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

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dc.contributor.authorSugi Lee-
dc.contributor.authorJaeeun Jung-
dc.contributor.authorIlkyu Park-
dc.contributor.authorKunhyang Park-
dc.contributor.authorDae Soo Kim-
dc.date.accessioned2020-10-27T03:26:54Z-
dc.date.available2020-10-27T03:26:54Z-
dc.date.issued2020-
dc.identifier.issn20010370-
dc.identifier.urihttps://oak.kribb.re.kr/handle/201005/23009-
dc.description.abstractPapillary 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.-
dc.titleA deep learning and similarity-based hierarchical clustering approach for pathological stage prediction of papillary renal cell carcinoma-
dc.title.alternativeA deep learning and similarity-based hierarchical clustering approach for pathological stage prediction of papillary renal cell carcinoma-
dc.typeArticle-
dc.citation.titleComputational and Structural Biotechnology Journal-
dc.citation.number0-
dc.citation.endPage2646-
dc.citation.startPage2639-
dc.citation.volume18-
dc.contributor.affiliatedAuthorKunhyang Park-
dc.contributor.affiliatedAuthorDae Soo Kim-
dc.contributor.alternativeName이수기-
dc.contributor.alternativeName정재은-
dc.contributor.alternativeName박일규-
dc.contributor.alternativeName박근향-
dc.contributor.alternativeName김대수-
dc.identifier.bibliographicCitationComputational and Structural Biotechnology Journal, vol. 18, pp. 2639-2646-
dc.identifier.doi10.1016/j.csbj.2020.09.029-
dc.subject.keywordDeep learning-
dc.subject.keywordPapillary renal cell carcinoma-
dc.subject.keywordPathological tumour stage-
dc.subject.keywordSimilarity-based hierarchical clustering-
dc.subject.localDeep learning-
dc.subject.localPapillary renal cell carcinoma-
dc.subject.localPathological tumour stage-
dc.subject.localSimilarity-based hierarchical clustering-
dc.description.journalClassY-
Appears in Collections:
Division of Bio Technology Innovation > Core Facility Management Center > 1. Journal Articles
Division of Research on National Challenges > Environmental diseases research center > 1. Journal Articles
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