Identification of Gene Signatures and Expression Patterns During Epithelial-to-Mesenchymal Transition From Single-Cell Expression Atlas
Cancer, which refers to abnormal cell proliferative diseases with systematic pathogenic potential, is one of the leading threats to human health. The final causes for patients’ deaths are usually cancer recurrence, metastasis, and drug resistance against continuing therapy. Epithelial-to-mesenchymal...
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doaj-32407cea8f64423fb2f0c15b52ddeee72021-01-28T08:15:53ZengFrontiers Media S.A.Frontiers in Genetics1664-80212021-01-011110.3389/fgene.2020.605012605012Identification of Gene Signatures and Expression Patterns During Epithelial-to-Mesenchymal Transition From Single-Cell Expression AtlasXiangtian Yu0XiaoYong Pan1ShiQi Zhang2Yu-Hang Zhang3Lei Chen4Lei Chen5Sibao Wan6Tao Huang7Yu-Dong Cai8Clinical Research Center, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, ChinaKey Laboratory of System Control and Information Processing, Ministry of Education of China, Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, ChinaDepartment of Biostatistics, University of Copenhagen, Copenhagen, DenmarkCAS Key Laboratory of Computational Biology, Bio-Med Big Data Center, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, ChinaCollege of Information Engineering, Shanghai Maritime University, Shanghai, ChinaShanghai Key Laboratory of PMMP, East China Normal University, Shanghai, ChinaSchool of Life Sciences, Shanghai University, Shanghai, ChinaCAS Key Laboratory of Computational Biology, Bio-Med Big Data Center, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, ChinaSchool of Life Sciences, Shanghai University, Shanghai, ChinaCancer, which refers to abnormal cell proliferative diseases with systematic pathogenic potential, is one of the leading threats to human health. The final causes for patients’ deaths are usually cancer recurrence, metastasis, and drug resistance against continuing therapy. Epithelial-to-mesenchymal transition (EMT), which is the transformation of tumor cells (TCs), is a prerequisite for pathogenic cancer recurrence, metastasis, and drug resistance. Conventional biomarkers can only define and recognize large tissues with obvious EMT markers but cannot accurately monitor detailed EMT processes. In this study, a systematic workflow was established integrating effective feature selection, multiple machine learning models [Random forest (RF), Support vector machine (SVM)], rule learning, and functional enrichment analyses to find new biomarkers and their functional implications for distinguishing single-cell isolated TCs with unique epithelial or mesenchymal markers using public single-cell expression profiling. Our discovered signatures may provide an effective and precise transcriptomic reference to monitor EMT progression at the single-cell level and contribute to the exploration of detailed tumorigenesis mechanisms during EMT.https://www.frontiersin.org/articles/10.3389/fgene.2020.605012/fullgene signatureexpression patternepithelial-to-mesenchymal transitionsingle cellclassification |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xiangtian Yu XiaoYong Pan ShiQi Zhang Yu-Hang Zhang Lei Chen Lei Chen Sibao Wan Tao Huang Yu-Dong Cai |
spellingShingle |
Xiangtian Yu XiaoYong Pan ShiQi Zhang Yu-Hang Zhang Lei Chen Lei Chen Sibao Wan Tao Huang Yu-Dong Cai Identification of Gene Signatures and Expression Patterns During Epithelial-to-Mesenchymal Transition From Single-Cell Expression Atlas Frontiers in Genetics gene signature expression pattern epithelial-to-mesenchymal transition single cell classification |
author_facet |
Xiangtian Yu XiaoYong Pan ShiQi Zhang Yu-Hang Zhang Lei Chen Lei Chen Sibao Wan Tao Huang Yu-Dong Cai |
author_sort |
Xiangtian Yu |
title |
Identification of Gene Signatures and Expression Patterns During Epithelial-to-Mesenchymal Transition From Single-Cell Expression Atlas |
title_short |
Identification of Gene Signatures and Expression Patterns During Epithelial-to-Mesenchymal Transition From Single-Cell Expression Atlas |
title_full |
Identification of Gene Signatures and Expression Patterns During Epithelial-to-Mesenchymal Transition From Single-Cell Expression Atlas |
title_fullStr |
Identification of Gene Signatures and Expression Patterns During Epithelial-to-Mesenchymal Transition From Single-Cell Expression Atlas |
title_full_unstemmed |
Identification of Gene Signatures and Expression Patterns During Epithelial-to-Mesenchymal Transition From Single-Cell Expression Atlas |
title_sort |
identification of gene signatures and expression patterns during epithelial-to-mesenchymal transition from single-cell expression atlas |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Genetics |
issn |
1664-8021 |
publishDate |
2021-01-01 |
description |
Cancer, which refers to abnormal cell proliferative diseases with systematic pathogenic potential, is one of the leading threats to human health. The final causes for patients’ deaths are usually cancer recurrence, metastasis, and drug resistance against continuing therapy. Epithelial-to-mesenchymal transition (EMT), which is the transformation of tumor cells (TCs), is a prerequisite for pathogenic cancer recurrence, metastasis, and drug resistance. Conventional biomarkers can only define and recognize large tissues with obvious EMT markers but cannot accurately monitor detailed EMT processes. In this study, a systematic workflow was established integrating effective feature selection, multiple machine learning models [Random forest (RF), Support vector machine (SVM)], rule learning, and functional enrichment analyses to find new biomarkers and their functional implications for distinguishing single-cell isolated TCs with unique epithelial or mesenchymal markers using public single-cell expression profiling. Our discovered signatures may provide an effective and precise transcriptomic reference to monitor EMT progression at the single-cell level and contribute to the exploration of detailed tumorigenesis mechanisms during EMT. |
topic |
gene signature expression pattern epithelial-to-mesenchymal transition single cell classification |
url |
https://www.frontiersin.org/articles/10.3389/fgene.2020.605012/full |
work_keys_str_mv |
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