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Dissecting the single-cell transcriptome network of macrophage and identifies a signature to predict prognosis in lung adenocarcinoma

PURPOSE: The tumor immune microenvironment (TME) plays a vital role in tumorigenesis, progression, and treatment. Macrophages, as an important component of the tumor microenvironment, play an essential role in antitumor immunity and TME remodeling. In this study, we aimed to explore the different fu...

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Autores principales: Hu, Zhengyang, Jin, Xing, Hong, Weifeng, Sui, Qihai, Zhao, Mengnan, Huang, Yiwei, Li, Ming, Wang, Qun, Zhan, Cheng, Chen, Zhencong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Netherlands 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10116118/
https://www.ncbi.nlm.nih.gov/pubmed/37079186
http://dx.doi.org/10.1007/s13402-023-00816-7
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author Hu, Zhengyang
Jin, Xing
Hong, Weifeng
Sui, Qihai
Zhao, Mengnan
Huang, Yiwei
Li, Ming
Wang, Qun
Zhan, Cheng
Chen, Zhencong
author_facet Hu, Zhengyang
Jin, Xing
Hong, Weifeng
Sui, Qihai
Zhao, Mengnan
Huang, Yiwei
Li, Ming
Wang, Qun
Zhan, Cheng
Chen, Zhencong
author_sort Hu, Zhengyang
collection PubMed
description PURPOSE: The tumor immune microenvironment (TME) plays a vital role in tumorigenesis, progression, and treatment. Macrophages, as an important component of the tumor microenvironment, play an essential role in antitumor immunity and TME remodeling. In this study, we aimed to explore the different functions of different origins macrophages in TME and their value as potential predictive markers of prognosis and treatment. METHODS: We performed single-cell analysis using 21 lung adenocarcinoma (LUAD), 12 normal, and four peripheral blood samples from our data and public databases. A prognostic prediction model was then constructed using 502 TCGA patients and explored the potential factors affecting prognosis. The model was validated using data from 4 different GEO datasets with 544 patients after integration. RESULTS: According to the source of macrophages, we classified macrophages into alveolar macrophages (AMs) and interstitial macrophages (IMs). AMs mainly infiltrated in normal lung tissue and expressed proliferative, antigen-presenting, scavenger receptors genes, while IMs occupied the majority in TME and expressed anti-inflammatory, lipid metabolism-related genes. Trajectory analysis revealed that AMs rely on self-renew, whereas IMs originated from monocytes in the blood. Cell-to-cell communication showed that AMs interacted mainly with T cells through the MHC I/II signaling pathway, while IMs mostly interacted with tumor-associated fibrocytes and tumor cells. We then constructed a risk model based on macrophage infiltration and showed an excellent predictive power. We further revealed the possible reasons for its potential prognosis prediction by differential genes, immune cell infiltration, and mutational differences. CONCLUSION: In conclusion, we investigated the composition, expression differences, and phenotypic changes of macrophages from different origins in lung adenocarcinoma. In addition, we developed a prognostic prediction model based on different macrophage subtype infiltration, which can be used as a valid prognostic biomarker. New insights were provided into the role of macrophages in the prognosis and potential treatment of LUAD patients. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13402-023-00816-7.
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spelling pubmed-101161182023-04-25 Dissecting the single-cell transcriptome network of macrophage and identifies a signature to predict prognosis in lung adenocarcinoma Hu, Zhengyang Jin, Xing Hong, Weifeng Sui, Qihai Zhao, Mengnan Huang, Yiwei Li, Ming Wang, Qun Zhan, Cheng Chen, Zhencong Cell Oncol (Dordr) Research PURPOSE: The tumor immune microenvironment (TME) plays a vital role in tumorigenesis, progression, and treatment. Macrophages, as an important component of the tumor microenvironment, play an essential role in antitumor immunity and TME remodeling. In this study, we aimed to explore the different functions of different origins macrophages in TME and their value as potential predictive markers of prognosis and treatment. METHODS: We performed single-cell analysis using 21 lung adenocarcinoma (LUAD), 12 normal, and four peripheral blood samples from our data and public databases. A prognostic prediction model was then constructed using 502 TCGA patients and explored the potential factors affecting prognosis. The model was validated using data from 4 different GEO datasets with 544 patients after integration. RESULTS: According to the source of macrophages, we classified macrophages into alveolar macrophages (AMs) and interstitial macrophages (IMs). AMs mainly infiltrated in normal lung tissue and expressed proliferative, antigen-presenting, scavenger receptors genes, while IMs occupied the majority in TME and expressed anti-inflammatory, lipid metabolism-related genes. Trajectory analysis revealed that AMs rely on self-renew, whereas IMs originated from monocytes in the blood. Cell-to-cell communication showed that AMs interacted mainly with T cells through the MHC I/II signaling pathway, while IMs mostly interacted with tumor-associated fibrocytes and tumor cells. We then constructed a risk model based on macrophage infiltration and showed an excellent predictive power. We further revealed the possible reasons for its potential prognosis prediction by differential genes, immune cell infiltration, and mutational differences. CONCLUSION: In conclusion, we investigated the composition, expression differences, and phenotypic changes of macrophages from different origins in lung adenocarcinoma. In addition, we developed a prognostic prediction model based on different macrophage subtype infiltration, which can be used as a valid prognostic biomarker. New insights were provided into the role of macrophages in the prognosis and potential treatment of LUAD patients. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13402-023-00816-7. Springer Netherlands 2023-04-20 /pmc/articles/PMC10116118/ /pubmed/37079186 http://dx.doi.org/10.1007/s13402-023-00816-7 Text en © Springer Nature Switzerland AG 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Research
Hu, Zhengyang
Jin, Xing
Hong, Weifeng
Sui, Qihai
Zhao, Mengnan
Huang, Yiwei
Li, Ming
Wang, Qun
Zhan, Cheng
Chen, Zhencong
Dissecting the single-cell transcriptome network of macrophage and identifies a signature to predict prognosis in lung adenocarcinoma
title Dissecting the single-cell transcriptome network of macrophage and identifies a signature to predict prognosis in lung adenocarcinoma
title_full Dissecting the single-cell transcriptome network of macrophage and identifies a signature to predict prognosis in lung adenocarcinoma
title_fullStr Dissecting the single-cell transcriptome network of macrophage and identifies a signature to predict prognosis in lung adenocarcinoma
title_full_unstemmed Dissecting the single-cell transcriptome network of macrophage and identifies a signature to predict prognosis in lung adenocarcinoma
title_short Dissecting the single-cell transcriptome network of macrophage and identifies a signature to predict prognosis in lung adenocarcinoma
title_sort dissecting the single-cell transcriptome network of macrophage and identifies a signature to predict prognosis in lung adenocarcinoma
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10116118/
https://www.ncbi.nlm.nih.gov/pubmed/37079186
http://dx.doi.org/10.1007/s13402-023-00816-7
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