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Single‐cell RNA sequencing identifies macrophage signatures correlated with clinical features and tumour microenvironment in meningiomas

Overview of attention for article published in IET Systems Biology, July 2023
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  • In the top 25% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#2 of 124)
  • High Attention Score compared to outputs of the same age (87th percentile)

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Title
Single‐cell RNA sequencing identifies macrophage signatures correlated with clinical features and tumour microenvironment in meningiomas
Published in
IET Systems Biology, July 2023
DOI 10.1049/syb2.12074
Pubmed ID
Authors

Xiaowei Zhang

Abstract

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. 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. 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. 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.

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Geographical breakdown

Country Count As %
Unknown 2 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 1 50%
Unknown 1 50%
Readers by discipline Count As %
Unspecified 1 50%
Unknown 1 50%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 12. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 August 2023.
All research outputs
#2,883,556
of 24,185,663 outputs
Outputs from IET Systems Biology
#2
of 124 outputs
Outputs of similar age
#22,250
of 176,436 outputs
Outputs of similar age from IET Systems Biology
#1
of 3 outputs
Altmetric has tracked 24,185,663 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 124 research outputs from this source. They receive a mean Attention Score of 1.5. This one has done particularly well, scoring higher than 99% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 176,436 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 87% of its contemporaries.
We're also able to compare this research output to 3 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them