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Radiogenomic analysis of prediction HER2 status in breast cancer by linking ultrasound radiomic feature module with biological functions

BACKGROUND: Human epidermal growth factor receptor 2 (HER2) overexpressed associated with poor prognosis in breast cancer and HER2 has been defined as a therapeutic target for breast cancer treatment. We aimed to explore the molecular biological information in ultrasound radiomic features (URFs) of...

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Autores principales: Cui, Hao, Sun, Yue, Zhao, Dantong, Zhang, Xudong, Kong, Hanqing, Hu, Nana, Wang, Panting, Zuo, Xiaoxuan, Fan, Wei, Yao, Yuan, Fu, Baiyang, Tian, Jiawei, Wu, Meixin, Gao, Yue, Ning, Shangwei, Zhang, Lei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9875533/
https://www.ncbi.nlm.nih.gov/pubmed/36694240
http://dx.doi.org/10.1186/s12967-022-03840-7
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author Cui, Hao
Sun, Yue
Zhao, Dantong
Zhang, Xudong
Kong, Hanqing
Hu, Nana
Wang, Panting
Zuo, Xiaoxuan
Fan, Wei
Yao, Yuan
Fu, Baiyang
Tian, Jiawei
Wu, Meixin
Gao, Yue
Ning, Shangwei
Zhang, Lei
author_facet Cui, Hao
Sun, Yue
Zhao, Dantong
Zhang, Xudong
Kong, Hanqing
Hu, Nana
Wang, Panting
Zuo, Xiaoxuan
Fan, Wei
Yao, Yuan
Fu, Baiyang
Tian, Jiawei
Wu, Meixin
Gao, Yue
Ning, Shangwei
Zhang, Lei
author_sort Cui, Hao
collection PubMed
description BACKGROUND: Human epidermal growth factor receptor 2 (HER2) overexpressed associated with poor prognosis in breast cancer and HER2 has been defined as a therapeutic target for breast cancer treatment. We aimed to explore the molecular biological information in ultrasound radiomic features (URFs) of HER2-positive breast cancer using radiogenomic analysis. Moreover, a radiomics model was developed to predict the status of HER2 in breast cancer. METHODS: This retrospective study included 489 patients who were diagnosed with breast cancer. URFs were extracted from a radiomics analysis set using PyRadiomics. The correlations between differential URFs and HER2-related genes were calculated using Pearson correlation analysis. Functional enrichment of the identified URFs-correlated HER2 positive-specific genes was performed. Lastly, the radiomics model was developed based on the URF-module mined from auxiliary differential URFs to assess the HER2 status of breast cancer. RESULTS: Eight differential URFs (p < 0.05) were identified among the 86 URFs extracted by Pyradiomics. 25 genes that were found to be the most closely associated with URFs. Then, the relevant biological functions of each differential URF were obtained through functional enrichment analysis. Among them, Zone Entropy is related to immune cell activity, which regulate the generation of calcification in breast cancer. The radiomics model based on the Logistic classifier and URF-module showed good discriminative ability (AUC = 0.80, 95% CI). CONCLUSION: We searched for the URFs of HER2-positive breast cancer, and explored the underlying genes and biological functions of these URFs. Furthermore, the radiomics model based on the Logistic classifier and URF-module relatively accurately predicted the HER2 status in breast cancer. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12967-022-03840-7.
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spelling pubmed-98755332023-01-26 Radiogenomic analysis of prediction HER2 status in breast cancer by linking ultrasound radiomic feature module with biological functions Cui, Hao Sun, Yue Zhao, Dantong Zhang, Xudong Kong, Hanqing Hu, Nana Wang, Panting Zuo, Xiaoxuan Fan, Wei Yao, Yuan Fu, Baiyang Tian, Jiawei Wu, Meixin Gao, Yue Ning, Shangwei Zhang, Lei J Transl Med Research BACKGROUND: Human epidermal growth factor receptor 2 (HER2) overexpressed associated with poor prognosis in breast cancer and HER2 has been defined as a therapeutic target for breast cancer treatment. We aimed to explore the molecular biological information in ultrasound radiomic features (URFs) of HER2-positive breast cancer using radiogenomic analysis. Moreover, a radiomics model was developed to predict the status of HER2 in breast cancer. METHODS: This retrospective study included 489 patients who were diagnosed with breast cancer. URFs were extracted from a radiomics analysis set using PyRadiomics. The correlations between differential URFs and HER2-related genes were calculated using Pearson correlation analysis. Functional enrichment of the identified URFs-correlated HER2 positive-specific genes was performed. Lastly, the radiomics model was developed based on the URF-module mined from auxiliary differential URFs to assess the HER2 status of breast cancer. RESULTS: Eight differential URFs (p < 0.05) were identified among the 86 URFs extracted by Pyradiomics. 25 genes that were found to be the most closely associated with URFs. Then, the relevant biological functions of each differential URF were obtained through functional enrichment analysis. Among them, Zone Entropy is related to immune cell activity, which regulate the generation of calcification in breast cancer. The radiomics model based on the Logistic classifier and URF-module showed good discriminative ability (AUC = 0.80, 95% CI). CONCLUSION: We searched for the URFs of HER2-positive breast cancer, and explored the underlying genes and biological functions of these URFs. Furthermore, the radiomics model based on the Logistic classifier and URF-module relatively accurately predicted the HER2 status in breast cancer. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12967-022-03840-7. BioMed Central 2023-01-24 /pmc/articles/PMC9875533/ /pubmed/36694240 http://dx.doi.org/10.1186/s12967-022-03840-7 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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
Cui, Hao
Sun, Yue
Zhao, Dantong
Zhang, Xudong
Kong, Hanqing
Hu, Nana
Wang, Panting
Zuo, Xiaoxuan
Fan, Wei
Yao, Yuan
Fu, Baiyang
Tian, Jiawei
Wu, Meixin
Gao, Yue
Ning, Shangwei
Zhang, Lei
Radiogenomic analysis of prediction HER2 status in breast cancer by linking ultrasound radiomic feature module with biological functions
title Radiogenomic analysis of prediction HER2 status in breast cancer by linking ultrasound radiomic feature module with biological functions
title_full Radiogenomic analysis of prediction HER2 status in breast cancer by linking ultrasound radiomic feature module with biological functions
title_fullStr Radiogenomic analysis of prediction HER2 status in breast cancer by linking ultrasound radiomic feature module with biological functions
title_full_unstemmed Radiogenomic analysis of prediction HER2 status in breast cancer by linking ultrasound radiomic feature module with biological functions
title_short Radiogenomic analysis of prediction HER2 status in breast cancer by linking ultrasound radiomic feature module with biological functions
title_sort radiogenomic analysis of prediction her2 status in breast cancer by linking ultrasound radiomic feature module with biological functions
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9875533/
https://www.ncbi.nlm.nih.gov/pubmed/36694240
http://dx.doi.org/10.1186/s12967-022-03840-7
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