One-class Differential Expression Analysis using Tensor Decomposition-based Unsupervised Feature Extraction Applied to Integrated Analysis of Multiple Omics Data from 26 Lung Adenocarcinoma Cell Lines

Abstract

Because usually there are no normal control cell lines, cancer cell lines can be examined only in a comparison between treatment and no-treatment conditions. Thus, characterization of cancer cell lines by themselves is impossible. To address this problem, one-class differential expression (DE) analysis, which can evaluate samples without a reference, is proposed here using tensor decomposition (TD)-based unsupervised feature extraction (FE) extended from recently proposed principal component analysis-based unsupervised FE. This one-class DE analysis was applied to multi-omics datasets of 26 lung adenocarcinoma cell lines. Enrichment analysis of selected genes identified multiple biological terms or concepts including signal recognition particles and nonsense-mediated decay (Reactome, Gene Ontology [GO] biological process), cadherin, poly(A) RNA binding (GO molecular function), eukaryotic translation initiation factors (Reactome), aberrant histone protein expression (Reactome and Human Protein Atlas [HPA]), and 163 transcription factors including E2F, PAX5, ARNT, AHR, and CREB, all of which are known to be related to non-small cell lung cancer and are expected to function cooperatively in lung adenocarcinoma oncogenesis. ,,,, These data not only indicate usefulness of one-class DE analysis using TD-based unsupervised FE but also point to new therapeutic targets in lung adenocarcinoma.