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Analysis of M2 macrophage-associated risk score signature in pancreatic cancer TME landscape and immunotherapy

Background: M2 macrophages perform an influential role in the progression of pancreatic cancer. This study is dedicated to explore the value of M2 macrophage-related genes in the treatment and prognosis of pancreatic cancer. Methods: RNA-Seq and clinical information were downloaded from TCGA, GEO an...

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Autores principales: Yang, Dashuai, Zhao, Fangrui, Su, Yang, Zhou, Yu, Shen, Jie, Zhao, Kailiang, Ding, Youming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10352656/
https://www.ncbi.nlm.nih.gov/pubmed/37469705
http://dx.doi.org/10.3389/fmolb.2023.1184708
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author Yang, Dashuai
Zhao, Fangrui
Su, Yang
Zhou, Yu
Shen, Jie
Zhao, Kailiang
Ding, Youming
author_facet Yang, Dashuai
Zhao, Fangrui
Su, Yang
Zhou, Yu
Shen, Jie
Zhao, Kailiang
Ding, Youming
author_sort Yang, Dashuai
collection PubMed
description Background: M2 macrophages perform an influential role in the progression of pancreatic cancer. This study is dedicated to explore the value of M2 macrophage-related genes in the treatment and prognosis of pancreatic cancer. Methods: RNA-Seq and clinical information were downloaded from TCGA, GEO and ICGC databases. The pancreatic cancer tumour microenvironment was revealed using the CIBERSORT algorithm. Weighted gene co-expression network analysis (WGCNA) was used to detect M2 macrophage-associated gene modules. Univariate Cox regression, Least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate Cox regression were applied to develop the prognostic model. The modelling and validation cohorts were divided into high-risk and low-risk groups according to the median risk score. The nomogram predicting survival was constructed based on risk scores. Correlations between risk scores and tumour mutational load, clinical variables, immune checkpoint blockade, and immune cells were further explored. Finally, potential associations between different risk models and chemotherapeutic agent efficacy were predicted. Results: The intersection of the WGCNA results from the TCGA and GEO data screened for 317 M2 macrophage-associated genes. Nine genes were identified by multivariate COX regression analysis and applied to the construction of risk models. The results of GSEA analysis revealed that most of these genes were related to signaling, cytokine receptor interaction and immunodeficiency pathways. The high and low risk groups were closely associated with tumour mutational burden, immune checkpoint blockade related genes, and immune cells. The maximum inhibitory concentrations of metformin, paclitaxel, and rufatinib lapatinib were significantly differences on the two risk groups. Conclusion: WGCNA-based analysis of M2 macrophage-associated genes can help predict the prognosis of pancreatic cancer patients and may provide new options for immunotherapy of pancreatic cancer.
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spelling pubmed-103526562023-07-19 Analysis of M2 macrophage-associated risk score signature in pancreatic cancer TME landscape and immunotherapy Yang, Dashuai Zhao, Fangrui Su, Yang Zhou, Yu Shen, Jie Zhao, Kailiang Ding, Youming Front Mol Biosci Molecular Biosciences Background: M2 macrophages perform an influential role in the progression of pancreatic cancer. This study is dedicated to explore the value of M2 macrophage-related genes in the treatment and prognosis of pancreatic cancer. Methods: RNA-Seq and clinical information were downloaded from TCGA, GEO and ICGC databases. The pancreatic cancer tumour microenvironment was revealed using the CIBERSORT algorithm. Weighted gene co-expression network analysis (WGCNA) was used to detect M2 macrophage-associated gene modules. Univariate Cox regression, Least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate Cox regression were applied to develop the prognostic model. The modelling and validation cohorts were divided into high-risk and low-risk groups according to the median risk score. The nomogram predicting survival was constructed based on risk scores. Correlations between risk scores and tumour mutational load, clinical variables, immune checkpoint blockade, and immune cells were further explored. Finally, potential associations between different risk models and chemotherapeutic agent efficacy were predicted. Results: The intersection of the WGCNA results from the TCGA and GEO data screened for 317 M2 macrophage-associated genes. Nine genes were identified by multivariate COX regression analysis and applied to the construction of risk models. The results of GSEA analysis revealed that most of these genes were related to signaling, cytokine receptor interaction and immunodeficiency pathways. The high and low risk groups were closely associated with tumour mutational burden, immune checkpoint blockade related genes, and immune cells. The maximum inhibitory concentrations of metformin, paclitaxel, and rufatinib lapatinib were significantly differences on the two risk groups. Conclusion: WGCNA-based analysis of M2 macrophage-associated genes can help predict the prognosis of pancreatic cancer patients and may provide new options for immunotherapy of pancreatic cancer. Frontiers Media S.A. 2023-07-04 /pmc/articles/PMC10352656/ /pubmed/37469705 http://dx.doi.org/10.3389/fmolb.2023.1184708 Text en Copyright © 2023 Yang, Zhao, Su, Zhou, Shen, Zhao and Ding. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Molecular Biosciences
Yang, Dashuai
Zhao, Fangrui
Su, Yang
Zhou, Yu
Shen, Jie
Zhao, Kailiang
Ding, Youming
Analysis of M2 macrophage-associated risk score signature in pancreatic cancer TME landscape and immunotherapy
title Analysis of M2 macrophage-associated risk score signature in pancreatic cancer TME landscape and immunotherapy
title_full Analysis of M2 macrophage-associated risk score signature in pancreatic cancer TME landscape and immunotherapy
title_fullStr Analysis of M2 macrophage-associated risk score signature in pancreatic cancer TME landscape and immunotherapy
title_full_unstemmed Analysis of M2 macrophage-associated risk score signature in pancreatic cancer TME landscape and immunotherapy
title_short Analysis of M2 macrophage-associated risk score signature in pancreatic cancer TME landscape and immunotherapy
title_sort analysis of m2 macrophage-associated risk score signature in pancreatic cancer tme landscape and immunotherapy
topic Molecular Biosciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10352656/
https://www.ncbi.nlm.nih.gov/pubmed/37469705
http://dx.doi.org/10.3389/fmolb.2023.1184708
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