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Identification of Master Regulators Driving Disease Progression, Relapse, and Drug Resistance in Lung Adenocarcinoma

Backgrounds: Lung cancer is the leading cause of cancer related death worldwide. Current treatment strategies primarily involve surgery, chemotherapy, radiotherapy, targeted therapy, and immunotherapy, determined by TNM stages, histologic types, and genetic profiles. Plenty of studies have been tryi...

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Autores principales: Xu, Qiong, Cha, Qiongfang, Qin, Hui, Liu, Bin, Wu, Xueling, Shi, Jiantao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9580914/
https://www.ncbi.nlm.nih.gov/pubmed/36304306
http://dx.doi.org/10.3389/fbinf.2022.813960
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author Xu, Qiong
Cha, Qiongfang
Qin, Hui
Liu, Bin
Wu, Xueling
Shi, Jiantao
author_facet Xu, Qiong
Cha, Qiongfang
Qin, Hui
Liu, Bin
Wu, Xueling
Shi, Jiantao
author_sort Xu, Qiong
collection PubMed
description Backgrounds: Lung cancer is the leading cause of cancer related death worldwide. Current treatment strategies primarily involve surgery, chemotherapy, radiotherapy, targeted therapy, and immunotherapy, determined by TNM stages, histologic types, and genetic profiles. Plenty of studies have been trying to identify robust prognostic gene expression signatures. Even for high performance signatures, they usually have few shared genes. This is not totally unexpected, since a prognostic signature is associated with patient survival and may contain no upstream regulators. Identification of master regulators driving disease progression is a vital step to understand underlying molecular mechanisms and develop new treatments. Methods: In this study, we have utilized a robust workflow to identify potential master regulators that drive poor prognosis in patients with lung adenocarcinoma. This workflow takes gene expression signatures that are associated with poor survival of early-stage lung adenocarcinoma, EGFR-TKI resistance, and responses to immune checkpoint inhibitors, respectively, and identifies recurrent master regulators from seven public gene expression datasets by a regulatory network-based approach. Results: We have found that majority of the master regulators driving poor prognosis in early stage LUAD are cell-cycle related according to Gene Ontology annotation. However, they were demonstrated experimentally to promote a spectrum of processes such as tumor cell proliferation, invasion, metastasis, and drug resistance. Master regulators predicted from EGFR-TKI resistance signature and the EMT pathway signature are largely shared, which suggests that EMT pathway functions as a hub and interact with other pathways such as hypoxia, angiogenesis, TNF-α signaling, inflammation, TNF-β signaling, Wnt, and Notch signaling pathways. Master regulators that repress immunotherapy are enriched with MYC targets, E2F targets, oxidative phosphorylation, and mTOR signaling. Conclusion: Our study uncovered possible mechanisms underlying recurrence, resistance to targeted therapy, and immunotherapy. The predicted master regulators may serve as potential therapeutic targets in patients with lung adenocarcinoma.
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spelling pubmed-95809142022-10-26 Identification of Master Regulators Driving Disease Progression, Relapse, and Drug Resistance in Lung Adenocarcinoma Xu, Qiong Cha, Qiongfang Qin, Hui Liu, Bin Wu, Xueling Shi, Jiantao Front Bioinform Bioinformatics Backgrounds: Lung cancer is the leading cause of cancer related death worldwide. Current treatment strategies primarily involve surgery, chemotherapy, radiotherapy, targeted therapy, and immunotherapy, determined by TNM stages, histologic types, and genetic profiles. Plenty of studies have been trying to identify robust prognostic gene expression signatures. Even for high performance signatures, they usually have few shared genes. This is not totally unexpected, since a prognostic signature is associated with patient survival and may contain no upstream regulators. Identification of master regulators driving disease progression is a vital step to understand underlying molecular mechanisms and develop new treatments. Methods: In this study, we have utilized a robust workflow to identify potential master regulators that drive poor prognosis in patients with lung adenocarcinoma. This workflow takes gene expression signatures that are associated with poor survival of early-stage lung adenocarcinoma, EGFR-TKI resistance, and responses to immune checkpoint inhibitors, respectively, and identifies recurrent master regulators from seven public gene expression datasets by a regulatory network-based approach. Results: We have found that majority of the master regulators driving poor prognosis in early stage LUAD are cell-cycle related according to Gene Ontology annotation. However, they were demonstrated experimentally to promote a spectrum of processes such as tumor cell proliferation, invasion, metastasis, and drug resistance. Master regulators predicted from EGFR-TKI resistance signature and the EMT pathway signature are largely shared, which suggests that EMT pathway functions as a hub and interact with other pathways such as hypoxia, angiogenesis, TNF-α signaling, inflammation, TNF-β signaling, Wnt, and Notch signaling pathways. Master regulators that repress immunotherapy are enriched with MYC targets, E2F targets, oxidative phosphorylation, and mTOR signaling. Conclusion: Our study uncovered possible mechanisms underlying recurrence, resistance to targeted therapy, and immunotherapy. The predicted master regulators may serve as potential therapeutic targets in patients with lung adenocarcinoma. Frontiers Media S.A. 2022-01-28 /pmc/articles/PMC9580914/ /pubmed/36304306 http://dx.doi.org/10.3389/fbinf.2022.813960 Text en Copyright © 2022 Xu, Cha, Qin, Liu, Wu and Shi. https://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 Bioinformatics
Xu, Qiong
Cha, Qiongfang
Qin, Hui
Liu, Bin
Wu, Xueling
Shi, Jiantao
Identification of Master Regulators Driving Disease Progression, Relapse, and Drug Resistance in Lung Adenocarcinoma
title Identification of Master Regulators Driving Disease Progression, Relapse, and Drug Resistance in Lung Adenocarcinoma
title_full Identification of Master Regulators Driving Disease Progression, Relapse, and Drug Resistance in Lung Adenocarcinoma
title_fullStr Identification of Master Regulators Driving Disease Progression, Relapse, and Drug Resistance in Lung Adenocarcinoma
title_full_unstemmed Identification of Master Regulators Driving Disease Progression, Relapse, and Drug Resistance in Lung Adenocarcinoma
title_short Identification of Master Regulators Driving Disease Progression, Relapse, and Drug Resistance in Lung Adenocarcinoma
title_sort identification of master regulators driving disease progression, relapse, and drug resistance in lung adenocarcinoma
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9580914/
https://www.ncbi.nlm.nih.gov/pubmed/36304306
http://dx.doi.org/10.3389/fbinf.2022.813960
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