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Hypoxia‐related lncRNAs to build prognostic classifier and reveal the immune characteristics of EGFR wild type and low expression of PD‐L1 squamous and adenocarcinoma NSCLC
BACKGROUND: Recently, the development and application of targeted therapies like tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs) have achieved remarkable survival benefits in non‐small cell lung cancer (NSCLC) treatment. However, epidermal growth factor receptor (EGFR) wild...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8419766/ https://www.ncbi.nlm.nih.gov/pubmed/34250747 http://dx.doi.org/10.1002/cam4.4126 |
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author | Zhao, Fang Wang, Min Zhu, Jie |
author_facet | Zhao, Fang Wang, Min Zhu, Jie |
author_sort | Zhao, Fang |
collection | PubMed |
description | BACKGROUND: Recently, the development and application of targeted therapies like tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs) have achieved remarkable survival benefits in non‐small cell lung cancer (NSCLC) treatment. However, epidermal growth factor receptor (EGFR) wild type and low expression of programmed death‐ligand 1 (PD‐L1) NSCLC remain unmanageable. Few treatments for these patients exist, and more side effects with combination therapies have been observed. We intended to generate a hypoxia‐related lncRNAs (hypolncRNAs) classifier that could successfully identify the high‐risk patients and reveal its underlying molecular immunology characteristics. METHODS: By identifying the bottom 25% PD‐L1 expression level as low expression of PD‐L1 and removing EGFR mutant samples, a total of 222 lung adenocarcinoma (LUAD) and lung squamous carcinoma (LUSC) samples and 93 adjacent non‐tumor samples were finally extracted from The Cancer Genome Atlas (TCGA). A 0 or 1 matrix was constructed by cyclically pairing hypoxia‐related long non‐coding RNAs (hypolncRNAs) and divided into the train set and test set. The univariate Cox regression analysis determined the prognostic hypolncRNAs pairs. Then, the prognostic classifier contained nine hypolncRNAs pairs which were generated by Lasso regression and multivariate Cox analysis. It successfully stratified EGFR wild type and low expression of PD‐L1 squamous and adenocarcinoma NSCLC (double‐negative LUAD and LUSC) patients into the high‐ and low‐risk groups, whose accuracy was proved by the time‐dependent receiver operating characteristic (ROC) curve. Furthermore, diverse acknowledged immunology methods include XCELL, TIMER, QUANTISEQ, MCPcounter, EPIC, CIBERSORT‐ABS, CIBERSORT, and the single‐sample gene set enrichment analysis (ssGSEA) revealed its underlying antitumor immunosuppressive status in the high‐risk patients. CONCLUSIONS: It is noteworthy that hypolncRNAs are associated with the survival of double‐negative LUAD and LUSC patients, for which the possible mechanism is inhibiting the antitumor immune process. |
format | Online Article Text |
id | pubmed-8419766 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-84197662021-09-08 Hypoxia‐related lncRNAs to build prognostic classifier and reveal the immune characteristics of EGFR wild type and low expression of PD‐L1 squamous and adenocarcinoma NSCLC Zhao, Fang Wang, Min Zhu, Jie Cancer Med Bioinformatics BACKGROUND: Recently, the development and application of targeted therapies like tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs) have achieved remarkable survival benefits in non‐small cell lung cancer (NSCLC) treatment. However, epidermal growth factor receptor (EGFR) wild type and low expression of programmed death‐ligand 1 (PD‐L1) NSCLC remain unmanageable. Few treatments for these patients exist, and more side effects with combination therapies have been observed. We intended to generate a hypoxia‐related lncRNAs (hypolncRNAs) classifier that could successfully identify the high‐risk patients and reveal its underlying molecular immunology characteristics. METHODS: By identifying the bottom 25% PD‐L1 expression level as low expression of PD‐L1 and removing EGFR mutant samples, a total of 222 lung adenocarcinoma (LUAD) and lung squamous carcinoma (LUSC) samples and 93 adjacent non‐tumor samples were finally extracted from The Cancer Genome Atlas (TCGA). A 0 or 1 matrix was constructed by cyclically pairing hypoxia‐related long non‐coding RNAs (hypolncRNAs) and divided into the train set and test set. The univariate Cox regression analysis determined the prognostic hypolncRNAs pairs. Then, the prognostic classifier contained nine hypolncRNAs pairs which were generated by Lasso regression and multivariate Cox analysis. It successfully stratified EGFR wild type and low expression of PD‐L1 squamous and adenocarcinoma NSCLC (double‐negative LUAD and LUSC) patients into the high‐ and low‐risk groups, whose accuracy was proved by the time‐dependent receiver operating characteristic (ROC) curve. Furthermore, diverse acknowledged immunology methods include XCELL, TIMER, QUANTISEQ, MCPcounter, EPIC, CIBERSORT‐ABS, CIBERSORT, and the single‐sample gene set enrichment analysis (ssGSEA) revealed its underlying antitumor immunosuppressive status in the high‐risk patients. CONCLUSIONS: It is noteworthy that hypolncRNAs are associated with the survival of double‐negative LUAD and LUSC patients, for which the possible mechanism is inhibiting the antitumor immune process. John Wiley and Sons Inc. 2021-07-11 /pmc/articles/PMC8419766/ /pubmed/34250747 http://dx.doi.org/10.1002/cam4.4126 Text en © 2021 The Authors. Cancer Medicine published by John Wiley & Sons Ltd. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Bioinformatics Zhao, Fang Wang, Min Zhu, Jie Hypoxia‐related lncRNAs to build prognostic classifier and reveal the immune characteristics of EGFR wild type and low expression of PD‐L1 squamous and adenocarcinoma NSCLC |
title | Hypoxia‐related lncRNAs to build prognostic classifier and reveal the immune characteristics of EGFR wild type and low expression of PD‐L1 squamous and adenocarcinoma NSCLC |
title_full | Hypoxia‐related lncRNAs to build prognostic classifier and reveal the immune characteristics of EGFR wild type and low expression of PD‐L1 squamous and adenocarcinoma NSCLC |
title_fullStr | Hypoxia‐related lncRNAs to build prognostic classifier and reveal the immune characteristics of EGFR wild type and low expression of PD‐L1 squamous and adenocarcinoma NSCLC |
title_full_unstemmed | Hypoxia‐related lncRNAs to build prognostic classifier and reveal the immune characteristics of EGFR wild type and low expression of PD‐L1 squamous and adenocarcinoma NSCLC |
title_short | Hypoxia‐related lncRNAs to build prognostic classifier and reveal the immune characteristics of EGFR wild type and low expression of PD‐L1 squamous and adenocarcinoma NSCLC |
title_sort | hypoxia‐related lncrnas to build prognostic classifier and reveal the immune characteristics of egfr wild type and low expression of pd‐l1 squamous and adenocarcinoma nsclc |
topic | Bioinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8419766/ https://www.ncbi.nlm.nih.gov/pubmed/34250747 http://dx.doi.org/10.1002/cam4.4126 |
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