Identification and validation of M2 macrophage-related gene signature as a novel prognostic model for head and neck squamous cell carcinoma

Abstract Head and neck squamous cell carcinoma (HNSCC) is a highly heterogeneous tumor. Commonly used tumor staging don’t sufficiently and accurately assess the prognosis of HNSCC patients, resulting in a lack of guidance for clinical treatment decisions. M2 macrophage infiltration has been shown to...

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Main Authors: Shengmei He, Huarong Chen, Changya Li, Bao Feng, Ruizhe Zhang, Houyu Zhao, Xianlu Zhuo
Format: Article
Language:English
Published: Nature Portfolio 2024-10-01
Series:Scientific Reports
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Online Access:https://doi.org/10.1038/s41598-024-76866-0
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author Shengmei He
Huarong Chen
Changya Li
Bao Feng
Ruizhe Zhang
Houyu Zhao
Xianlu Zhuo
author_facet Shengmei He
Huarong Chen
Changya Li
Bao Feng
Ruizhe Zhang
Houyu Zhao
Xianlu Zhuo
author_sort Shengmei He
collection DOAJ
description Abstract Head and neck squamous cell carcinoma (HNSCC) is a highly heterogeneous tumor. Commonly used tumor staging don’t sufficiently and accurately assess the prognosis of HNSCC patients, resulting in a lack of guidance for clinical treatment decisions. M2 macrophage infiltration has been shown to be strongly associated with the tumor prognosis. In this study, we used the Cancer Genome Atlas (TCGA) data to screen for genes co-expressed with M2 macrophages in HNSCC. We used univariate Cox regression to screen out the genes associated with HNSCC prognosis, and constructed a HNSCC prognosis model by Lasso regression analysis. The results confirmed that the model had good predictive value and accuracy for the prognosis of HNSCC patients by survival analysis, ROC curve and nomogram. We divided the HNSCC samples into high-risk and low-risk groups according to the risk score, and the results showed that patients in the high-risk group were more prone to genetic mutations and had a higher tumor mutational burden. In addition, there were significant differences between risk groups in terms of immune cell infiltration and drug sensitivity. The HNSCC prognostic model established in this study may provide guidance for clinical therapeutic decision-making and provide a theoretical foundation for the development of new immunotherapy methods.
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spelling doaj-art-54cbbafd8ab7410294c039ea8c3eaa092025-08-20T02:11:17ZengNature PortfolioScientific Reports2045-23222024-10-0114111410.1038/s41598-024-76866-0Identification and validation of M2 macrophage-related gene signature as a novel prognostic model for head and neck squamous cell carcinomaShengmei He0Huarong Chen1Changya Li2Bao Feng3Ruizhe Zhang4Houyu Zhao5Xianlu Zhuo6Department of Otorhinolaryngology Head and Neck Surgery, The Affiliated Hospital of Guizhou Medical UniversityDepartment of Otorhinolaryngology Head and Neck Surgery, The Affiliated Hospital of Guizhou Medical UniversityDepartment of Otorhinolaryngology Head and Neck Surgery, The Affiliated Hospital of Guizhou Medical UniversitySchool of clinical medicine, Guizhou Medical UniversitySchool of clinical medicine, Guizhou Medical UniversityDepartment of Otorhinolaryngology Head and Neck Surgery, The Affiliated Hospital of Guizhou Medical UniversityDepartment of Otorhinolaryngology Head and Neck Surgery, The Affiliated Hospital of Guizhou Medical UniversityAbstract Head and neck squamous cell carcinoma (HNSCC) is a highly heterogeneous tumor. Commonly used tumor staging don’t sufficiently and accurately assess the prognosis of HNSCC patients, resulting in a lack of guidance for clinical treatment decisions. M2 macrophage infiltration has been shown to be strongly associated with the tumor prognosis. In this study, we used the Cancer Genome Atlas (TCGA) data to screen for genes co-expressed with M2 macrophages in HNSCC. We used univariate Cox regression to screen out the genes associated with HNSCC prognosis, and constructed a HNSCC prognosis model by Lasso regression analysis. The results confirmed that the model had good predictive value and accuracy for the prognosis of HNSCC patients by survival analysis, ROC curve and nomogram. We divided the HNSCC samples into high-risk and low-risk groups according to the risk score, and the results showed that patients in the high-risk group were more prone to genetic mutations and had a higher tumor mutational burden. In addition, there were significant differences between risk groups in terms of immune cell infiltration and drug sensitivity. The HNSCC prognostic model established in this study may provide guidance for clinical therapeutic decision-making and provide a theoretical foundation for the development of new immunotherapy methods.https://doi.org/10.1038/s41598-024-76866-0M2 macrophageHead and neck squamous cell carcinomaImmune infiltrationPrognostic model
spellingShingle Shengmei He
Huarong Chen
Changya Li
Bao Feng
Ruizhe Zhang
Houyu Zhao
Xianlu Zhuo
Identification and validation of M2 macrophage-related gene signature as a novel prognostic model for head and neck squamous cell carcinoma
Scientific Reports
M2 macrophage
Head and neck squamous cell carcinoma
Immune infiltration
Prognostic model
title Identification and validation of M2 macrophage-related gene signature as a novel prognostic model for head and neck squamous cell carcinoma
title_full Identification and validation of M2 macrophage-related gene signature as a novel prognostic model for head and neck squamous cell carcinoma
title_fullStr Identification and validation of M2 macrophage-related gene signature as a novel prognostic model for head and neck squamous cell carcinoma
title_full_unstemmed Identification and validation of M2 macrophage-related gene signature as a novel prognostic model for head and neck squamous cell carcinoma
title_short Identification and validation of M2 macrophage-related gene signature as a novel prognostic model for head and neck squamous cell carcinoma
title_sort identification and validation of m2 macrophage related gene signature as a novel prognostic model for head and neck squamous cell carcinoma
topic M2 macrophage
Head and neck squamous cell carcinoma
Immune infiltration
Prognostic model
url https://doi.org/10.1038/s41598-024-76866-0
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