Summary of Study ST003092
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 PR001921. The data can be accessed directly via it's Project DOI: 10.21228/M8DH8T 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 | ST003092 |
Study Title | Prediction of metabolites associated with somatic mutations in cancers by using genome-scale metabolic models and mutation data |
Study Summary | Background Oncometabolites, often generated as a result of a gene mutation, show pro-oncogenic function when abnormally accumulated in cancer cells. Identification of such mutation-associated metabolites will facilitate developing treatment strategies for cancers, but is challenging due to the large number of metabolites in a cell and the presence of multiple genes associated with cancer development. Results Here we report the development of a computational workflow that predicts metabolite-gene-pathway sets. Metabolite-gene-pathway sets present metabolites and metabolic pathways significantly associated with specific somatic mutations in cancers. The computational workflow uses both cancer patient-specific genome-scale metabolic models (GEMs) and mutation data to generate metabolite-gene-pathway sets. A GEM is a computational model that predicts reaction fluxes at a genome scale, and can be constructed in a cell-specific manner by using omics data. The computational workflow is first validated by comparing the resulting metabolite-gene pairs with multi-omics data (i.e., mutation data, RNA-seq data, and metabolome data) from acute myeloid leukemia and renal cell carcinoma samples collected in this study. The computational workflow is further validated by evaluating the metabolite-gene-pathway sets predicted for 18 cancer types, by using RNA-seq data publicly available, in comparison with the reported studies. Therapeutic potential of the resulting metabolite-gene-pathway sets is also discussed. Conclusions Validation of the metabolite-gene-pathway set-predicting computational workflow indicates that a decent number of metabolites and metabolic pathways appear to be significantly associated with specific somatic mutations. The computational workflow and the resulting metabolite-gene-pathway sets will help identify novel oncometabolites, and also suggest cancer treatment strategies. |
Institute | Korea Advanced Institute of Science and Technology (KAIST) |
Last Name | Lee |
First Name | Sang Mi |
Address | 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, South Korea |
sandra1996@kaist.ac.kr | |
Phone | +82-42-350-3955 |
Submit Date | 2024-02-18 |
Num Groups | 2 |
Total Subjects | 38 |
Analysis Type Detail | LC-MS |
Release Date | 2024-02-20 |
Release Version | 1 |
Select appropriate tab below to view additional metadata details:
Sample Preparation:
Sampleprep ID: | SP003213 |
Sampleprep Summary: | The AML and RCC samples for metabolome analysis were prepared in accordance with instructions from Human Metabolome Technologies (HMT). |