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Single‐cell RNA sequencing identifies macrophage signatures correlated with clinical features and tumour microenvironment in meningiomas
BACKGROUND: Meningiomas are common primary brain tumours, with macrophages playing a crucial role in their development and progression. This study aims to identify module genes correlated with meningioma‐associated macrophages and analyse their correlation with clinical features and immune infiltrat...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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John Wiley and Sons Inc.
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10579993/ https://www.ncbi.nlm.nih.gov/pubmed/37515398 http://dx.doi.org/10.1049/syb2.12074 |
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author | Zhang, Xiaowei |
author_facet | Zhang, Xiaowei |
author_sort | Zhang, Xiaowei |
collection | PubMed |
description | BACKGROUND: Meningiomas are common primary brain tumours, with macrophages playing a crucial role in their development and progression. This study aims to identify module genes correlated with meningioma‐associated macrophages and analyse their correlation with clinical features and immune infiltration. METHODS: We analysed single‐cell RNA sequencing (scRNA‐seq) data from two paired meningioma and normal meninges to identify meningioma‐associated macrophages. High‐dimensional weighted gene co‐expression network analysis (hdWGCNA) was employed to identify module genes linked to these macrophages, followed by functional enrichment and pseudotime trajectory analyses. A machine learning‐based model using the module genes was developed to predict tumour grades. Finally, meningiomas were classified into two molecular subtypes based on the module genes, followed by a comparison of clinical characteristics and immune cell infiltration. RESULTS: Meningiomas exhibited a significantly higher proportion of macrophages than normal meninges, including novel macrophage clusters referred to as meningioma‐associated macrophages. The hdWGCNA analysis of macrophages within meningiomas unveiled 12 distinct modules, with the blue, black, and turquoise modules closely correlated with the meningioma‐associated macrophages. Hub genes within these modules were enriched in immune regulation, cellular communication, and metabolism pathways. Machine learning analysis identified 13 module genes (RSBN1, TIPRL, ATIC, SPP1, MALSU1, CDK1, MGP, DDIT3, SUPT16H, NFKBIA, SRSF5, ATXN2L, and UBB) strongly correlated with meningioma grade and constructed a predictive model with high accuracy and robustness. Based on the module genes, meningiomas were classified into two subtypes with distinct clinical and tumour microenvironment characteristics. CONCLUSIONS: Our findings provide insights into the molecular characteristics underlying macrophage infiltration in meningiomas. The molecular signatures of macrophages demonstrate correlations with clinical features and immune cell infiltration in meningiomas. |
format | Online Article Text |
id | pubmed-10579993 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-105799932023-10-18 Single‐cell RNA sequencing identifies macrophage signatures correlated with clinical features and tumour microenvironment in meningiomas Zhang, Xiaowei IET Syst Biol Original Research BACKGROUND: Meningiomas are common primary brain tumours, with macrophages playing a crucial role in their development and progression. This study aims to identify module genes correlated with meningioma‐associated macrophages and analyse their correlation with clinical features and immune infiltration. METHODS: We analysed single‐cell RNA sequencing (scRNA‐seq) data from two paired meningioma and normal meninges to identify meningioma‐associated macrophages. High‐dimensional weighted gene co‐expression network analysis (hdWGCNA) was employed to identify module genes linked to these macrophages, followed by functional enrichment and pseudotime trajectory analyses. A machine learning‐based model using the module genes was developed to predict tumour grades. Finally, meningiomas were classified into two molecular subtypes based on the module genes, followed by a comparison of clinical characteristics and immune cell infiltration. RESULTS: Meningiomas exhibited a significantly higher proportion of macrophages than normal meninges, including novel macrophage clusters referred to as meningioma‐associated macrophages. The hdWGCNA analysis of macrophages within meningiomas unveiled 12 distinct modules, with the blue, black, and turquoise modules closely correlated with the meningioma‐associated macrophages. Hub genes within these modules were enriched in immune regulation, cellular communication, and metabolism pathways. Machine learning analysis identified 13 module genes (RSBN1, TIPRL, ATIC, SPP1, MALSU1, CDK1, MGP, DDIT3, SUPT16H, NFKBIA, SRSF5, ATXN2L, and UBB) strongly correlated with meningioma grade and constructed a predictive model with high accuracy and robustness. Based on the module genes, meningiomas were classified into two subtypes with distinct clinical and tumour microenvironment characteristics. CONCLUSIONS: Our findings provide insights into the molecular characteristics underlying macrophage infiltration in meningiomas. The molecular signatures of macrophages demonstrate correlations with clinical features and immune cell infiltration in meningiomas. John Wiley and Sons Inc. 2023-07-29 /pmc/articles/PMC10579993/ /pubmed/37515398 http://dx.doi.org/10.1049/syb2.12074 Text en © 2023 The Authors. IET Systems Biology published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. |
spellingShingle | Original Research Zhang, Xiaowei Single‐cell RNA sequencing identifies macrophage signatures correlated with clinical features and tumour microenvironment in meningiomas |
title | Single‐cell RNA sequencing identifies macrophage signatures correlated with clinical features and tumour microenvironment in meningiomas |
title_full | Single‐cell RNA sequencing identifies macrophage signatures correlated with clinical features and tumour microenvironment in meningiomas |
title_fullStr | Single‐cell RNA sequencing identifies macrophage signatures correlated with clinical features and tumour microenvironment in meningiomas |
title_full_unstemmed | Single‐cell RNA sequencing identifies macrophage signatures correlated with clinical features and tumour microenvironment in meningiomas |
title_short | Single‐cell RNA sequencing identifies macrophage signatures correlated with clinical features and tumour microenvironment in meningiomas |
title_sort | single‐cell rna sequencing identifies macrophage signatures correlated with clinical features and tumour microenvironment in meningiomas |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10579993/ https://www.ncbi.nlm.nih.gov/pubmed/37515398 http://dx.doi.org/10.1049/syb2.12074 |
work_keys_str_mv | AT zhangxiaowei singlecellrnasequencingidentifiesmacrophagesignaturescorrelatedwithclinicalfeaturesandtumourmicroenvironmentinmeningiomas |