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CTpathway: a CrossTalk-based pathway enrichment analysis method for cancer research
BACKGROUND: Pathway enrichment analysis (PEA) is a common method for exploring functions of hundreds of genes and identifying disease-risk pathways. Moreover, different pathways exert their functions through crosstalk. However, existing PEA methods do not sufficiently integrate essential pathway fea...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9563764/ https://www.ncbi.nlm.nih.gov/pubmed/36229842 http://dx.doi.org/10.1186/s13073-022-01119-6 |
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author | Liu, Haizhou Yuan, Mengqin Mitra, Ramkrishna Zhou, Xu Long, Min Lei, Wanyue Zhou, Shunheng Huang, Yu-e Hou, Fei Eischen, Christine M. Jiang, Wei |
author_facet | Liu, Haizhou Yuan, Mengqin Mitra, Ramkrishna Zhou, Xu Long, Min Lei, Wanyue Zhou, Shunheng Huang, Yu-e Hou, Fei Eischen, Christine M. Jiang, Wei |
author_sort | Liu, Haizhou |
collection | PubMed |
description | BACKGROUND: Pathway enrichment analysis (PEA) is a common method for exploring functions of hundreds of genes and identifying disease-risk pathways. Moreover, different pathways exert their functions through crosstalk. However, existing PEA methods do not sufficiently integrate essential pathway features, including pathway crosstalk, molecular interactions, and network topologies, resulting in many risk pathways that remain uninvestigated. METHODS: To overcome these limitations, we develop a new crosstalk-based PEA method, CTpathway, based on a global pathway crosstalk map (GPCM) with >440,000 edges by combing pathways from eight resources, transcription factor-gene regulations, and large-scale protein-protein interactions. Integrating gene differential expression and crosstalk effects in GPCM, we assign a risk score to genes in the GPCM and identify risk pathways enriched with the risk genes. RESULTS: Analysis of >8300 expression profiles covering ten cancer tissues and blood samples indicates that CTpathway outperforms the current state-of-the-art methods in identifying risk pathways with higher accuracy, reproducibility, and speed. CTpathway recapitulates known risk pathways and exclusively identifies several previously unreported critical pathways for individual cancer types. CTpathway also outperforms other methods in identifying risk pathways across all cancer stages, including early-stage cancer with a small number of differentially expressed genes. Moreover, the robust design of CTpathway enables researchers to analyze both bulk and single-cell RNA-seq profiles to predict both cancer tissue and cell type-specific risk pathways with higher accuracy. CONCLUSIONS: Collectively, CTpathway is a fast, accurate, and stable pathway enrichment analysis method for cancer research that can be used to identify cancer risk pathways. The CTpathway interactive web server can be accessed here http://www.jianglab.cn/CTpathway/. The stand-alone program can be accessed here https://github.com/Bioccjw/CTpathway. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13073-022-01119-6. |
format | Online Article Text |
id | pubmed-9563764 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-95637642022-10-15 CTpathway: a CrossTalk-based pathway enrichment analysis method for cancer research Liu, Haizhou Yuan, Mengqin Mitra, Ramkrishna Zhou, Xu Long, Min Lei, Wanyue Zhou, Shunheng Huang, Yu-e Hou, Fei Eischen, Christine M. Jiang, Wei Genome Med Research BACKGROUND: Pathway enrichment analysis (PEA) is a common method for exploring functions of hundreds of genes and identifying disease-risk pathways. Moreover, different pathways exert their functions through crosstalk. However, existing PEA methods do not sufficiently integrate essential pathway features, including pathway crosstalk, molecular interactions, and network topologies, resulting in many risk pathways that remain uninvestigated. METHODS: To overcome these limitations, we develop a new crosstalk-based PEA method, CTpathway, based on a global pathway crosstalk map (GPCM) with >440,000 edges by combing pathways from eight resources, transcription factor-gene regulations, and large-scale protein-protein interactions. Integrating gene differential expression and crosstalk effects in GPCM, we assign a risk score to genes in the GPCM and identify risk pathways enriched with the risk genes. RESULTS: Analysis of >8300 expression profiles covering ten cancer tissues and blood samples indicates that CTpathway outperforms the current state-of-the-art methods in identifying risk pathways with higher accuracy, reproducibility, and speed. CTpathway recapitulates known risk pathways and exclusively identifies several previously unreported critical pathways for individual cancer types. CTpathway also outperforms other methods in identifying risk pathways across all cancer stages, including early-stage cancer with a small number of differentially expressed genes. Moreover, the robust design of CTpathway enables researchers to analyze both bulk and single-cell RNA-seq profiles to predict both cancer tissue and cell type-specific risk pathways with higher accuracy. CONCLUSIONS: Collectively, CTpathway is a fast, accurate, and stable pathway enrichment analysis method for cancer research that can be used to identify cancer risk pathways. The CTpathway interactive web server can be accessed here http://www.jianglab.cn/CTpathway/. The stand-alone program can be accessed here https://github.com/Bioccjw/CTpathway. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13073-022-01119-6. BioMed Central 2022-10-13 /pmc/articles/PMC9563764/ /pubmed/36229842 http://dx.doi.org/10.1186/s13073-022-01119-6 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Liu, Haizhou Yuan, Mengqin Mitra, Ramkrishna Zhou, Xu Long, Min Lei, Wanyue Zhou, Shunheng Huang, Yu-e Hou, Fei Eischen, Christine M. Jiang, Wei CTpathway: a CrossTalk-based pathway enrichment analysis method for cancer research |
title | CTpathway: a CrossTalk-based pathway enrichment analysis method for cancer research |
title_full | CTpathway: a CrossTalk-based pathway enrichment analysis method for cancer research |
title_fullStr | CTpathway: a CrossTalk-based pathway enrichment analysis method for cancer research |
title_full_unstemmed | CTpathway: a CrossTalk-based pathway enrichment analysis method for cancer research |
title_short | CTpathway: a CrossTalk-based pathway enrichment analysis method for cancer research |
title_sort | ctpathway: a crosstalk-based pathway enrichment analysis method for cancer research |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9563764/ https://www.ncbi.nlm.nih.gov/pubmed/36229842 http://dx.doi.org/10.1186/s13073-022-01119-6 |
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