |
| Phone: | 5746780124 |
