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MACdb: A Curated Knowledgebase for Metabolic Associations across Human Cancers
Cancer is one of the leading causes of human death. As metabolomics techniques become more and more widely used in cancer research, metabolites are increasingly recognized as crucial factors in both cancer diagnosis and treatment. In this study, we developed MACdb (https://ngdc.cncb.ac.cn/macdb), a...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Association for Cancer Research
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10320464/ https://www.ncbi.nlm.nih.gov/pubmed/37027007 http://dx.doi.org/10.1158/1541-7786.MCR-22-0909 |
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author | Sun, Yanling Zheng, Xinchang Wang, Guoliang Wang, Yibo Chen, Xiaoning Sun, Jiani Xiong, Zhuang Zhang, Sisi Wang, Tianyi Fan, Zhuojing Bu, Congfan Bao, Yiming Zhao, Wenming |
author_facet | Sun, Yanling Zheng, Xinchang Wang, Guoliang Wang, Yibo Chen, Xiaoning Sun, Jiani Xiong, Zhuang Zhang, Sisi Wang, Tianyi Fan, Zhuojing Bu, Congfan Bao, Yiming Zhao, Wenming |
author_sort | Sun, Yanling |
collection | PubMed |
description | Cancer is one of the leading causes of human death. As metabolomics techniques become more and more widely used in cancer research, metabolites are increasingly recognized as crucial factors in both cancer diagnosis and treatment. In this study, we developed MACdb (https://ngdc.cncb.ac.cn/macdb), a curated knowledgebase to recruit the metabolic associations between metabolites and cancers. Unlike conventional data-driven resources, MACdb integrates cancer-metabolic knowledge from extensive publications, providing high quality metabolite associations and tools to support multiple research purposes. In the current implementation, MACdb has integrated 40,710 cancer-metabolite associations, covering 267 traits from 17 categories of cancers with high incidence or mortality, based entirely on manual curation from 1,127 studies reported in 462 publications (screened from 5,153 research papers). MACdb offers intuitive browsing functions to explore associations at multi-dimensions (metabolite, trait, study, and publication), and constructs knowledge graph to provide overall landscape among cancer, trait, and metabolite. Furthermore, NameToCid (map metabolite name to PubChem Cid) and Enrichment tools are developed to help users enrich the association of metabolites with various cancer types and traits. IMPLICATION: MACdb paves an informative and practical way to evaluate cancer-metabolite associations and has a great potential to help researchers identify key predictive metabolic markers in cancers. |
format | Online Article Text |
id | pubmed-10320464 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | American Association for Cancer Research |
record_format | MEDLINE/PubMed |
spelling | pubmed-103204642023-07-06 MACdb: A Curated Knowledgebase for Metabolic Associations across Human Cancers Sun, Yanling Zheng, Xinchang Wang, Guoliang Wang, Yibo Chen, Xiaoning Sun, Jiani Xiong, Zhuang Zhang, Sisi Wang, Tianyi Fan, Zhuojing Bu, Congfan Bao, Yiming Zhao, Wenming Mol Cancer Res Metabolism Cancer is one of the leading causes of human death. As metabolomics techniques become more and more widely used in cancer research, metabolites are increasingly recognized as crucial factors in both cancer diagnosis and treatment. In this study, we developed MACdb (https://ngdc.cncb.ac.cn/macdb), a curated knowledgebase to recruit the metabolic associations between metabolites and cancers. Unlike conventional data-driven resources, MACdb integrates cancer-metabolic knowledge from extensive publications, providing high quality metabolite associations and tools to support multiple research purposes. In the current implementation, MACdb has integrated 40,710 cancer-metabolite associations, covering 267 traits from 17 categories of cancers with high incidence or mortality, based entirely on manual curation from 1,127 studies reported in 462 publications (screened from 5,153 research papers). MACdb offers intuitive browsing functions to explore associations at multi-dimensions (metabolite, trait, study, and publication), and constructs knowledge graph to provide overall landscape among cancer, trait, and metabolite. Furthermore, NameToCid (map metabolite name to PubChem Cid) and Enrichment tools are developed to help users enrich the association of metabolites with various cancer types and traits. IMPLICATION: MACdb paves an informative and practical way to evaluate cancer-metabolite associations and has a great potential to help researchers identify key predictive metabolic markers in cancers. American Association for Cancer Research 2023-07-05 2023-04-07 /pmc/articles/PMC10320464/ /pubmed/37027007 http://dx.doi.org/10.1158/1541-7786.MCR-22-0909 Text en ©2023 The Authors; Published by the American Association for Cancer Research https://creativecommons.org/licenses/by-nc-nd/4.0/This open access article is distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license. |
spellingShingle | Metabolism Sun, Yanling Zheng, Xinchang Wang, Guoliang Wang, Yibo Chen, Xiaoning Sun, Jiani Xiong, Zhuang Zhang, Sisi Wang, Tianyi Fan, Zhuojing Bu, Congfan Bao, Yiming Zhao, Wenming MACdb: A Curated Knowledgebase for Metabolic Associations across Human Cancers |
title | MACdb: A Curated Knowledgebase for Metabolic Associations across Human Cancers |
title_full | MACdb: A Curated Knowledgebase for Metabolic Associations across Human Cancers |
title_fullStr | MACdb: A Curated Knowledgebase for Metabolic Associations across Human Cancers |
title_full_unstemmed | MACdb: A Curated Knowledgebase for Metabolic Associations across Human Cancers |
title_short | MACdb: A Curated Knowledgebase for Metabolic Associations across Human Cancers |
title_sort | macdb: a curated knowledgebase for metabolic associations across human cancers |
topic | Metabolism |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10320464/ https://www.ncbi.nlm.nih.gov/pubmed/37027007 http://dx.doi.org/10.1158/1541-7786.MCR-22-0909 |
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