Summary of Study ST001172
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 PR000784. The data can be accessed directly via it's Project DOI: 10.21228/M8BH6F 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 | ST001172 |
| Study Title | Deep Metabolomics of a High-Grade Serous Ovarian Cancer Triple Knockout Mouse Model. |
| Study Type | Untargeted metabolomics |
| Study Summary | Metabolic alternations were investigated by applying Ultra Performance Liquid Chromatography Mass Spectrometry (UPLC-MS) to serum samples collected from triple knockout (TKO) mice at pre-malignant, early, and advanced stages of HGSC. Samples were analyzed with control mice, which have the same genetic background as TKO mice but develop no tumors. To enhance the selectivity for HGSC-specific metabolite markers, a tumor control group was also included. These were uterine tumor (UT) mice that developed uterine tumors, but no HGSC. All samples were analyzed using reverse phase (RP) and hydrophilic interaction liquid chromatography (HILIC) UPLC-MS analysis in positive and negative ion modes. |
| Institute | Georgia Institute of Technology |
| Department | Chemistry |
| Laboratory | Fernández |
| Last Name | Huang |
| First Name | Danning |
| Address | 901 Atlantic Dr NE |
| dhuang74@gatech.edu | |
| Phone | 4045127523 |
| Submit Date | 2019-04-16 |
| Num Groups | 5 |
| Total Subjects | 84 |
| Num Females | 84 |
| Raw Data Available | Yes |
| Raw Data File Type(s) | raw(Thermo) |
| Analysis Type Detail | LC-MS |
| Release Date | 2019-10-11 |
| Release Version | 1 |
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Subject:
| Subject ID: | SU001237 |
| Subject Type: | Mammal |
| Subject Species: | Mus musculus |
| Taxonomy ID: | 10090 |
| Gender: | Female |
