Oncocytoma-Related Gene Signature to Differentiate Chromophobe Renal Cancer and Oncocytoma Using Machine Learning

Khaled Bin Satter, Paul Minh Huy Tran, Lynn Kim Hoang Tran, Zach Ramsey, Katheine Pinkerton, Shan Bai, Natasha M. Savage, Sravan Kavuri, Martha K. Terris, Jin Xiong She, Sharad Purohit

Research output: Contribution to journalArticlepeer-review

Abstract

Publicly available gene expression datasets were analyzed to develop a chromophobe and oncocytoma related gene signature (COGS) to distinguish chRCC from RO. The datasets GSE11151, GSE19982, GSE2109, GSE8271 and GSE11024 were combined into a discovery dataset. The tran-scriptomic differences were identified with unsupervised learning in the discovery dataset (97.8% accuracy) with density based UMAP (DBU). The top 30 genes were identified by univariate gene expression analysis and ROC analysis, to create a gene signature called COGS. COGS, combined with DBU, was able to differentiate chRCC from RO in the discovery dataset with an accuracy of 97.8%. The classification accuracy of COGS was validated in an independent meta-dataset consisting of TCGA-KICH and GSE12090, where COGS could differentiate chRCC from RO with 100% accuracy. The differentially expressed genes were involved in carbohydrate metabolism, transcriptomic regulation by TP53, beta-catenin-dependent Wnt signaling, and cytokine (IL-4 and IL-13) signaling highly active in cancer cells. Using multiple datasets and machine learning, we constructed and validated COGS as a tool that can differentiate chRCC from RO and complement histology in routine clinical practice to distinguish these two tumors.

Original languageEnglish (US)
Article number287
JournalCells
Volume11
Issue number2
DOIs
StatePublished - Jan 1 2022
Externally publishedYes

Keywords

  • Classification
  • Gene signature
  • Machine learning
  • Oncocytoma
  • Transcriptomic
  • chromophobe

ASJC Scopus subject areas

  • Medicine(all)

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