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Immune‐related matrisomes are potential biomarkers to predict the prognosis and immune microenvironment of glioma patients

The extracellular matrix (ECM) plays a vital role in the progression and metastasis of glioma and is an important part of the tumor microenvironment. The matrisome is composed of ECM components and related proteins. There have been several studies on the effects of matrisomes on the glioma immune mi...

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Detalles Bibliográficos
Autores principales: Yu, Hao, Wang, Minjie, Wang, Xuan, Jiang, Xiaobing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9900094/
https://www.ncbi.nlm.nih.gov/pubmed/36560848
http://dx.doi.org/10.1002/2211-5463.13541
Descripción
Sumario:The extracellular matrix (ECM) plays a vital role in the progression and metastasis of glioma and is an important part of the tumor microenvironment. The matrisome is composed of ECM components and related proteins. There have been several studies on the effects of matrisomes on the glioma immune microenvironment, but most of these studies were performed on individual glioma immune‐related matrisomes rather than integral analysis. Hence, an overall analysis of all potential immune‐related matrisomes in gliomas is needed. Here, we divided 667 glioma patients in The Cancer Genome Atlas (TCGA) database into low, moderate, and high immune infiltration groups. Immune‐related matrisomes differentially expressed among the three groups were analyzed, and a risk signature was established. Eight immune‐related matrisomes were screened, namely, LIF, LOX, MMP9, S100A4, SRPX2, SLIT1, SMOC1, and TIMP1. Kaplan–Meier analysis, operating characteristic curve analysis, and nomogram were constructed to analyze the relationships between risk signatures and the prognosis of glioma patients. The risk signature was significantly correlated with the overall survival of glioma patients. Both high‐ and low‐risk signatures were also associated with some immune checkpoints. In addition, analysis of somatic mutations and anti‐PD1/L1 immunotherapy responses in the high‐ and low‐risk groups showed that the high‐risk group had worse prognosis and a higher response to anti‐PD1/L1 immunotherapy. Our analysis of immune‐related matrisomes may improve understanding of the characteristics of the glioma immune microenvironment and provide direction for glioma immunotherapy development in the future.