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Identification of non-cancer cells from cancer transcriptomic data()
Interactions between cancer cells and non-cancer cells composing the tumour microenvironment play a primary role in determining cancer progression and shaping the response to therapy. The qualitative and quantitative characterisation of the different cell populations in the tumour microenvironment i...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7346884/ https://www.ncbi.nlm.nih.gov/pubmed/31654804 http://dx.doi.org/10.1016/j.bbagrm.2019.194445 |
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author | Bortolomeazzi, Michele Keddar, Mohamed Reda Ciccarelli, Francesca D. Benedetti, Lorena |
author_facet | Bortolomeazzi, Michele Keddar, Mohamed Reda Ciccarelli, Francesca D. Benedetti, Lorena |
author_sort | Bortolomeazzi, Michele |
collection | PubMed |
description | Interactions between cancer cells and non-cancer cells composing the tumour microenvironment play a primary role in determining cancer progression and shaping the response to therapy. The qualitative and quantitative characterisation of the different cell populations in the tumour microenvironment is therefore crucial to understand its role in cancer. In recent years, many experimental and computational approaches have been developed to identify the cell populations composing heterogeneous tissue samples, such as cancer. In this review, we describe the state-of-the-art approaches for the quantification of non-cancer cells from bulk and single-cell cancer transcriptomic data, with a focus on immune cells. We illustrate the main features of these approaches and highlight their applications for the analysis of the tumour microenvironment in solid cancers. We also discuss techniques that are complementary and alternative to RNA sequencing, particularly focusing on approaches that can provide spatial information on the distribution of the cells within the tumour in addition to their qualitative and quantitative measurements. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Federico Manuel Giorgi and Dr. Shaun Mahony. |
format | Online Article Text |
id | pubmed-7346884 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-73468842020-07-14 Identification of non-cancer cells from cancer transcriptomic data() Bortolomeazzi, Michele Keddar, Mohamed Reda Ciccarelli, Francesca D. Benedetti, Lorena Biochim Biophys Acta Gene Regul Mech Article Interactions between cancer cells and non-cancer cells composing the tumour microenvironment play a primary role in determining cancer progression and shaping the response to therapy. The qualitative and quantitative characterisation of the different cell populations in the tumour microenvironment is therefore crucial to understand its role in cancer. In recent years, many experimental and computational approaches have been developed to identify the cell populations composing heterogeneous tissue samples, such as cancer. In this review, we describe the state-of-the-art approaches for the quantification of non-cancer cells from bulk and single-cell cancer transcriptomic data, with a focus on immune cells. We illustrate the main features of these approaches and highlight their applications for the analysis of the tumour microenvironment in solid cancers. We also discuss techniques that are complementary and alternative to RNA sequencing, particularly focusing on approaches that can provide spatial information on the distribution of the cells within the tumour in addition to their qualitative and quantitative measurements. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Federico Manuel Giorgi and Dr. Shaun Mahony. Elsevier 2020-06 /pmc/articles/PMC7346884/ /pubmed/31654804 http://dx.doi.org/10.1016/j.bbagrm.2019.194445 Text en © 2020 The Authors. Published by Elsevier B.V. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Article Bortolomeazzi, Michele Keddar, Mohamed Reda Ciccarelli, Francesca D. Benedetti, Lorena Identification of non-cancer cells from cancer transcriptomic data() |
title | Identification of non-cancer cells from cancer transcriptomic data() |
title_full | Identification of non-cancer cells from cancer transcriptomic data() |
title_fullStr | Identification of non-cancer cells from cancer transcriptomic data() |
title_full_unstemmed | Identification of non-cancer cells from cancer transcriptomic data() |
title_short | Identification of non-cancer cells from cancer transcriptomic data() |
title_sort | identification of non-cancer cells from cancer transcriptomic data() |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7346884/ https://www.ncbi.nlm.nih.gov/pubmed/31654804 http://dx.doi.org/10.1016/j.bbagrm.2019.194445 |
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