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Machine Learning Identifies Pan-Cancer Landscape of Nrf2 Oxidative Stress Response Pathway-Related Genes
BACKGROUND: Oxidative stress produced a large amount of reactive oxygen species (ROS), which played a pivotal role in balanced ability and determining cell fate. The activated Nrf2 signaling pathway that responds to the excessive ROS regulated the expressions of antiapoptotic proteins, antioxidative...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8886747/ https://www.ncbi.nlm.nih.gov/pubmed/35242279 http://dx.doi.org/10.1155/2022/8450087 |
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author | Li, Na Zhan, Xianquan |
author_facet | Li, Na Zhan, Xianquan |
author_sort | Li, Na |
collection | PubMed |
description | BACKGROUND: Oxidative stress produced a large amount of reactive oxygen species (ROS), which played a pivotal role in balanced ability and determining cell fate. The activated Nrf2 signaling pathway that responds to the excessive ROS regulated the expressions of antiapoptotic proteins, antioxidative enzymes, drug transporters, and detoxifying factors. METHODS: The Nrf2 signaling pathway-related genes that had a direct relationship with Nrf2, including ATF4, BACH1, CREBBP, CUL3, EIF2AK3, EP300, FOS, FOSL1, GSK3B, JUN, KEAP1, MAF, MAFF, MAFG, MAFK, MAPK1, MAPK3, MAPK7, MAPK8, MAPK9, PIK3CA, PRRT2, and RIT1, were selected to do a systematic pan-cancer analysis. The relationship of Nrf2 signaling pathway-related gene expressions with tumor mutation burden, microsatellite status, clinical characteristics, immune system, cancer stemness index, and drug sensitivity was calculated by the Spearson correlation analysis across 11,057 subjects representing 33 cancer types. The prognosis models in lung squamous carcinoma, breast cancer, and stomach cancer were constructed with the Cox multivariate regression analysis and least absolute shrinkage and selection operator (Lasso) regression. RESULTS: Many Nrf2 signaling pathway-related genes were differently expressed between tumor and normal tissues. PIK3CA showed high mutation rate in pan-cancer. The expressions of Nrf2 signaling pathway-related genes were significantly related to tumor mutation burden, copy number variant, microsatellite instability score, survival rate, pathological stage, immune phenotype, immune score, immune cell, cancer stemness index, and drug sensitivity. The prognosis models were significantly associated with survival rate in lung squamous carcinoma, breast cancer, and stomach cancer; and the prognosis model-based riskscore was significantly associated with clinicopathological characteristics of each cancer. CONCLUSIONS: The study provided a comprehensive pan-cancer landscape of Nrf2 pathway-related genes. Based on the same Nrf2 pathway-related genes, the different prognosis models were constructed for different types of cancers. |
format | Online Article Text |
id | pubmed-8886747 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-88867472022-03-02 Machine Learning Identifies Pan-Cancer Landscape of Nrf2 Oxidative Stress Response Pathway-Related Genes Li, Na Zhan, Xianquan Oxid Med Cell Longev Research Article BACKGROUND: Oxidative stress produced a large amount of reactive oxygen species (ROS), which played a pivotal role in balanced ability and determining cell fate. The activated Nrf2 signaling pathway that responds to the excessive ROS regulated the expressions of antiapoptotic proteins, antioxidative enzymes, drug transporters, and detoxifying factors. METHODS: The Nrf2 signaling pathway-related genes that had a direct relationship with Nrf2, including ATF4, BACH1, CREBBP, CUL3, EIF2AK3, EP300, FOS, FOSL1, GSK3B, JUN, KEAP1, MAF, MAFF, MAFG, MAFK, MAPK1, MAPK3, MAPK7, MAPK8, MAPK9, PIK3CA, PRRT2, and RIT1, were selected to do a systematic pan-cancer analysis. The relationship of Nrf2 signaling pathway-related gene expressions with tumor mutation burden, microsatellite status, clinical characteristics, immune system, cancer stemness index, and drug sensitivity was calculated by the Spearson correlation analysis across 11,057 subjects representing 33 cancer types. The prognosis models in lung squamous carcinoma, breast cancer, and stomach cancer were constructed with the Cox multivariate regression analysis and least absolute shrinkage and selection operator (Lasso) regression. RESULTS: Many Nrf2 signaling pathway-related genes were differently expressed between tumor and normal tissues. PIK3CA showed high mutation rate in pan-cancer. The expressions of Nrf2 signaling pathway-related genes were significantly related to tumor mutation burden, copy number variant, microsatellite instability score, survival rate, pathological stage, immune phenotype, immune score, immune cell, cancer stemness index, and drug sensitivity. The prognosis models were significantly associated with survival rate in lung squamous carcinoma, breast cancer, and stomach cancer; and the prognosis model-based riskscore was significantly associated with clinicopathological characteristics of each cancer. CONCLUSIONS: The study provided a comprehensive pan-cancer landscape of Nrf2 pathway-related genes. Based on the same Nrf2 pathway-related genes, the different prognosis models were constructed for different types of cancers. Hindawi 2022-02-17 /pmc/articles/PMC8886747/ /pubmed/35242279 http://dx.doi.org/10.1155/2022/8450087 Text en Copyright © 2022 Na Li and Xianquan Zhan. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Li, Na Zhan, Xianquan Machine Learning Identifies Pan-Cancer Landscape of Nrf2 Oxidative Stress Response Pathway-Related Genes |
title | Machine Learning Identifies Pan-Cancer Landscape of Nrf2 Oxidative Stress Response Pathway-Related Genes |
title_full | Machine Learning Identifies Pan-Cancer Landscape of Nrf2 Oxidative Stress Response Pathway-Related Genes |
title_fullStr | Machine Learning Identifies Pan-Cancer Landscape of Nrf2 Oxidative Stress Response Pathway-Related Genes |
title_full_unstemmed | Machine Learning Identifies Pan-Cancer Landscape of Nrf2 Oxidative Stress Response Pathway-Related Genes |
title_short | Machine Learning Identifies Pan-Cancer Landscape of Nrf2 Oxidative Stress Response Pathway-Related Genes |
title_sort | machine learning identifies pan-cancer landscape of nrf2 oxidative stress response pathway-related genes |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8886747/ https://www.ncbi.nlm.nih.gov/pubmed/35242279 http://dx.doi.org/10.1155/2022/8450087 |
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