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Genomic and immunogenomic analysis of three prognostic signature genes in LUAD

BACKGROUND: Searching for immunotherapy-related markers is an important research content to screen for target populations suitable for immunotherapy. Prognosis-related genes in early stage lung cancer may also affect the tumor immune microenvironment, which in turn affects immunotherapy. RESULTS: We...

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Autores principales: Feng, Hai-Ming, Zhao, Ye, Yan, Wei-Jian, Li, Bin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9843910/
https://www.ncbi.nlm.nih.gov/pubmed/36650426
http://dx.doi.org/10.1186/s12859-023-05137-y
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author Feng, Hai-Ming
Zhao, Ye
Yan, Wei-Jian
Li, Bin
author_facet Feng, Hai-Ming
Zhao, Ye
Yan, Wei-Jian
Li, Bin
author_sort Feng, Hai-Ming
collection PubMed
description BACKGROUND: Searching for immunotherapy-related markers is an important research content to screen for target populations suitable for immunotherapy. Prognosis-related genes in early stage lung cancer may also affect the tumor immune microenvironment, which in turn affects immunotherapy. RESULTS: We analyzed the differential genes affecting lung cancer patients receiving immunotherapy through the Cancer Treatment Response gene signature DataBase (CTR-DB), and set a threshold to obtain a total of 176 differential genes between response and non-response to immunotherapy. Functional enrichment analysis found that these differential genes were mainly involved in immune regulation-related pathways. The early-stage lung adenocarcinoma (LUAD) prognostic model was constructed through the cancer genome atlas (TCGA) database, and three target genes (MMP12, NFE2, HOXC8) were screened to calculate the risk score of early-stage LUAD. The receiver operating characteristic (ROC) curve indicated that the model had good prognostic value, and the validation set (GSE50081, GSE11969 and GSE42127) from the gene expression omnibus (GEO) analysis indicated that the model had good stability, and the risk score was correlated with immune infiltrations to varying degrees. Multi-type survival analysis and immune infiltration analysis revealed that the transcriptome, methylation and the copy number variation (CNV) levels of the three genes were correlated with patient prognosis and some tumor microenvironment (TME) components. Drug sensitivity analysis found that the three genes may affect some anti-tumor drugs. The mRNA expression of immune checkpoint-related genes showed significant differences between the high and low group of the three genes, and there may be a mutual regulatory network between immune checkpoint-related genes and target genes. Tumor immune dysfunction and exclusion (TIDE) analysis found that three genes were associated with immunotherapy response and maybe the potential predictors to immunotherapy, consistent with the CTR-DB database analysis. CONCLUSIONS: From the perspective of data mining, this study suggests that MMP12, NFE2, and HOXC8 may be involved in tumor immune regulation and affect immunotherapy. They are expected to become markers of immunotherapy and are worthy of further experimental research. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-023-05137-y.
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spelling pubmed-98439102023-01-18 Genomic and immunogenomic analysis of three prognostic signature genes in LUAD Feng, Hai-Ming Zhao, Ye Yan, Wei-Jian Li, Bin BMC Bioinformatics Research BACKGROUND: Searching for immunotherapy-related markers is an important research content to screen for target populations suitable for immunotherapy. Prognosis-related genes in early stage lung cancer may also affect the tumor immune microenvironment, which in turn affects immunotherapy. RESULTS: We analyzed the differential genes affecting lung cancer patients receiving immunotherapy through the Cancer Treatment Response gene signature DataBase (CTR-DB), and set a threshold to obtain a total of 176 differential genes between response and non-response to immunotherapy. Functional enrichment analysis found that these differential genes were mainly involved in immune regulation-related pathways. The early-stage lung adenocarcinoma (LUAD) prognostic model was constructed through the cancer genome atlas (TCGA) database, and three target genes (MMP12, NFE2, HOXC8) were screened to calculate the risk score of early-stage LUAD. The receiver operating characteristic (ROC) curve indicated that the model had good prognostic value, and the validation set (GSE50081, GSE11969 and GSE42127) from the gene expression omnibus (GEO) analysis indicated that the model had good stability, and the risk score was correlated with immune infiltrations to varying degrees. Multi-type survival analysis and immune infiltration analysis revealed that the transcriptome, methylation and the copy number variation (CNV) levels of the three genes were correlated with patient prognosis and some tumor microenvironment (TME) components. Drug sensitivity analysis found that the three genes may affect some anti-tumor drugs. The mRNA expression of immune checkpoint-related genes showed significant differences between the high and low group of the three genes, and there may be a mutual regulatory network between immune checkpoint-related genes and target genes. Tumor immune dysfunction and exclusion (TIDE) analysis found that three genes were associated with immunotherapy response and maybe the potential predictors to immunotherapy, consistent with the CTR-DB database analysis. CONCLUSIONS: From the perspective of data mining, this study suggests that MMP12, NFE2, and HOXC8 may be involved in tumor immune regulation and affect immunotherapy. They are expected to become markers of immunotherapy and are worthy of further experimental research. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-023-05137-y. BioMed Central 2023-01-17 /pmc/articles/PMC9843910/ /pubmed/36650426 http://dx.doi.org/10.1186/s12859-023-05137-y Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Feng, Hai-Ming
Zhao, Ye
Yan, Wei-Jian
Li, Bin
Genomic and immunogenomic analysis of three prognostic signature genes in LUAD
title Genomic and immunogenomic analysis of three prognostic signature genes in LUAD
title_full Genomic and immunogenomic analysis of three prognostic signature genes in LUAD
title_fullStr Genomic and immunogenomic analysis of three prognostic signature genes in LUAD
title_full_unstemmed Genomic and immunogenomic analysis of three prognostic signature genes in LUAD
title_short Genomic and immunogenomic analysis of three prognostic signature genes in LUAD
title_sort genomic and immunogenomic analysis of three prognostic signature genes in luad
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9843910/
https://www.ncbi.nlm.nih.gov/pubmed/36650426
http://dx.doi.org/10.1186/s12859-023-05137-y
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