Radiogenomics analysis reveals the associations of dynamic contrast-enhanced–MRI features with gene expression characteristics, PAM50 subtypes, and prognosis of breast cancer
BackgroundTo investigate reliable associations between dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) features and gene expression characteristics in breast cancer (BC) and to develop and validate classifiers for predicting PAM50 subtypes and prognosis from DCE-MRI non-invasively.Met...
| Main Authors: | , , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-07-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2022.943326/full |
| _version_ | 1811312453291606016 |
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| author | Wenlong Ming Yanhui Zhu Yunfei Bai Wanjun Gu Wanjun Gu Fuyu Li Zixi Hu Tiansong Xia Zuolei Dai Xiafei Yu Huamei Li Yu Gu Shaoxun Yuan Rongxin Zhang Haitao Li Wenyong Zhu Jianing Ding Xiao Sun Yun Liu Hongde Liu Xiaoan Liu |
| author_facet | Wenlong Ming Yanhui Zhu Yunfei Bai Wanjun Gu Wanjun Gu Fuyu Li Zixi Hu Tiansong Xia Zuolei Dai Xiafei Yu Huamei Li Yu Gu Shaoxun Yuan Rongxin Zhang Haitao Li Wenyong Zhu Jianing Ding Xiao Sun Yun Liu Hongde Liu Xiaoan Liu |
| author_sort | Wenlong Ming |
| collection | DOAJ |
| description | BackgroundTo investigate reliable associations between dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) features and gene expression characteristics in breast cancer (BC) and to develop and validate classifiers for predicting PAM50 subtypes and prognosis from DCE-MRI non-invasively.MethodsTwo radiogenomics cohorts with paired DCE-MRI and RNA-sequencing (RNA-seq) data were collected from local and public databases and divided into discovery (n = 174) and validation cohorts (n = 72). Six external datasets (n = 1,443) were used for prognostic validation. Spatial–temporal features of DCE-MRI were extracted, normalized properly, and associated with gene expression to identify the imaging features that can indicate subtypes and prognosis.ResultsExpression of genes including RBP4, MYBL2, and LINC00993 correlated significantly with DCE-MRI features (q-value < 0.05). Importantly, genes in the cell cycle pathway exhibited a significant association with imaging features (p-value < 0.001). With eight imaging-associated genes (CHEK1, TTK, CDC45, BUB1B, PLK1, E2F1, CDC20, and CDC25A), we developed a radiogenomics prognostic signature that can distinguish BC outcomes in multiple datasets well. High expression of the signature indicated a poor prognosis (p-values < 0.01). Based on DCE-MRI features, we established classifiers to predict BC clinical receptors, PAM50 subtypes, and prognostic gene sets. The imaging-based machine learning classifiers performed well in the independent dataset (areas under the receiver operating characteristic curve (AUCs) of 0.8361, 0.809, 0.7742, and 0.7277 for estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2)-enriched, basal-like, and obtained radiogenomics signature). Furthermore, we developed a prognostic model directly using DCE-MRI features (p-value < 0.0001).ConclusionsOur results identified the DCE-MRI features that are robust and associated with the gene expression in BC and displayed the possibility of using the features to predict clinical receptors and PAM50 subtypes and to indicate BC prognosis. |
| first_indexed | 2024-04-13T10:37:38Z |
| format | Article |
| id | doaj.art-e68cc9c9fd5c4d02966355f557cd18bb |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-04-13T10:37:38Z |
| publishDate | 2022-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-e68cc9c9fd5c4d02966355f557cd18bb2022-12-22T02:50:01ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2022-07-011210.3389/fonc.2022.943326943326Radiogenomics analysis reveals the associations of dynamic contrast-enhanced–MRI features with gene expression characteristics, PAM50 subtypes, and prognosis of breast cancerWenlong Ming0Yanhui Zhu1Yunfei Bai2Wanjun Gu3Wanjun Gu4Fuyu Li5Zixi Hu6Tiansong Xia7Zuolei Dai8Xiafei Yu9Huamei Li10Yu Gu11Shaoxun Yuan12Rongxin Zhang13Haitao Li14Wenyong Zhu15Jianing Ding16Xiao Sun17Yun Liu18Hongde Liu19Xiaoan Liu20State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, ChinaDepartment of Breast Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, ChinaState Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, ChinaState Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, ChinaCollaborative Innovation Center of Jiangsu Province of Cancer Prevention and Treatment of Chinese Medicine, School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing, ChinaState Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, ChinaState Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, ChinaDepartment of Breast Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, ChinaDepartment of Information, The First Affiliated Hospital of Nanjing Medical University, Nanjing, ChinaDepartment of Breast Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, ChinaState Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, ChinaState Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, ChinaState Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, ChinaState Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, ChinaState Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, ChinaState Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, ChinaState Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, ChinaState Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, ChinaDepartment of Information, The First Affiliated Hospital of Nanjing Medical University, Nanjing, ChinaState Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, ChinaDepartment of Breast Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, ChinaBackgroundTo investigate reliable associations between dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) features and gene expression characteristics in breast cancer (BC) and to develop and validate classifiers for predicting PAM50 subtypes and prognosis from DCE-MRI non-invasively.MethodsTwo radiogenomics cohorts with paired DCE-MRI and RNA-sequencing (RNA-seq) data were collected from local and public databases and divided into discovery (n = 174) and validation cohorts (n = 72). Six external datasets (n = 1,443) were used for prognostic validation. Spatial–temporal features of DCE-MRI were extracted, normalized properly, and associated with gene expression to identify the imaging features that can indicate subtypes and prognosis.ResultsExpression of genes including RBP4, MYBL2, and LINC00993 correlated significantly with DCE-MRI features (q-value < 0.05). Importantly, genes in the cell cycle pathway exhibited a significant association with imaging features (p-value < 0.001). With eight imaging-associated genes (CHEK1, TTK, CDC45, BUB1B, PLK1, E2F1, CDC20, and CDC25A), we developed a radiogenomics prognostic signature that can distinguish BC outcomes in multiple datasets well. High expression of the signature indicated a poor prognosis (p-values < 0.01). Based on DCE-MRI features, we established classifiers to predict BC clinical receptors, PAM50 subtypes, and prognostic gene sets. The imaging-based machine learning classifiers performed well in the independent dataset (areas under the receiver operating characteristic curve (AUCs) of 0.8361, 0.809, 0.7742, and 0.7277 for estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2)-enriched, basal-like, and obtained radiogenomics signature). Furthermore, we developed a prognostic model directly using DCE-MRI features (p-value < 0.0001).ConclusionsOur results identified the DCE-MRI features that are robust and associated with the gene expression in BC and displayed the possibility of using the features to predict clinical receptors and PAM50 subtypes and to indicate BC prognosis.https://www.frontiersin.org/articles/10.3389/fonc.2022.943326/fullbreast cancerradiogenomicsradiomicsPAM50 subtypesDCE-MRImachine learning |
| spellingShingle | Wenlong Ming Yanhui Zhu Yunfei Bai Wanjun Gu Wanjun Gu Fuyu Li Zixi Hu Tiansong Xia Zuolei Dai Xiafei Yu Huamei Li Yu Gu Shaoxun Yuan Rongxin Zhang Haitao Li Wenyong Zhu Jianing Ding Xiao Sun Yun Liu Hongde Liu Xiaoan Liu Radiogenomics analysis reveals the associations of dynamic contrast-enhanced–MRI features with gene expression characteristics, PAM50 subtypes, and prognosis of breast cancer Frontiers in Oncology breast cancer radiogenomics radiomics PAM50 subtypes DCE-MRI machine learning |
| title | Radiogenomics analysis reveals the associations of dynamic contrast-enhanced–MRI features with gene expression characteristics, PAM50 subtypes, and prognosis of breast cancer |
| title_full | Radiogenomics analysis reveals the associations of dynamic contrast-enhanced–MRI features with gene expression characteristics, PAM50 subtypes, and prognosis of breast cancer |
| title_fullStr | Radiogenomics analysis reveals the associations of dynamic contrast-enhanced–MRI features with gene expression characteristics, PAM50 subtypes, and prognosis of breast cancer |
| title_full_unstemmed | Radiogenomics analysis reveals the associations of dynamic contrast-enhanced–MRI features with gene expression characteristics, PAM50 subtypes, and prognosis of breast cancer |
| title_short | Radiogenomics analysis reveals the associations of dynamic contrast-enhanced–MRI features with gene expression characteristics, PAM50 subtypes, and prognosis of breast cancer |
| title_sort | radiogenomics analysis reveals the associations of dynamic contrast enhanced mri features with gene expression characteristics pam50 subtypes and prognosis of breast cancer |
| topic | breast cancer radiogenomics radiomics PAM50 subtypes DCE-MRI machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2022.943326/full |
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