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A Comprehensive Landscape of Imaging Feature-Associated RNA Expression Profiles in Human Breast Tissue
The expression abundance of transcripts in nondiseased breast tissue varies among individuals. The association study of genotypes and imaging phenotypes may help us to understand this individual variation. Since existing reports mainly focus on tumors or lesion areas, the heterogeneity of pathologic...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9921444/ https://www.ncbi.nlm.nih.gov/pubmed/36772473 http://dx.doi.org/10.3390/s23031432 |
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author | Mou, Tian Liang, Jianwen Vu, Trung Nghia Tian, Mu Gao, Yi |
author_facet | Mou, Tian Liang, Jianwen Vu, Trung Nghia Tian, Mu Gao, Yi |
author_sort | Mou, Tian |
collection | PubMed |
description | The expression abundance of transcripts in nondiseased breast tissue varies among individuals. The association study of genotypes and imaging phenotypes may help us to understand this individual variation. Since existing reports mainly focus on tumors or lesion areas, the heterogeneity of pathological image features and their correlations with RNA expression profiles for nondiseased tissue are not clear. The aim of this study is to discover the association between the nucleus features and the transcriptome-wide RNAs. We analyzed both microscopic histology images and RNA-sequencing data of 456 breast tissues from the Genotype-Tissue Expression (GTEx) project and constructed an automatic computational framework. We classified all samples into four clusters based on their nucleus morphological features and discovered feature-specific gene sets. The biological pathway analysis was performed on each gene set. The proposed framework evaluates the morphological characteristics of the cell nucleus quantitatively and identifies the associated genes. We found image features that capture population variation in breast tissue associated with RNA expressions, suggesting that the variation in expression pattern affects population variation in the morphological traits of breast tissue. This study provides a comprehensive transcriptome-wide view of imaging-feature-specific RNA expression for healthy breast tissue. Such a framework could also be used for understanding the connection between RNA expression and morphology in other tissues and organs. Pathway analysis indicated that the gene sets we identified were involved in specific biological processes, such as immune processes. |
format | Online Article Text |
id | pubmed-9921444 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-99214442023-02-12 A Comprehensive Landscape of Imaging Feature-Associated RNA Expression Profiles in Human Breast Tissue Mou, Tian Liang, Jianwen Vu, Trung Nghia Tian, Mu Gao, Yi Sensors (Basel) Article The expression abundance of transcripts in nondiseased breast tissue varies among individuals. The association study of genotypes and imaging phenotypes may help us to understand this individual variation. Since existing reports mainly focus on tumors or lesion areas, the heterogeneity of pathological image features and their correlations with RNA expression profiles for nondiseased tissue are not clear. The aim of this study is to discover the association between the nucleus features and the transcriptome-wide RNAs. We analyzed both microscopic histology images and RNA-sequencing data of 456 breast tissues from the Genotype-Tissue Expression (GTEx) project and constructed an automatic computational framework. We classified all samples into four clusters based on their nucleus morphological features and discovered feature-specific gene sets. The biological pathway analysis was performed on each gene set. The proposed framework evaluates the morphological characteristics of the cell nucleus quantitatively and identifies the associated genes. We found image features that capture population variation in breast tissue associated with RNA expressions, suggesting that the variation in expression pattern affects population variation in the morphological traits of breast tissue. This study provides a comprehensive transcriptome-wide view of imaging-feature-specific RNA expression for healthy breast tissue. Such a framework could also be used for understanding the connection between RNA expression and morphology in other tissues and organs. Pathway analysis indicated that the gene sets we identified were involved in specific biological processes, such as immune processes. MDPI 2023-01-28 /pmc/articles/PMC9921444/ /pubmed/36772473 http://dx.doi.org/10.3390/s23031432 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Mou, Tian Liang, Jianwen Vu, Trung Nghia Tian, Mu Gao, Yi A Comprehensive Landscape of Imaging Feature-Associated RNA Expression Profiles in Human Breast Tissue |
title | A Comprehensive Landscape of Imaging Feature-Associated RNA Expression Profiles in Human Breast Tissue |
title_full | A Comprehensive Landscape of Imaging Feature-Associated RNA Expression Profiles in Human Breast Tissue |
title_fullStr | A Comprehensive Landscape of Imaging Feature-Associated RNA Expression Profiles in Human Breast Tissue |
title_full_unstemmed | A Comprehensive Landscape of Imaging Feature-Associated RNA Expression Profiles in Human Breast Tissue |
title_short | A Comprehensive Landscape of Imaging Feature-Associated RNA Expression Profiles in Human Breast Tissue |
title_sort | comprehensive landscape of imaging feature-associated rna expression profiles in human breast tissue |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9921444/ https://www.ncbi.nlm.nih.gov/pubmed/36772473 http://dx.doi.org/10.3390/s23031432 |
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