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

ABSTRACT: 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 r

SUBMITTER: Lee S 

PROVIDER: S-EPMC7533347 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature