Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Comparative Analysis on Molecular Characteristics of Chromophobe Renal Cancer and Oncocytoma

Version 1 : Received: 18 August 2021 / Approved: 20 August 2021 / Online: 20 August 2021 (11:23:43 CEST)

How to cite: Satter, K.B.; Tran, P.M.H.; Tran, L.K.H.; Bai, S.; Savage, N.M.; Kavuri, S.K.; Terris, M.; She, J.; Purohit, S. Comparative Analysis on Molecular Characteristics of Chromophobe Renal Cancer and Oncocytoma. Preprints 2021, 2021080409 (doi: 10.20944/preprints202108.0409.v1). Satter, K.B.; Tran, P.M.H.; Tran, L.K.H.; Bai, S.; Savage, N.M.; Kavuri, S.K.; Terris, M.; She, J.; Purohit, S. Comparative Analysis on Molecular Characteristics of Chromophobe Renal Cancer and Oncocytoma. Preprints 2021, 2021080409 (doi: 10.20944/preprints202108.0409.v1).

Abstract

Chromophobe renal cell carcinoma (chRCC) and oncocytoma (RO) are renal tumor types originating from alpha intercalated cells of the collecting ducts of the kidney. Both tumor types have similar gross histological morphology and increased mitochondria, which leads to difficulties differentiating between these tumors, especially with core biopsy samples. This study aims to apply a machine learning approach to develop a molecular classifier based on transcriptomics data. Here we generated a meta-data set containing 62 chRCC and 45 RO gene expression arrays. Arrays were subjected to quality control steps, and genes were selected based on differential expression and ROC analysis. The final gene list was evaluated with UMAP based dimension reduction followed by density-based clustering with 95.5% accuracy. Molecular profiling by KEGG pathway analysis identified enrichment of fatty acid oxidation pathway in RO. We finally identified and validated the 30-gene signature, with an accuracy of 94.4% to distinguish chRCC from RO on UMAP analysis. Our results show that chRCC and RO have a distinct gene signature that can differentiate these tumors and complement histology for routine diagnosis of these two tumors.

Keywords

Renal cancers; oncocytoma; chromophobe; transcriptomics; machine learning; clustering; gene signature; unsupervised learning

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