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Identification of key somatic oncogenic mutation based on a confounder-free causal inference model
Abnormal cell proliferation and epithelial-mesenchymal transition (EMT) are the essential events that induce cancer initiation and progression. A fundamental goal in cancer research is to develop an efficient method to detect mutational genes capable of driving cancer. Although several computational...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9499235/ https://www.ncbi.nlm.nih.gov/pubmed/36137089 http://dx.doi.org/10.1371/journal.pcbi.1010529 |
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author | Liu, Yijun Sun, Ji Sun, Huiyan Chang, Yi |
author_facet | Liu, Yijun Sun, Ji Sun, Huiyan Chang, Yi |
author_sort | Liu, Yijun |
collection | PubMed |
description | Abnormal cell proliferation and epithelial-mesenchymal transition (EMT) are the essential events that induce cancer initiation and progression. A fundamental goal in cancer research is to develop an efficient method to detect mutational genes capable of driving cancer. Although several computational methods have been proposed to identify these key mutations, many of them focus on the association between genetic mutations and functional changes in relevant biological processes, but not their real causality. Causal effect inference provides a way to estimate the real induce effect of a certain mutation on vital biological processes of cancer initiation and progression, through addressing the confounder bias due to neutral mutations and unobserved latent variables. In this study, integrating genomic and transcriptomic data, we construct a novel causal inference model based on a deep variational autoencoder to identify key oncogenic somatic mutations. Applied to 10 cancer types, our method quantifies the causal effect of genetic mutations on cell proliferation and EMT by reducing both observed and unobserved confounding biases. The experimental results indicate that genes with higher mutation frequency do not necessarily mean they are more potent in inducing cancer and promoting cancer development. Moreover, our study fills a gap in the use of machine learning for causal inference to identify oncogenic mutations. |
format | Online Article Text |
id | pubmed-9499235 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-94992352022-09-23 Identification of key somatic oncogenic mutation based on a confounder-free causal inference model Liu, Yijun Sun, Ji Sun, Huiyan Chang, Yi PLoS Comput Biol Research Article Abnormal cell proliferation and epithelial-mesenchymal transition (EMT) are the essential events that induce cancer initiation and progression. A fundamental goal in cancer research is to develop an efficient method to detect mutational genes capable of driving cancer. Although several computational methods have been proposed to identify these key mutations, many of them focus on the association between genetic mutations and functional changes in relevant biological processes, but not their real causality. Causal effect inference provides a way to estimate the real induce effect of a certain mutation on vital biological processes of cancer initiation and progression, through addressing the confounder bias due to neutral mutations and unobserved latent variables. In this study, integrating genomic and transcriptomic data, we construct a novel causal inference model based on a deep variational autoencoder to identify key oncogenic somatic mutations. Applied to 10 cancer types, our method quantifies the causal effect of genetic mutations on cell proliferation and EMT by reducing both observed and unobserved confounding biases. The experimental results indicate that genes with higher mutation frequency do not necessarily mean they are more potent in inducing cancer and promoting cancer development. Moreover, our study fills a gap in the use of machine learning for causal inference to identify oncogenic mutations. Public Library of Science 2022-09-22 /pmc/articles/PMC9499235/ /pubmed/36137089 http://dx.doi.org/10.1371/journal.pcbi.1010529 Text en © 2022 Liu et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Liu, Yijun Sun, Ji Sun, Huiyan Chang, Yi Identification of key somatic oncogenic mutation based on a confounder-free causal inference model |
title | Identification of key somatic oncogenic mutation based on a confounder-free causal inference model |
title_full | Identification of key somatic oncogenic mutation based on a confounder-free causal inference model |
title_fullStr | Identification of key somatic oncogenic mutation based on a confounder-free causal inference model |
title_full_unstemmed | Identification of key somatic oncogenic mutation based on a confounder-free causal inference model |
title_short | Identification of key somatic oncogenic mutation based on a confounder-free causal inference model |
title_sort | identification of key somatic oncogenic mutation based on a confounder-free causal inference model |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9499235/ https://www.ncbi.nlm.nih.gov/pubmed/36137089 http://dx.doi.org/10.1371/journal.pcbi.1010529 |
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