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.

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Study IDST003092
Study TitlePrediction of metabolites associated with somatic mutations in cancers by using genome-scale metabolic models and mutation data
Study SummaryBackground 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 NameLee
First NameSang Mi
Address291 Daehak-ro, Yuseong-gu, Daejeon, 34141, South Korea
Emailsandra1996@kaist.ac.kr
Phone+82-42-350-3955
Submit Date2024-02-18
Num Groups2
Total Subjects38
Analysis Type DetailLC-MS
Release Date2024-02-20
Release Version1
Sang Mi Lee Sang Mi Lee
https://dx.doi.org/10.21228/M8DH8T
ftp://www.metabolomicsworkbench.org/Studies/ application/zip

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Combined analysis:

Analysis ID AN005060 AN005061
Analysis type MS MS
Chromatography type CE CE
Chromatography system Agilent CE-TOFMS system Agilent CE-TOFMS system
Column Capillary: Fused silica capillary i.d. 50 μm × 80 cm Capillary: Fused silica capillary i.d. 50 μm × 80 cm
MS Type ESI ESI
MS instrument type Other Other
MS instrument name Agilent CE-TOFMS system Agilent CE-TOFMS system
Ion Mode UNSPECIFIED UNSPECIFIED
Units Relative peak area Relative peak area
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