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A deep learning and similarity-based hierarchical clustering approach for pathological stage prediction of papillary renal cell carcinoma

Papillary renal cell carcinoma (pRCC), which accounts for 10–15% of renal cell carcinomas, is the second most frequent renal cell carcinoma. pRCC patient classification is difficult because of disease heterogeneity, histologic subtypes, and variations in both disease progression and patient outcomes...

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Autores principales: Lee, Sugi, Jung, Jaeeun, Park, Ilkyu, Park, Kunhyang, Kim, Dae-Soo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Research Network of Computational and Structural Biotechnology 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7533347/
https://www.ncbi.nlm.nih.gov/pubmed/33033583
http://dx.doi.org/10.1016/j.csbj.2020.09.029
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author Lee, Sugi
Jung, Jaeeun
Park, Ilkyu
Park, Kunhyang
Kim, Dae-Soo
author_facet Lee, Sugi
Jung, Jaeeun
Park, Ilkyu
Park, Kunhyang
Kim, Dae-Soo
author_sort Lee, Sugi
collection PubMed
description Papillary renal cell carcinoma (pRCC), which accounts for 10–15% of renal cell carcinomas, is the second most frequent renal cell carcinoma. pRCC patient classification is difficult because of disease heterogeneity, histologic subtypes, and variations in both disease progression and patient outcomes. Nevertheless, symptom-based patient classification is indispensable in deciding treatment options. Here we introduce a prediction method for distinguishing pRCC pathological tumour stages using deep learning and similarity-based hierarchical clustering approaches. Differentially expressed genes (DEGs) were identified from gene expression data of pRCC patients retrieved from TCGA. Thirty-three of these genes were distinguished based on expression in early or late stage pRCC using the Wilcoxon rank sum test, confidence interval, and LASSO regression. Then, a deep learning model was constructed to predict tumour progression with an accuracy of 0.942 and area under curve of 0.933. Furthermore, pathological sub-stage information with an accuracy of 0.857 was obtained via similarity-based hierarchical clustering using 18 DEGs between stages I and II, and 11 DEGs between stages III and IV, identified through Wilcoxon rank sum test and quantile approach. Additionally, we offer this classification process as an R function. This is the first report of a model distinguishing the pathological tumour stages of pRCC using deep learning and similarity-based hierarchical clustering methods. Our findings are potentially applicable for improving early detection and treatment of pRCC and establishing a clearer classification of the pathological stages in other tumours.
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spelling pubmed-75333472020-10-07 A deep learning and similarity-based hierarchical clustering approach for pathological stage prediction of papillary renal cell carcinoma Lee, Sugi Jung, Jaeeun Park, Ilkyu Park, Kunhyang Kim, Dae-Soo Comput Struct Biotechnol J Research Article Papillary renal cell carcinoma (pRCC), which accounts for 10–15% of renal cell carcinomas, is the second most frequent renal cell carcinoma. pRCC patient classification is difficult because of disease heterogeneity, histologic subtypes, and variations in both disease progression and patient outcomes. Nevertheless, symptom-based patient classification is indispensable in deciding treatment options. Here we introduce a prediction method for distinguishing pRCC pathological tumour stages using deep learning and similarity-based hierarchical clustering approaches. Differentially expressed genes (DEGs) were identified from gene expression data of pRCC patients retrieved from TCGA. Thirty-three of these genes were distinguished based on expression in early or late stage pRCC using the Wilcoxon rank sum test, confidence interval, and LASSO regression. Then, a deep learning model was constructed to predict tumour progression with an accuracy of 0.942 and area under curve of 0.933. Furthermore, pathological sub-stage information with an accuracy of 0.857 was obtained via similarity-based hierarchical clustering using 18 DEGs between stages I and II, and 11 DEGs between stages III and IV, identified through Wilcoxon rank sum test and quantile approach. Additionally, we offer this classification process as an R function. This is the first report of a model distinguishing the pathological tumour stages of pRCC using deep learning and similarity-based hierarchical clustering methods. Our findings are potentially applicable for improving early detection and treatment of pRCC and establishing a clearer classification of the pathological stages in other tumours. Research Network of Computational and Structural Biotechnology 2020-09-24 /pmc/articles/PMC7533347/ /pubmed/33033583 http://dx.doi.org/10.1016/j.csbj.2020.09.029 Text en © 2020 The Author(s) http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Research Article
Lee, Sugi
Jung, Jaeeun
Park, Ilkyu
Park, Kunhyang
Kim, Dae-Soo
A deep learning and similarity-based hierarchical clustering approach for pathological stage prediction of papillary renal cell carcinoma
title A deep learning and similarity-based hierarchical clustering approach for pathological stage prediction of papillary renal cell carcinoma
title_full A deep learning and similarity-based hierarchical clustering approach for pathological stage prediction of papillary renal cell carcinoma
title_fullStr A deep learning and similarity-based hierarchical clustering approach for pathological stage prediction of papillary renal cell carcinoma
title_full_unstemmed A deep learning and similarity-based hierarchical clustering approach for pathological stage prediction of papillary renal cell carcinoma
title_short A deep learning and similarity-based hierarchical clustering approach for pathological stage prediction of papillary renal cell carcinoma
title_sort deep learning and similarity-based hierarchical clustering approach for pathological stage prediction of papillary renal cell carcinoma
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7533347/
https://www.ncbi.nlm.nih.gov/pubmed/33033583
http://dx.doi.org/10.1016/j.csbj.2020.09.029
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