Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Yu, Xiangtian, Pan, XiaoYong, Zhang, ShiQi, Zhang, Yu-Hang, Chen, Lei, Wan, Sibao, Huang, Tao, Cai, Yu-Dong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7876317/
https://www.ncbi.nlm.nih.gov/pubmed/33584803
http://dx.doi.org/10.3389/fgene.2020.605012
_version_ 1783649947595309056
author Yu, Xiangtian
Pan, XiaoYong
Zhang, ShiQi
Zhang, Yu-Hang
Chen, Lei
Wan, Sibao
Huang, Tao
Cai, Yu-Dong
author_facet Yu, Xiangtian
Pan, XiaoYong
Zhang, ShiQi
Zhang, Yu-Hang
Chen, Lei
Wan, Sibao
Huang, Tao
Cai, Yu-Dong
author_sort Yu, Xiangtian
collection PubMed
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.
format Online
Article
Text
id pubmed-7876317
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-78763172021-02-12 Identification of Gene Signatures and Expression Patterns During Epithelial-to-Mesenchymal Transition From Single-Cell Expression Atlas Yu, Xiangtian Pan, XiaoYong Zhang, ShiQi Zhang, Yu-Hang Chen, Lei Wan, Sibao Huang, Tao Cai, Yu-Dong Front Genet Genetics 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. Frontiers Media S.A. 2021-01-28 /pmc/articles/PMC7876317/ /pubmed/33584803 http://dx.doi.org/10.3389/fgene.2020.605012 Text en Copyright © 2021 Yu, Pan, Zhang, Zhang, Chen, Wan, Huang and Cai. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Yu, Xiangtian
Pan, XiaoYong
Zhang, ShiQi
Zhang, Yu-Hang
Chen, Lei
Wan, Sibao
Huang, Tao
Cai, Yu-Dong
Identification of Gene Signatures and Expression Patterns During Epithelial-to-Mesenchymal Transition From Single-Cell Expression Atlas
title 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_short 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
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7876317/
https://www.ncbi.nlm.nih.gov/pubmed/33584803
http://dx.doi.org/10.3389/fgene.2020.605012
work_keys_str_mv AT yuxiangtian identificationofgenesignaturesandexpressionpatternsduringepithelialtomesenchymaltransitionfromsinglecellexpressionatlas
AT panxiaoyong identificationofgenesignaturesandexpressionpatternsduringepithelialtomesenchymaltransitionfromsinglecellexpressionatlas
AT zhangshiqi identificationofgenesignaturesandexpressionpatternsduringepithelialtomesenchymaltransitionfromsinglecellexpressionatlas
AT zhangyuhang identificationofgenesignaturesandexpressionpatternsduringepithelialtomesenchymaltransitionfromsinglecellexpressionatlas
AT chenlei identificationofgenesignaturesandexpressionpatternsduringepithelialtomesenchymaltransitionfromsinglecellexpressionatlas
AT wansibao identificationofgenesignaturesandexpressionpatternsduringepithelialtomesenchymaltransitionfromsinglecellexpressionatlas
AT huangtao identificationofgenesignaturesandexpressionpatternsduringepithelialtomesenchymaltransitionfromsinglecellexpressionatlas
AT caiyudong identificationofgenesignaturesandexpressionpatternsduringepithelialtomesenchymaltransitionfromsinglecellexpressionatlas