Prediction of metabolites associated with somatic mutations in cancers by using genome-scale metabolic models and mutation data
Abstract 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...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2024-03-01
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| Series: | Genome Biology |
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| Online Access: | https://doi.org/10.1186/s13059-024-03208-8 |
| _version_ | 1827156649413967872 |
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| author | GaRyoung Lee Sang Mi Lee Sungyoung Lee Chang Wook Jeong Hyojin Song Sang Yup Lee Hongseok Yun Youngil Koh Hyun Uk Kim |
| author_facet | GaRyoung Lee Sang Mi Lee Sungyoung Lee Chang Wook Jeong Hyojin Song Sang Yup Lee Hongseok Yun Youngil Koh Hyun Uk Kim |
| author_sort | GaRyoung Lee |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-04-24T23:05:30Z |
| format | Article |
| id | doaj.art-0467cfd1336a40f8bd499538e746a008 |
| institution | Directory Open Access Journal |
| issn | 1474-760X |
| language | English |
| last_indexed | 2025-03-20T23:12:48Z |
| publishDate | 2024-03-01 |
| publisher | BMC |
| record_format | Article |
| series | Genome Biology |
| spelling | doaj.art-0467cfd1336a40f8bd499538e746a0082024-08-04T11:24:48ZengBMCGenome Biology1474-760X2024-03-0125112610.1186/s13059-024-03208-8Prediction of metabolites associated with somatic mutations in cancers by using genome-scale metabolic models and mutation dataGaRyoung Lee0Sang Mi Lee1Sungyoung Lee2Chang Wook Jeong3Hyojin Song4Sang Yup Lee5Hongseok Yun6Youngil Koh7Hyun Uk Kim8Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST)Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST)Department of Genomic Medicine, Seoul National University HospitalDepartment of Urology, Seoul National University College of Medicine, and Seoul National University HospitalDepartment of Genomic Medicine, Seoul National University HospitalDepartment of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST)Department of Genomic Medicine, Seoul National University HospitalDepartment of Internal Medicine, Seoul National University HospitalDepartment of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST)Abstract 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.https://doi.org/10.1186/s13059-024-03208-8CancerOncometaboliteGenome-scale metabolic modelMutation dataRNA-seq |
| spellingShingle | GaRyoung Lee Sang Mi Lee Sungyoung Lee Chang Wook Jeong Hyojin Song Sang Yup Lee Hongseok Yun Youngil Koh Hyun Uk Kim Prediction of metabolites associated with somatic mutations in cancers by using genome-scale metabolic models and mutation data Genome Biology Cancer Oncometabolite Genome-scale metabolic model Mutation data RNA-seq |
| title | Prediction of metabolites associated with somatic mutations in cancers by using genome-scale metabolic models and mutation data |
| title_full | Prediction of metabolites associated with somatic mutations in cancers by using genome-scale metabolic models and mutation data |
| title_fullStr | Prediction of metabolites associated with somatic mutations in cancers by using genome-scale metabolic models and mutation data |
| title_full_unstemmed | Prediction of metabolites associated with somatic mutations in cancers by using genome-scale metabolic models and mutation data |
| title_short | Prediction of metabolites associated with somatic mutations in cancers by using genome-scale metabolic models and mutation data |
| title_sort | prediction of metabolites associated with somatic mutations in cancers by using genome scale metabolic models and mutation data |
| topic | Cancer Oncometabolite Genome-scale metabolic model Mutation data RNA-seq |
| url | https://doi.org/10.1186/s13059-024-03208-8 |
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