IMR Press / FBL / Volume 27 / Issue 4 / DOI: 10.31083/j.fbl2704127
Open Access Original Research
Detection of Biomarkers for Epithelial-Mesenchymal Transition with Single-Cell Trajectory Inference
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1 Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 565-0871 Suita, Japan
*Correspondence: matsuda@ist.osaka-u.ac.jp (Hideo Matsuda)
Academic Editors: Tatsuya Akutsu and TSUI Kwok Wing Stephen
Front. Biosci. (Landmark Ed) 2022, 27(4), 127; https://doi.org/10.31083/j.fbl2704127
Submitted: 5 March 2022 | Revised: 31 March 2022 | Accepted: 7 April 2022 | Published: 15 April 2022
Copyright: © 2022 The Author(s). Published by IMR Press.
This is an open access article under the CC BY 4.0 license.
Abstract

Background: Epithelial-mesenchymal transition (EMT) has been recognized as playing a crucial role in cancer progression. Among the studies on EMT, biomarker detection has been one of the important topics to understand the biology and mechanism of EMT related to tumor progression and treatment resistance. The existing methods often identified differentially-expressed genes as potential markers by ranking all genes by their variances. This paper proposes a novel method to detect markers for respective lineages in the EMT process. Methods and Results: Our method consists of three steps: first, perform trajectory inference to identify the lineage of transitional processes in EMT progression, and secondly, identify the lineage for EMT reversion in addition to EMT progression, and thirdly detect biomarkers for both of the EMT progression and reversion lineages with differential expression analysis. Furthermore, to elucidate the heterogeneity of the EMT process, we performed a clustering analysis of the cells in the EMT progression and reversion conditions. We then explored branching trajectories that order clusters using time information of the time-course samples. Using this method, we successfully detected two potential biomarkers related to EMT, phospholipid phosphatase 4 (PLPP4) and lymphotoxin-beta (LTB), which have not been detected by the existing method. Conclusions: In this study, we propose a method for the detection of biomarkers of EMT based on trajectory inference with single-cell RNA-seq data. The performance of the method is demonstrated by the detection of potential biomarkers related to EMT.

Keywords
pseudotime analysis
trajectory inference
single-cell RNA-seq
epithelial-mesenchymal transition
Figures
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