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Identification of cancer stemness and M2 macrophage-associated biomarkers in lung adenocarcinoma

OBJECTIVE: Cancer stemness and M2 macrophages are intimately linked to the prognosis of lung adenocarcinoma (LUAD). For this reason, this investigation sought to identify the key genes relevant to cancer stemness and M2 macrophages, explore the relationship between these genes and clinical character...

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Autores principales: Wang, XiaoFang, Luo, Xuan, Wang, ZhiYuan, Wang, YangHao, Zhao, Juan, Bian, Li
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10472008/
https://www.ncbi.nlm.nih.gov/pubmed/37662825
http://dx.doi.org/10.1016/j.heliyon.2023.e19114
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author Wang, XiaoFang
Luo, Xuan
Wang, ZhiYuan
Wang, YangHao
Zhao, Juan
Bian, Li
author_facet Wang, XiaoFang
Luo, Xuan
Wang, ZhiYuan
Wang, YangHao
Zhao, Juan
Bian, Li
author_sort Wang, XiaoFang
collection PubMed
description OBJECTIVE: Cancer stemness and M2 macrophages are intimately linked to the prognosis of lung adenocarcinoma (LUAD). For this reason, this investigation sought to identify the key genes relevant to cancer stemness and M2 macrophages, explore the relationship between these genes and clinical characteristics, and determine the potential mechanism. METHODS: LUAD transcriptomic data was analyzed from The Cancer Genome Atlas (TCGA) as well as the Gene Expression Omnibus databases. Differential expression analysis was performed to discern abnormally expressed genes between LUAD and control samples in TCGA cohort. The Cell type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was applied to determine varyingly infiltrated immune cells in LUAD compared with the control samples in TCGA cohort. Weighted correlation network analysis (WGCNA) was performed to identify genes associated with mRNA expression-based stemness index (mRNAsi) and M2 macrophages. Least absolute shrinkage and selection operator (LASSO), RandomForest (RF) and support vector machine-recursive feature elimination (SVM-RFE) machine learning methods were conducted to detect gene signatures. Global survival evaluation (Kaplan-Meier curve) was applied to investigate the relationship between gene signatures and the survival time of LUAD patients. Receiver operating characteristic (ROC) curves were produced to define biomarkers relevant to diagnosis. Gene Set Enrichment Analysis (GSEA) was performed to probe the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways related to diagnostic biomarkers. The public single-cell dataset of LUAD (GSE123902) was used to investigate the expression differences of diagnostic biomarkers in various cell types in LUAD. Real-time quantitative PCR (qRT-PCR) was performed to confirm key genes in lung adenocarcinoma cells. RESULTS: A total of 5,410 differentialy expressed genes (DEGs) as well as 15 differentially infiltrated immune cells were identified between LUAD and control sepcimens in TCGA cohort. Thirty-seven DEGs were associated with both M2 macrophages and mRNAsi according to the WGCNA analysis. Sixteen common gene signatures were obtained using three diverse algorithms. CBFA2T3, DENND3 and FCAMR were correlated to overall and disease-free survival of LUAD patients. ROC curves revealed that CBFA2T3 and DENND3 expression accurately classified LUAD and control samples. The results of single cell related analysis showed that two diagnostic biomarkers expressions were differed between the different tissue sources in M2-like macrophages. QRT-PCR demonstrated the mRNA expressions of CBFA2T3 and DENND3 were upregulated in lung adenocarcinoma cells A549 and H2122. CONCLUSION: Our study identified CBFA2T3 and DENND3 as key genes associated with mRNAsi and M2 macrophages in LUAD and investigated the potential molecular mechanisms underlying this relationship.
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spelling pubmed-104720082023-09-02 Identification of cancer stemness and M2 macrophage-associated biomarkers in lung adenocarcinoma Wang, XiaoFang Luo, Xuan Wang, ZhiYuan Wang, YangHao Zhao, Juan Bian, Li Heliyon Research Article OBJECTIVE: Cancer stemness and M2 macrophages are intimately linked to the prognosis of lung adenocarcinoma (LUAD). For this reason, this investigation sought to identify the key genes relevant to cancer stemness and M2 macrophages, explore the relationship between these genes and clinical characteristics, and determine the potential mechanism. METHODS: LUAD transcriptomic data was analyzed from The Cancer Genome Atlas (TCGA) as well as the Gene Expression Omnibus databases. Differential expression analysis was performed to discern abnormally expressed genes between LUAD and control samples in TCGA cohort. The Cell type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was applied to determine varyingly infiltrated immune cells in LUAD compared with the control samples in TCGA cohort. Weighted correlation network analysis (WGCNA) was performed to identify genes associated with mRNA expression-based stemness index (mRNAsi) and M2 macrophages. Least absolute shrinkage and selection operator (LASSO), RandomForest (RF) and support vector machine-recursive feature elimination (SVM-RFE) machine learning methods were conducted to detect gene signatures. Global survival evaluation (Kaplan-Meier curve) was applied to investigate the relationship between gene signatures and the survival time of LUAD patients. Receiver operating characteristic (ROC) curves were produced to define biomarkers relevant to diagnosis. Gene Set Enrichment Analysis (GSEA) was performed to probe the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways related to diagnostic biomarkers. The public single-cell dataset of LUAD (GSE123902) was used to investigate the expression differences of diagnostic biomarkers in various cell types in LUAD. Real-time quantitative PCR (qRT-PCR) was performed to confirm key genes in lung adenocarcinoma cells. RESULTS: A total of 5,410 differentialy expressed genes (DEGs) as well as 15 differentially infiltrated immune cells were identified between LUAD and control sepcimens in TCGA cohort. Thirty-seven DEGs were associated with both M2 macrophages and mRNAsi according to the WGCNA analysis. Sixteen common gene signatures were obtained using three diverse algorithms. CBFA2T3, DENND3 and FCAMR were correlated to overall and disease-free survival of LUAD patients. ROC curves revealed that CBFA2T3 and DENND3 expression accurately classified LUAD and control samples. The results of single cell related analysis showed that two diagnostic biomarkers expressions were differed between the different tissue sources in M2-like macrophages. QRT-PCR demonstrated the mRNA expressions of CBFA2T3 and DENND3 were upregulated in lung adenocarcinoma cells A549 and H2122. CONCLUSION: Our study identified CBFA2T3 and DENND3 as key genes associated with mRNAsi and M2 macrophages in LUAD and investigated the potential molecular mechanisms underlying this relationship. Elsevier 2023-08-16 /pmc/articles/PMC10472008/ /pubmed/37662825 http://dx.doi.org/10.1016/j.heliyon.2023.e19114 Text en © 2023 Published by Elsevier Ltd. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Research Article
Wang, XiaoFang
Luo, Xuan
Wang, ZhiYuan
Wang, YangHao
Zhao, Juan
Bian, Li
Identification of cancer stemness and M2 macrophage-associated biomarkers in lung adenocarcinoma
title Identification of cancer stemness and M2 macrophage-associated biomarkers in lung adenocarcinoma
title_full Identification of cancer stemness and M2 macrophage-associated biomarkers in lung adenocarcinoma
title_fullStr Identification of cancer stemness and M2 macrophage-associated biomarkers in lung adenocarcinoma
title_full_unstemmed Identification of cancer stemness and M2 macrophage-associated biomarkers in lung adenocarcinoma
title_short Identification of cancer stemness and M2 macrophage-associated biomarkers in lung adenocarcinoma
title_sort identification of cancer stemness and m2 macrophage-associated biomarkers in lung adenocarcinoma
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10472008/
https://www.ncbi.nlm.nih.gov/pubmed/37662825
http://dx.doi.org/10.1016/j.heliyon.2023.e19114
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