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MRI-based breast cancer radiogenomics using RNA profiling: association with subtypes in a single-center prospective study

BACKGROUND: There are few prospective studies on the correlations between MRI features and whole RNA-sequencing data in breast cancer according to molecular subtypes. The purpose of our study was to explore the association between genetic profiles and MRI phenotypes of breast cancer and to identify...

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Autores principales: Park, Ah Young, Han, Mi-Ryung, Seo, Bo Kyoung, Ju, Hye-Yeon, Son, Gil Soo, Lee, Hye Yoon, Chang, Young Woo, Choi, Jungyoon, Cho, Kyu Ran, Song, Sung Eun, Woo, Ok Hee, Park, Hyun Soo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10311893/
https://www.ncbi.nlm.nih.gov/pubmed/37391754
http://dx.doi.org/10.1186/s13058-023-01668-7
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author Park, Ah Young
Han, Mi-Ryung
Seo, Bo Kyoung
Ju, Hye-Yeon
Son, Gil Soo
Lee, Hye Yoon
Chang, Young Woo
Choi, Jungyoon
Cho, Kyu Ran
Song, Sung Eun
Woo, Ok Hee
Park, Hyun Soo
author_facet Park, Ah Young
Han, Mi-Ryung
Seo, Bo Kyoung
Ju, Hye-Yeon
Son, Gil Soo
Lee, Hye Yoon
Chang, Young Woo
Choi, Jungyoon
Cho, Kyu Ran
Song, Sung Eun
Woo, Ok Hee
Park, Hyun Soo
author_sort Park, Ah Young
collection PubMed
description BACKGROUND: There are few prospective studies on the correlations between MRI features and whole RNA-sequencing data in breast cancer according to molecular subtypes. The purpose of our study was to explore the association between genetic profiles and MRI phenotypes of breast cancer and to identify imaging markers that influences the prognosis and treatment according to subtypes. METHODS: From June 2017 to August 2018, MRIs of 95 women with invasive breast cancer were prospectively analyzed, using the breast imaging-reporting and data system and texture analysis. Whole RNA obtained from surgical specimens was analyzed using next-generation sequencing. The association between MRI features and gene expression profiles was analyzed in the entire tumor and subtypes. Gene networks, enriched functions, and canonical pathways were analyzed using Ingenuity Pathway Analysis. The P value for differential expression was obtained using a parametric F test comparing nested linear models and adjusted for multiple testing by reporting Q value. RESULTS: In 95 participants (mean age, 53 years ± 11 [standard deviation]), mass lesion type was associated with upregulation of CCL3L1 (sevenfold) and irregular mass shape was associated with downregulation of MIR421 (sixfold). In estrogen receptor-positive cancer with mass lesion type, CCL3L1 (21-fold), SNHG12 (11-fold), and MIR206 (sevenfold) were upregulated, and MIR597 (265-fold), MIR126 (12-fold), and SOX17 (fivefold) were downregulated. In triple-negative breast cancer with increased standard deviation of texture analysis on precontrast T1-weighted imaging, CLEC3A (23-fold), SRGN (13-fold), HSPG2 (sevenfold), KMT2D (fivefold), and VMP1 (fivefold) were upregulated, and IGLC2 (73-fold) and PRDX4 (sevenfold) were downregulated (all, P < 0.05 and Q < 0.1). Gene network and functional analysis showed that mass type estrogen receptor-positive cancers were associated with cell growth, anti-estrogen resistance, and poor survival. CONCLUSION: MRI characteristics are associated with the different expressions of genes related to metastasis, anti-drug resistance, and prognosis, depending on the molecular subtypes of breast cancer. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13058-023-01668-7.
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spelling pubmed-103118932023-07-01 MRI-based breast cancer radiogenomics using RNA profiling: association with subtypes in a single-center prospective study Park, Ah Young Han, Mi-Ryung Seo, Bo Kyoung Ju, Hye-Yeon Son, Gil Soo Lee, Hye Yoon Chang, Young Woo Choi, Jungyoon Cho, Kyu Ran Song, Sung Eun Woo, Ok Hee Park, Hyun Soo Breast Cancer Res Research BACKGROUND: There are few prospective studies on the correlations between MRI features and whole RNA-sequencing data in breast cancer according to molecular subtypes. The purpose of our study was to explore the association between genetic profiles and MRI phenotypes of breast cancer and to identify imaging markers that influences the prognosis and treatment according to subtypes. METHODS: From June 2017 to August 2018, MRIs of 95 women with invasive breast cancer were prospectively analyzed, using the breast imaging-reporting and data system and texture analysis. Whole RNA obtained from surgical specimens was analyzed using next-generation sequencing. The association between MRI features and gene expression profiles was analyzed in the entire tumor and subtypes. Gene networks, enriched functions, and canonical pathways were analyzed using Ingenuity Pathway Analysis. The P value for differential expression was obtained using a parametric F test comparing nested linear models and adjusted for multiple testing by reporting Q value. RESULTS: In 95 participants (mean age, 53 years ± 11 [standard deviation]), mass lesion type was associated with upregulation of CCL3L1 (sevenfold) and irregular mass shape was associated with downregulation of MIR421 (sixfold). In estrogen receptor-positive cancer with mass lesion type, CCL3L1 (21-fold), SNHG12 (11-fold), and MIR206 (sevenfold) were upregulated, and MIR597 (265-fold), MIR126 (12-fold), and SOX17 (fivefold) were downregulated. In triple-negative breast cancer with increased standard deviation of texture analysis on precontrast T1-weighted imaging, CLEC3A (23-fold), SRGN (13-fold), HSPG2 (sevenfold), KMT2D (fivefold), and VMP1 (fivefold) were upregulated, and IGLC2 (73-fold) and PRDX4 (sevenfold) were downregulated (all, P < 0.05 and Q < 0.1). Gene network and functional analysis showed that mass type estrogen receptor-positive cancers were associated with cell growth, anti-estrogen resistance, and poor survival. CONCLUSION: MRI characteristics are associated with the different expressions of genes related to metastasis, anti-drug resistance, and prognosis, depending on the molecular subtypes of breast cancer. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13058-023-01668-7. BioMed Central 2023-06-30 2023 /pmc/articles/PMC10311893/ /pubmed/37391754 http://dx.doi.org/10.1186/s13058-023-01668-7 Text en © The Author(s) 2023 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Park, Ah Young
Han, Mi-Ryung
Seo, Bo Kyoung
Ju, Hye-Yeon
Son, Gil Soo
Lee, Hye Yoon
Chang, Young Woo
Choi, Jungyoon
Cho, Kyu Ran
Song, Sung Eun
Woo, Ok Hee
Park, Hyun Soo
MRI-based breast cancer radiogenomics using RNA profiling: association with subtypes in a single-center prospective study
title MRI-based breast cancer radiogenomics using RNA profiling: association with subtypes in a single-center prospective study
title_full MRI-based breast cancer radiogenomics using RNA profiling: association with subtypes in a single-center prospective study
title_fullStr MRI-based breast cancer radiogenomics using RNA profiling: association with subtypes in a single-center prospective study
title_full_unstemmed MRI-based breast cancer radiogenomics using RNA profiling: association with subtypes in a single-center prospective study
title_short MRI-based breast cancer radiogenomics using RNA profiling: association with subtypes in a single-center prospective study
title_sort mri-based breast cancer radiogenomics using rna profiling: association with subtypes in a single-center prospective study
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10311893/
https://www.ncbi.nlm.nih.gov/pubmed/37391754
http://dx.doi.org/10.1186/s13058-023-01668-7
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