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.

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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
Email	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

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Sample Preparation:

Sampleprep ID:	SP002368
Sampleprep Summary:	The lipid extraction solvent was prepared by adding 700 µL of the isotopically labeled lipid standard mixture to 42 mL of 2-propanol. Serum samples were thawed on ice, followed by extraction of non-polar metabolites. The extraction procedure was carried out by adding the prepared extraction solvent to 10-25 µL serum sample in a 3:1 ratio. Following this step, samples were vortex-mixed for 30 s and centrifuged at 13,000 rpm for 7 min. The resulting supernatant was transferred to LC vials and stored at -80 °C until analysis, which was performed within a week. A blank sample, prepared with LC-MS grade water, underwent the same sample preparation process as the serum samples. A pooled quality control (QC) sample was prepared by adding 2-5 µL aliquot of supernatant to each serum sample. This QC sample was analyzed every 10 runs to assess LC-MS instrument stability through the course of the experiment. Samples were run in a randomized order on consecutive days.

Sampleprep ID:

SP002368

Sampleprep Summary:

The lipid extraction solvent was prepared by adding 700 µL of the isotopically labeled lipid standard mixture to 42 mL of 2-propanol. Serum samples were thawed on ice, followed by extraction of non-polar metabolites. The extraction procedure was carried out by adding the prepared extraction solvent to 10-25 µL serum sample in a 3:1 ratio. Following this step, samples were vortex-mixed for 30 s and centrifuged at 13,000 rpm for 7 min. The resulting supernatant was transferred to LC vials and stored at -80 °C until analysis, which was performed within a week. A blank sample, prepared with LC-MS grade water, underwent the same sample preparation process as the serum samples. A pooled quality control (QC) sample was prepared by adding 2-5 µL aliquot of supernatant to each serum sample. This QC sample was analyzed every 10 runs to assess LC-MS instrument stability through the course of the experiment. Samples were run in a randomized order on consecutive days.