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Estimation of tumor cell total mRNA expression in 15 cancer types predicts disease progression
Single-cell RNA sequencing studies have suggested that total mRNA content correlates with tumor phenotypes. Technical and analytical challenges, however, have so far impeded at-scale pan-cancer examination of total mRNA content. Here we present a method to quantify tumor-specific total mRNA expressi...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group US
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9646498/ https://www.ncbi.nlm.nih.gov/pubmed/35697807 http://dx.doi.org/10.1038/s41587-022-01342-x |
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author | Cao, Shaolong Wang, Jennifer R. Ji, Shuangxi Yang, Peng Dai, Yaoyi Guo, Shuai Montierth, Matthew D. Shen, John Paul Zhao, Xiao Chen, Jingxiao Lee, Jaewon James Guerrero, Paola A. Spetsieris, Nicholas Engedal, Nikolai Taavitsainen, Sinja Yu, Kaixian Livingstone, Julie Bhandari, Vinayak Hubert, Shawna M. Daw, Najat C. Futreal, P. Andrew Efstathiou, Eleni Lim, Bora Viale, Andrea Zhang, Jianjun Nykter, Matti Czerniak, Bogdan A. Brown, Powel H. Swanton, Charles Msaouel, Pavlos Maitra, Anirban Kopetz, Scott Campbell, Peter Speed, Terence P. Boutros, Paul C. Zhu, Hongtu Urbanucci, Alfonso Demeulemeester, Jonas Van Loo, Peter Wang, Wenyi |
author_facet | Cao, Shaolong Wang, Jennifer R. Ji, Shuangxi Yang, Peng Dai, Yaoyi Guo, Shuai Montierth, Matthew D. Shen, John Paul Zhao, Xiao Chen, Jingxiao Lee, Jaewon James Guerrero, Paola A. Spetsieris, Nicholas Engedal, Nikolai Taavitsainen, Sinja Yu, Kaixian Livingstone, Julie Bhandari, Vinayak Hubert, Shawna M. Daw, Najat C. Futreal, P. Andrew Efstathiou, Eleni Lim, Bora Viale, Andrea Zhang, Jianjun Nykter, Matti Czerniak, Bogdan A. Brown, Powel H. Swanton, Charles Msaouel, Pavlos Maitra, Anirban Kopetz, Scott Campbell, Peter Speed, Terence P. Boutros, Paul C. Zhu, Hongtu Urbanucci, Alfonso Demeulemeester, Jonas Van Loo, Peter Wang, Wenyi |
author_sort | Cao, Shaolong |
collection | PubMed |
description | Single-cell RNA sequencing studies have suggested that total mRNA content correlates with tumor phenotypes. Technical and analytical challenges, however, have so far impeded at-scale pan-cancer examination of total mRNA content. Here we present a method to quantify tumor-specific total mRNA expression (TmS) from bulk sequencing data, taking into account tumor transcript proportion, purity and ploidy, which are estimated through transcriptomic/genomic deconvolution. We estimate and validate TmS in 6,590 patient tumors across 15 cancer types, identifying significant inter-tumor variability. Across cancers, high TmS is associated with increased risk of disease progression and death. TmS is influenced by cancer-specific patterns of gene alteration and intra-tumor genetic heterogeneity as well as by pan-cancer trends in metabolic dysregulation. Taken together, our results indicate that measuring cell-type-specific total mRNA expression in tumor cells predicts tumor phenotypes and clinical outcomes. |
format | Online Article Text |
id | pubmed-9646498 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group US |
record_format | MEDLINE/PubMed |
spelling | pubmed-96464982022-11-15 Estimation of tumor cell total mRNA expression in 15 cancer types predicts disease progression Cao, Shaolong Wang, Jennifer R. Ji, Shuangxi Yang, Peng Dai, Yaoyi Guo, Shuai Montierth, Matthew D. Shen, John Paul Zhao, Xiao Chen, Jingxiao Lee, Jaewon James Guerrero, Paola A. Spetsieris, Nicholas Engedal, Nikolai Taavitsainen, Sinja Yu, Kaixian Livingstone, Julie Bhandari, Vinayak Hubert, Shawna M. Daw, Najat C. Futreal, P. Andrew Efstathiou, Eleni Lim, Bora Viale, Andrea Zhang, Jianjun Nykter, Matti Czerniak, Bogdan A. Brown, Powel H. Swanton, Charles Msaouel, Pavlos Maitra, Anirban Kopetz, Scott Campbell, Peter Speed, Terence P. Boutros, Paul C. Zhu, Hongtu Urbanucci, Alfonso Demeulemeester, Jonas Van Loo, Peter Wang, Wenyi Nat Biotechnol Article Single-cell RNA sequencing studies have suggested that total mRNA content correlates with tumor phenotypes. Technical and analytical challenges, however, have so far impeded at-scale pan-cancer examination of total mRNA content. Here we present a method to quantify tumor-specific total mRNA expression (TmS) from bulk sequencing data, taking into account tumor transcript proportion, purity and ploidy, which are estimated through transcriptomic/genomic deconvolution. We estimate and validate TmS in 6,590 patient tumors across 15 cancer types, identifying significant inter-tumor variability. Across cancers, high TmS is associated with increased risk of disease progression and death. TmS is influenced by cancer-specific patterns of gene alteration and intra-tumor genetic heterogeneity as well as by pan-cancer trends in metabolic dysregulation. Taken together, our results indicate that measuring cell-type-specific total mRNA expression in tumor cells predicts tumor phenotypes and clinical outcomes. Nature Publishing Group US 2022-06-13 2022 /pmc/articles/PMC9646498/ /pubmed/35697807 http://dx.doi.org/10.1038/s41587-022-01342-x Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Cao, Shaolong Wang, Jennifer R. Ji, Shuangxi Yang, Peng Dai, Yaoyi Guo, Shuai Montierth, Matthew D. Shen, John Paul Zhao, Xiao Chen, Jingxiao Lee, Jaewon James Guerrero, Paola A. Spetsieris, Nicholas Engedal, Nikolai Taavitsainen, Sinja Yu, Kaixian Livingstone, Julie Bhandari, Vinayak Hubert, Shawna M. Daw, Najat C. Futreal, P. Andrew Efstathiou, Eleni Lim, Bora Viale, Andrea Zhang, Jianjun Nykter, Matti Czerniak, Bogdan A. Brown, Powel H. Swanton, Charles Msaouel, Pavlos Maitra, Anirban Kopetz, Scott Campbell, Peter Speed, Terence P. Boutros, Paul C. Zhu, Hongtu Urbanucci, Alfonso Demeulemeester, Jonas Van Loo, Peter Wang, Wenyi Estimation of tumor cell total mRNA expression in 15 cancer types predicts disease progression |
title | Estimation of tumor cell total mRNA expression in 15 cancer types predicts disease progression |
title_full | Estimation of tumor cell total mRNA expression in 15 cancer types predicts disease progression |
title_fullStr | Estimation of tumor cell total mRNA expression in 15 cancer types predicts disease progression |
title_full_unstemmed | Estimation of tumor cell total mRNA expression in 15 cancer types predicts disease progression |
title_short | Estimation of tumor cell total mRNA expression in 15 cancer types predicts disease progression |
title_sort | estimation of tumor cell total mrna expression in 15 cancer types predicts disease progression |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9646498/ https://www.ncbi.nlm.nih.gov/pubmed/35697807 http://dx.doi.org/10.1038/s41587-022-01342-x |
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