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Identification and Validation of the Pyroptosis-Related Molecular Subtypes of Lung Adenocarcinoma by Bioinformatics and Machine Learning

Lung cancer remains the leading cause of cancer death globally, with lung adenocarcinoma (LUAD) being its most prevalent subtype. Due to the heterogeneity of LUAD, patients given the same treatment regimen may have different responses and clinical outcomes. Therefore, identifying new subtypes of LUA...

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Autores principales: Liu, Le-Ping, Lu, Lu, Zhao, Qiang-Qiang, Kou, Qin-Jie, Jiang, Zhen-Zhen, Gui, Rong, Luo, Yan-Wei, Zhao, Qin-Yu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8599430/
https://www.ncbi.nlm.nih.gov/pubmed/34805165
http://dx.doi.org/10.3389/fcell.2021.756340
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author Liu, Le-Ping
Lu, Lu
Zhao, Qiang-Qiang
Kou, Qin-Jie
Jiang, Zhen-Zhen
Gui, Rong
Luo, Yan-Wei
Zhao, Qin-Yu
author_facet Liu, Le-Ping
Lu, Lu
Zhao, Qiang-Qiang
Kou, Qin-Jie
Jiang, Zhen-Zhen
Gui, Rong
Luo, Yan-Wei
Zhao, Qin-Yu
author_sort Liu, Le-Ping
collection PubMed
description Lung cancer remains the leading cause of cancer death globally, with lung adenocarcinoma (LUAD) being its most prevalent subtype. Due to the heterogeneity of LUAD, patients given the same treatment regimen may have different responses and clinical outcomes. Therefore, identifying new subtypes of LUAD is important for predicting prognosis and providing personalized treatment for patients. Pyroptosis-related genes play an essential role in anticancer, but there is limited research investigating pyroptosis in LUAD. In this study, 33 pyroptosis gene expression profiles and clinical information were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. By bioinformatics and machine learning analyses, we identified novel subtypes of LUAD based on 10 pyroptosis-related genes and further validated them in the GEO dataset, with machine learning models performing up to an AUC of 1 for classifying in GEO. A web-based tool was established for clinicians to use our clustering model (http://www.aimedicallab.com/tool/aiml-subphe-luad.html). LUAD patients were clustered into 3 subtypes (A, B, and C), and survival analysis showed that B had the best survival outcome and C had the worst survival outcome. The relationships between pyroptosis gene expression and clinical characteristics were further analyzed in the three molecular subtypes. Immune profiling revealed significant differences in immune cell infiltration among the three molecular subtypes. GO enrichment and KEGG pathway analyses were performed based on the differential genes of the three subtypes, indicating that differentially expressed genes (DEGs) were involved in multiple cellular and biological functions, including RNA catabolic process, mRNA catabolic process, and pathways of neurodegeneration-multiple diseases. Finally, we developed an 8-gene prognostic model that accurately predicted 1-, 3-, and 5-year overall survival. In conclusion, pyroptosis-related genes may play a critical role in LUAD, and provide new insights into the underlying mechanisms of LUAD.
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spelling pubmed-85994302021-11-19 Identification and Validation of the Pyroptosis-Related Molecular Subtypes of Lung Adenocarcinoma by Bioinformatics and Machine Learning Liu, Le-Ping Lu, Lu Zhao, Qiang-Qiang Kou, Qin-Jie Jiang, Zhen-Zhen Gui, Rong Luo, Yan-Wei Zhao, Qin-Yu Front Cell Dev Biol Cell and Developmental Biology Lung cancer remains the leading cause of cancer death globally, with lung adenocarcinoma (LUAD) being its most prevalent subtype. Due to the heterogeneity of LUAD, patients given the same treatment regimen may have different responses and clinical outcomes. Therefore, identifying new subtypes of LUAD is important for predicting prognosis and providing personalized treatment for patients. Pyroptosis-related genes play an essential role in anticancer, but there is limited research investigating pyroptosis in LUAD. In this study, 33 pyroptosis gene expression profiles and clinical information were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. By bioinformatics and machine learning analyses, we identified novel subtypes of LUAD based on 10 pyroptosis-related genes and further validated them in the GEO dataset, with machine learning models performing up to an AUC of 1 for classifying in GEO. A web-based tool was established for clinicians to use our clustering model (http://www.aimedicallab.com/tool/aiml-subphe-luad.html). LUAD patients were clustered into 3 subtypes (A, B, and C), and survival analysis showed that B had the best survival outcome and C had the worst survival outcome. The relationships between pyroptosis gene expression and clinical characteristics were further analyzed in the three molecular subtypes. Immune profiling revealed significant differences in immune cell infiltration among the three molecular subtypes. GO enrichment and KEGG pathway analyses were performed based on the differential genes of the three subtypes, indicating that differentially expressed genes (DEGs) were involved in multiple cellular and biological functions, including RNA catabolic process, mRNA catabolic process, and pathways of neurodegeneration-multiple diseases. Finally, we developed an 8-gene prognostic model that accurately predicted 1-, 3-, and 5-year overall survival. In conclusion, pyroptosis-related genes may play a critical role in LUAD, and provide new insights into the underlying mechanisms of LUAD. Frontiers Media S.A. 2021-11-04 /pmc/articles/PMC8599430/ /pubmed/34805165 http://dx.doi.org/10.3389/fcell.2021.756340 Text en Copyright © 2021 Liu, Lu, Zhao, Kou, Jiang, Gui, Luo and Zhao. 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 Cell and Developmental Biology
Liu, Le-Ping
Lu, Lu
Zhao, Qiang-Qiang
Kou, Qin-Jie
Jiang, Zhen-Zhen
Gui, Rong
Luo, Yan-Wei
Zhao, Qin-Yu
Identification and Validation of the Pyroptosis-Related Molecular Subtypes of Lung Adenocarcinoma by Bioinformatics and Machine Learning
title Identification and Validation of the Pyroptosis-Related Molecular Subtypes of Lung Adenocarcinoma by Bioinformatics and Machine Learning
title_full Identification and Validation of the Pyroptosis-Related Molecular Subtypes of Lung Adenocarcinoma by Bioinformatics and Machine Learning
title_fullStr Identification and Validation of the Pyroptosis-Related Molecular Subtypes of Lung Adenocarcinoma by Bioinformatics and Machine Learning
title_full_unstemmed Identification and Validation of the Pyroptosis-Related Molecular Subtypes of Lung Adenocarcinoma by Bioinformatics and Machine Learning
title_short Identification and Validation of the Pyroptosis-Related Molecular Subtypes of Lung Adenocarcinoma by Bioinformatics and Machine Learning
title_sort identification and validation of the pyroptosis-related molecular subtypes of lung adenocarcinoma by bioinformatics and machine learning
topic Cell and Developmental Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8599430/
https://www.ncbi.nlm.nih.gov/pubmed/34805165
http://dx.doi.org/10.3389/fcell.2021.756340
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