Radiogenomics Map Reveals the Landscape of m6A Methylation Modification Pattern in Bladder Cancer

We aimed to develop a noninvasive radiomics approach to reveal the m6A methylation status and predict survival outcomes and therapeutic responses in patients. A total of 25 m6A regulators were selected for further analysis, we confirmed that expression level and genomic mutations rate of m6A regulat...
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Gespeichert in:

Autor*in:	Fangdie Ye [verfasserIn] Yun Hu [verfasserIn] Jiahao Gao [verfasserIn] Yingchun Liang [verfasserIn] Yufei Liu [verfasserIn] Yuxi Ou [verfasserIn] Zhang Cheng [verfasserIn] Haowen Jiang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	m6A radiogenomics contrast-enhanced computed tomography immunotherapy mutation burden

Übergeordnetes Werk:	In: Frontiers in Immunology - Frontiers Media S.A., 2011, 12(2021)
Übergeordnetes Werk:	volume:12 ; year:2021

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fimmu.2021.722642

Katalog-ID:	DOAJ061285714

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520			\|a We aimed to develop a noninvasive radiomics approach to reveal the m6A methylation status and predict survival outcomes and therapeutic responses in patients. A total of 25 m6A regulators were selected for further analysis, we confirmed that expression level and genomic mutations rate of m6A regulators were significantly different between cancer and normal tissues. Besides, we constructed methylation modification models and explored the immune infiltration and biological pathway alteration among different models. The m6A subtypes identified in this study can effectively predict the clinical outcome of bladder cancer (including m6AClusters, geneClusters, and m6Ascore models). In addition, we observed that immune response markers such as PD1 and CTLA4 were significantly corelated with the m6Ascore. Subsequently, a total of 98 obtained digital images were processed to capture the image signature and construct image prediction models based on the m6Ascore classification using a radiomics algorithm. We constructed seven signature radiogenomics models to reveal the m6A methylation status, and the model achieved an area under curve (AUC) degree of 0.887 and 0.762 for the training and test datasets, respectively. The presented radiogenomics models, a noninvasive prediction approach that combined the radiomics signatures and genomics characteristics, displayed satisfactory effective performance for predicting survival outcomes and therapeutic responses of patients. In the future, more interdisciplinary fields concerning the combination of medicine and electronics remains to be explored.
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allfields	10.3389/fimmu.2021.722642 doi (DE-627)DOAJ061285714 (DE-599)DOAJ93011e1e04384c26b80d782f64d80491 DE-627 ger DE-627 rakwb eng RC581-607 Fangdie Ye verfasserin aut Radiogenomics Map Reveals the Landscape of m6A Methylation Modification Pattern in Bladder Cancer 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We aimed to develop a noninvasive radiomics approach to reveal the m6A methylation status and predict survival outcomes and therapeutic responses in patients. A total of 25 m6A regulators were selected for further analysis, we confirmed that expression level and genomic mutations rate of m6A regulators were significantly different between cancer and normal tissues. Besides, we constructed methylation modification models and explored the immune infiltration and biological pathway alteration among different models. The m6A subtypes identified in this study can effectively predict the clinical outcome of bladder cancer (including m6AClusters, geneClusters, and m6Ascore models). In addition, we observed that immune response markers such as PD1 and CTLA4 were significantly corelated with the m6Ascore. Subsequently, a total of 98 obtained digital images were processed to capture the image signature and construct image prediction models based on the m6Ascore classification using a radiomics algorithm. We constructed seven signature radiogenomics models to reveal the m6A methylation status, and the model achieved an area under curve (AUC) degree of 0.887 and 0.762 for the training and test datasets, respectively. The presented radiogenomics models, a noninvasive prediction approach that combined the radiomics signatures and genomics characteristics, displayed satisfactory effective performance for predicting survival outcomes and therapeutic responses of patients. In the future, more interdisciplinary fields concerning the combination of medicine and electronics remains to be explored. m6A radiogenomics contrast-enhanced computed tomography immunotherapy mutation burden Immunologic diseases. Allergy Fangdie Ye verfasserin aut Yun Hu verfasserin aut Yun Hu verfasserin aut Jiahao Gao verfasserin aut Yingchun Liang verfasserin aut Yingchun Liang verfasserin aut Yufei Liu verfasserin aut Yufei Liu verfasserin aut Yuxi Ou verfasserin aut Yuxi Ou verfasserin aut Zhang Cheng verfasserin aut Zhang Cheng verfasserin aut Haowen Jiang verfasserin aut Haowen Jiang verfasserin aut Haowen Jiang verfasserin aut In Frontiers in Immunology Frontiers Media S.A., 2011 12(2021) (DE-627)657998354 (DE-600)2606827-8 16643224 nnns volume:12 year:2021 https://doi.org/10.3389/fimmu.2021.722642 kostenfrei https://doaj.org/article/93011e1e04384c26b80d782f64d80491 kostenfrei https://www.frontiersin.org/articles/10.3389/fimmu.2021.722642/full kostenfrei https://doaj.org/toc/1664-3224 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2021
spelling	10.3389/fimmu.2021.722642 doi (DE-627)DOAJ061285714 (DE-599)DOAJ93011e1e04384c26b80d782f64d80491 DE-627 ger DE-627 rakwb eng RC581-607 Fangdie Ye verfasserin aut Radiogenomics Map Reveals the Landscape of m6A Methylation Modification Pattern in Bladder Cancer 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We aimed to develop a noninvasive radiomics approach to reveal the m6A methylation status and predict survival outcomes and therapeutic responses in patients. A total of 25 m6A regulators were selected for further analysis, we confirmed that expression level and genomic mutations rate of m6A regulators were significantly different between cancer and normal tissues. Besides, we constructed methylation modification models and explored the immune infiltration and biological pathway alteration among different models. The m6A subtypes identified in this study can effectively predict the clinical outcome of bladder cancer (including m6AClusters, geneClusters, and m6Ascore models). In addition, we observed that immune response markers such as PD1 and CTLA4 were significantly corelated with the m6Ascore. Subsequently, a total of 98 obtained digital images were processed to capture the image signature and construct image prediction models based on the m6Ascore classification using a radiomics algorithm. We constructed seven signature radiogenomics models to reveal the m6A methylation status, and the model achieved an area under curve (AUC) degree of 0.887 and 0.762 for the training and test datasets, respectively. The presented radiogenomics models, a noninvasive prediction approach that combined the radiomics signatures and genomics characteristics, displayed satisfactory effective performance for predicting survival outcomes and therapeutic responses of patients. In the future, more interdisciplinary fields concerning the combination of medicine and electronics remains to be explored. m6A radiogenomics contrast-enhanced computed tomography immunotherapy mutation burden Immunologic diseases. Allergy Fangdie Ye verfasserin aut Yun Hu verfasserin aut Yun Hu verfasserin aut Jiahao Gao verfasserin aut Yingchun Liang verfasserin aut Yingchun Liang verfasserin aut Yufei Liu verfasserin aut Yufei Liu verfasserin aut Yuxi Ou verfasserin aut Yuxi Ou verfasserin aut Zhang Cheng verfasserin aut Zhang Cheng verfasserin aut Haowen Jiang verfasserin aut Haowen Jiang verfasserin aut Haowen Jiang verfasserin aut In Frontiers in Immunology Frontiers Media S.A., 2011 12(2021) (DE-627)657998354 (DE-600)2606827-8 16643224 nnns volume:12 year:2021 https://doi.org/10.3389/fimmu.2021.722642 kostenfrei https://doaj.org/article/93011e1e04384c26b80d782f64d80491 kostenfrei https://www.frontiersin.org/articles/10.3389/fimmu.2021.722642/full kostenfrei https://doaj.org/toc/1664-3224 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2021
allfields_unstemmed	10.3389/fimmu.2021.722642 doi (DE-627)DOAJ061285714 (DE-599)DOAJ93011e1e04384c26b80d782f64d80491 DE-627 ger DE-627 rakwb eng RC581-607 Fangdie Ye verfasserin aut Radiogenomics Map Reveals the Landscape of m6A Methylation Modification Pattern in Bladder Cancer 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We aimed to develop a noninvasive radiomics approach to reveal the m6A methylation status and predict survival outcomes and therapeutic responses in patients. A total of 25 m6A regulators were selected for further analysis, we confirmed that expression level and genomic mutations rate of m6A regulators were significantly different between cancer and normal tissues. Besides, we constructed methylation modification models and explored the immune infiltration and biological pathway alteration among different models. The m6A subtypes identified in this study can effectively predict the clinical outcome of bladder cancer (including m6AClusters, geneClusters, and m6Ascore models). In addition, we observed that immune response markers such as PD1 and CTLA4 were significantly corelated with the m6Ascore. Subsequently, a total of 98 obtained digital images were processed to capture the image signature and construct image prediction models based on the m6Ascore classification using a radiomics algorithm. We constructed seven signature radiogenomics models to reveal the m6A methylation status, and the model achieved an area under curve (AUC) degree of 0.887 and 0.762 for the training and test datasets, respectively. The presented radiogenomics models, a noninvasive prediction approach that combined the radiomics signatures and genomics characteristics, displayed satisfactory effective performance for predicting survival outcomes and therapeutic responses of patients. In the future, more interdisciplinary fields concerning the combination of medicine and electronics remains to be explored. m6A radiogenomics contrast-enhanced computed tomography immunotherapy mutation burden Immunologic diseases. Allergy Fangdie Ye verfasserin aut Yun Hu verfasserin aut Yun Hu verfasserin aut Jiahao Gao verfasserin aut Yingchun Liang verfasserin aut Yingchun Liang verfasserin aut Yufei Liu verfasserin aut Yufei Liu verfasserin aut Yuxi Ou verfasserin aut Yuxi Ou verfasserin aut Zhang Cheng verfasserin aut Zhang Cheng verfasserin aut Haowen Jiang verfasserin aut Haowen Jiang verfasserin aut Haowen Jiang verfasserin aut In Frontiers in Immunology Frontiers Media S.A., 2011 12(2021) (DE-627)657998354 (DE-600)2606827-8 16643224 nnns volume:12 year:2021 https://doi.org/10.3389/fimmu.2021.722642 kostenfrei https://doaj.org/article/93011e1e04384c26b80d782f64d80491 kostenfrei https://www.frontiersin.org/articles/10.3389/fimmu.2021.722642/full kostenfrei https://doaj.org/toc/1664-3224 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2021
allfieldsGer	10.3389/fimmu.2021.722642 doi (DE-627)DOAJ061285714 (DE-599)DOAJ93011e1e04384c26b80d782f64d80491 DE-627 ger DE-627 rakwb eng RC581-607 Fangdie Ye verfasserin aut Radiogenomics Map Reveals the Landscape of m6A Methylation Modification Pattern in Bladder Cancer 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We aimed to develop a noninvasive radiomics approach to reveal the m6A methylation status and predict survival outcomes and therapeutic responses in patients. A total of 25 m6A regulators were selected for further analysis, we confirmed that expression level and genomic mutations rate of m6A regulators were significantly different between cancer and normal tissues. Besides, we constructed methylation modification models and explored the immune infiltration and biological pathway alteration among different models. The m6A subtypes identified in this study can effectively predict the clinical outcome of bladder cancer (including m6AClusters, geneClusters, and m6Ascore models). In addition, we observed that immune response markers such as PD1 and CTLA4 were significantly corelated with the m6Ascore. Subsequently, a total of 98 obtained digital images were processed to capture the image signature and construct image prediction models based on the m6Ascore classification using a radiomics algorithm. We constructed seven signature radiogenomics models to reveal the m6A methylation status, and the model achieved an area under curve (AUC) degree of 0.887 and 0.762 for the training and test datasets, respectively. The presented radiogenomics models, a noninvasive prediction approach that combined the radiomics signatures and genomics characteristics, displayed satisfactory effective performance for predicting survival outcomes and therapeutic responses of patients. In the future, more interdisciplinary fields concerning the combination of medicine and electronics remains to be explored. m6A radiogenomics contrast-enhanced computed tomography immunotherapy mutation burden Immunologic diseases. Allergy Fangdie Ye verfasserin aut Yun Hu verfasserin aut Yun Hu verfasserin aut Jiahao Gao verfasserin aut Yingchun Liang verfasserin aut Yingchun Liang verfasserin aut Yufei Liu verfasserin aut Yufei Liu verfasserin aut Yuxi Ou verfasserin aut Yuxi Ou verfasserin aut Zhang Cheng verfasserin aut Zhang Cheng verfasserin aut Haowen Jiang verfasserin aut Haowen Jiang verfasserin aut Haowen Jiang verfasserin aut In Frontiers in Immunology Frontiers Media S.A., 2011 12(2021) (DE-627)657998354 (DE-600)2606827-8 16643224 nnns volume:12 year:2021 https://doi.org/10.3389/fimmu.2021.722642 kostenfrei https://doaj.org/article/93011e1e04384c26b80d782f64d80491 kostenfrei https://www.frontiersin.org/articles/10.3389/fimmu.2021.722642/full kostenfrei https://doaj.org/toc/1664-3224 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2021
allfieldsSound	10.3389/fimmu.2021.722642 doi (DE-627)DOAJ061285714 (DE-599)DOAJ93011e1e04384c26b80d782f64d80491 DE-627 ger DE-627 rakwb eng RC581-607 Fangdie Ye verfasserin aut Radiogenomics Map Reveals the Landscape of m6A Methylation Modification Pattern in Bladder Cancer 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We aimed to develop a noninvasive radiomics approach to reveal the m6A methylation status and predict survival outcomes and therapeutic responses in patients. A total of 25 m6A regulators were selected for further analysis, we confirmed that expression level and genomic mutations rate of m6A regulators were significantly different between cancer and normal tissues. Besides, we constructed methylation modification models and explored the immune infiltration and biological pathway alteration among different models. The m6A subtypes identified in this study can effectively predict the clinical outcome of bladder cancer (including m6AClusters, geneClusters, and m6Ascore models). In addition, we observed that immune response markers such as PD1 and CTLA4 were significantly corelated with the m6Ascore. Subsequently, a total of 98 obtained digital images were processed to capture the image signature and construct image prediction models based on the m6Ascore classification using a radiomics algorithm. We constructed seven signature radiogenomics models to reveal the m6A methylation status, and the model achieved an area under curve (AUC) degree of 0.887 and 0.762 for the training and test datasets, respectively. The presented radiogenomics models, a noninvasive prediction approach that combined the radiomics signatures and genomics characteristics, displayed satisfactory effective performance for predicting survival outcomes and therapeutic responses of patients. In the future, more interdisciplinary fields concerning the combination of medicine and electronics remains to be explored. m6A radiogenomics contrast-enhanced computed tomography immunotherapy mutation burden Immunologic diseases. Allergy Fangdie Ye verfasserin aut Yun Hu verfasserin aut Yun Hu verfasserin aut Jiahao Gao verfasserin aut Yingchun Liang verfasserin aut Yingchun Liang verfasserin aut Yufei Liu verfasserin aut Yufei Liu verfasserin aut Yuxi Ou verfasserin aut Yuxi Ou verfasserin aut Zhang Cheng verfasserin aut Zhang Cheng verfasserin aut Haowen Jiang verfasserin aut Haowen Jiang verfasserin aut Haowen Jiang verfasserin aut In Frontiers in Immunology Frontiers Media S.A., 2011 12(2021) (DE-627)657998354 (DE-600)2606827-8 16643224 nnns volume:12 year:2021 https://doi.org/10.3389/fimmu.2021.722642 kostenfrei https://doaj.org/article/93011e1e04384c26b80d782f64d80491 kostenfrei https://www.frontiersin.org/articles/10.3389/fimmu.2021.722642/full kostenfrei https://doaj.org/toc/1664-3224 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2021
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callnumber-first	R - Medicine
author	Fangdie Ye
spellingShingle	Fangdie Ye misc RC581-607 misc m6A misc radiogenomics misc contrast-enhanced computed tomography misc immunotherapy misc mutation burden misc Immunologic diseases. Allergy Radiogenomics Map Reveals the Landscape of m6A Methylation Modification Pattern in Bladder Cancer
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topic_title	RC581-607 Radiogenomics Map Reveals the Landscape of m6A Methylation Modification Pattern in Bladder Cancer m6A radiogenomics contrast-enhanced computed tomography immunotherapy mutation burden
topic	misc RC581-607 misc m6A misc radiogenomics misc contrast-enhanced computed tomography misc immunotherapy misc mutation burden misc Immunologic diseases. Allergy
topic_unstemmed	misc RC581-607 misc m6A misc radiogenomics misc contrast-enhanced computed tomography misc immunotherapy misc mutation burden misc Immunologic diseases. Allergy
topic_browse	misc RC581-607 misc m6A misc radiogenomics misc contrast-enhanced computed tomography misc immunotherapy misc mutation burden misc Immunologic diseases. Allergy
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title	Radiogenomics Map Reveals the Landscape of m6A Methylation Modification Pattern in Bladder Cancer
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title_full	Radiogenomics Map Reveals the Landscape of m6A Methylation Modification Pattern in Bladder Cancer
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author_browse	Fangdie Ye Yun Hu Jiahao Gao Yingchun Liang Yufei Liu Yuxi Ou Zhang Cheng Haowen Jiang
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title_sort	radiogenomics map reveals the landscape of m6a methylation modification pattern in bladder cancer
callnumber	RC581-607
title_auth	Radiogenomics Map Reveals the Landscape of m6A Methylation Modification Pattern in Bladder Cancer
abstract	We aimed to develop a noninvasive radiomics approach to reveal the m6A methylation status and predict survival outcomes and therapeutic responses in patients. A total of 25 m6A regulators were selected for further analysis, we confirmed that expression level and genomic mutations rate of m6A regulators were significantly different between cancer and normal tissues. Besides, we constructed methylation modification models and explored the immune infiltration and biological pathway alteration among different models. The m6A subtypes identified in this study can effectively predict the clinical outcome of bladder cancer (including m6AClusters, geneClusters, and m6Ascore models). In addition, we observed that immune response markers such as PD1 and CTLA4 were significantly corelated with the m6Ascore. Subsequently, a total of 98 obtained digital images were processed to capture the image signature and construct image prediction models based on the m6Ascore classification using a radiomics algorithm. We constructed seven signature radiogenomics models to reveal the m6A methylation status, and the model achieved an area under curve (AUC) degree of 0.887 and 0.762 for the training and test datasets, respectively. The presented radiogenomics models, a noninvasive prediction approach that combined the radiomics signatures and genomics characteristics, displayed satisfactory effective performance for predicting survival outcomes and therapeutic responses of patients. In the future, more interdisciplinary fields concerning the combination of medicine and electronics remains to be explored.
abstractGer	We aimed to develop a noninvasive radiomics approach to reveal the m6A methylation status and predict survival outcomes and therapeutic responses in patients. A total of 25 m6A regulators were selected for further analysis, we confirmed that expression level and genomic mutations rate of m6A regulators were significantly different between cancer and normal tissues. Besides, we constructed methylation modification models and explored the immune infiltration and biological pathway alteration among different models. The m6A subtypes identified in this study can effectively predict the clinical outcome of bladder cancer (including m6AClusters, geneClusters, and m6Ascore models). In addition, we observed that immune response markers such as PD1 and CTLA4 were significantly corelated with the m6Ascore. Subsequently, a total of 98 obtained digital images were processed to capture the image signature and construct image prediction models based on the m6Ascore classification using a radiomics algorithm. We constructed seven signature radiogenomics models to reveal the m6A methylation status, and the model achieved an area under curve (AUC) degree of 0.887 and 0.762 for the training and test datasets, respectively. The presented radiogenomics models, a noninvasive prediction approach that combined the radiomics signatures and genomics characteristics, displayed satisfactory effective performance for predicting survival outcomes and therapeutic responses of patients. In the future, more interdisciplinary fields concerning the combination of medicine and electronics remains to be explored.
abstract_unstemmed	We aimed to develop a noninvasive radiomics approach to reveal the m6A methylation status and predict survival outcomes and therapeutic responses in patients. A total of 25 m6A regulators were selected for further analysis, we confirmed that expression level and genomic mutations rate of m6A regulators were significantly different between cancer and normal tissues. Besides, we constructed methylation modification models and explored the immune infiltration and biological pathway alteration among different models. The m6A subtypes identified in this study can effectively predict the clinical outcome of bladder cancer (including m6AClusters, geneClusters, and m6Ascore models). In addition, we observed that immune response markers such as PD1 and CTLA4 were significantly corelated with the m6Ascore. Subsequently, a total of 98 obtained digital images were processed to capture the image signature and construct image prediction models based on the m6Ascore classification using a radiomics algorithm. We constructed seven signature radiogenomics models to reveal the m6A methylation status, and the model achieved an area under curve (AUC) degree of 0.887 and 0.762 for the training and test datasets, respectively. The presented radiogenomics models, a noninvasive prediction approach that combined the radiomics signatures and genomics characteristics, displayed satisfactory effective performance for predicting survival outcomes and therapeutic responses of patients. In the future, more interdisciplinary fields concerning the combination of medicine and electronics remains to be explored.
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title_short	Radiogenomics Map Reveals the Landscape of m6A Methylation Modification Pattern in Bladder Cancer
url	https://doi.org/10.3389/fimmu.2021.722642 https://doaj.org/article/93011e1e04384c26b80d782f64d80491 https://www.frontiersin.org/articles/10.3389/fimmu.2021.722642/full https://doaj.org/toc/1664-3224
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author2	Fangdie Ye Yun Hu Jiahao Gao Yingchun Liang Yufei Liu Yuxi Ou Zhang Cheng Haowen Jiang
author2Str	Fangdie Ye Yun Hu Jiahao Gao Yingchun Liang Yufei Liu Yuxi Ou Zhang Cheng Haowen Jiang
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up_date	2024-07-03T19:52:31.998Z
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We constructed seven signature radiogenomics models to reveal the m6A methylation status, and the model achieved an area under curve (AUC) degree of 0.887 and 0.762 for the training and test datasets, respectively. The presented radiogenomics models, a noninvasive prediction approach that combined the radiomics signatures and genomics characteristics, displayed satisfactory effective performance for predicting survival outcomes and therapeutic responses of patients. 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Radiogenomics Map Reveals the Landscape of m6A Methylation Modification Pattern in Bladder Cancer

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