윤홍석 (서울대학교 의과대학, 서울대학교병원)

한빛사논문

조회236

서울대학교 의과대학, 서울대학교병원

Genome Biol., 11 March 2024 | https://doi.org/10.1186/s13059-024-03208-8 Prediction of metabolites associated with somatic mutations in cancers by using genome-scale metabolic models and mutation data

GaRyoung Lee^1,2†, Sang Mi Lee ^1,2†, Sungyoung Lee ³, Chang Wook Jeong ⁴, Hyojin Song ³, Sang Yup Lee ^1,2,5, Hongseok Yun ^3*, Youngil Koh ^6* and Hyun Uk Kim ^1,2,5*

¹Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
²Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon 34141, Republic of Korea
³Department of Genomic Medicine, Seoul National University Hospital, Seoul 03080, Republic of Korea
⁴Department of Urology, Seoul National University College of Medicine, and Seoul National University Hospital, Seoul 03080, Republic of Korea
⁵Graduate School of Engineering Biology, BioProcess Engineering Research Center, and BioInformatics Research Center, KAIST, Daejeon 34141, Republic of Korea
⁶Department of Internal Medicine, Seoul National University Hospital, Seoul 03080, Republic of Korea

^†GaRyoung Lee and Sang Mi Lee contributed equally to this work.

^*Corresponding authors: correspondence to Hongseok Yun, Youngil Koh or Hyun Uk Kim

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.

논문정보

형식Research article
게재일2024년 03월 (BRIC 등록일 2024-03-12)
연구진국내 연구진
분야 바이오·의료융합 > 바이오·의료융합 기타

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