The molecular feature of macrophages in tumor immune microenvironment of glioma patients.

Background: Gliomas are one of the most common types of primary tumors in central nervous system. Previous studies have found that macrophages actively participate in tumor growth.
Methods: Weighted gene co-expression network analysis was used to identify meaningful macrophage-related gene genes for clustering. Pamr, SVM, and neural network were applied for validating clustering results. Somatic mutation and methylation were used for defining the features of identified clusters. Differentially expressed genes (DEGs) between the stratified groups after performing elastic regression and principal component analyses were used for the construction of MScores. The expression of macrophage-specific genes were evaluated in tumor microenvironment based on single cell sequencing analysis. A total of 2365 samples from 15 glioma datasets and 5842 pan-cancer samples were used for external validation of MScore.
Results: Macrophages were identified to be negatively associated with the survival of glioma patients. Twenty-six macrophage-specific DEGs obtained by elastic regression and PCA were highly expressed in macrophages at single-cell level. The prognostic value of MScores in glioma was validated by the active proinflammatory and metabolic profile of infiltrating microenvironment and response to immunotherapies of samples with this signature. MScores managed to stratify patient survival probabilities in 15 external glioma datasets and pan-cancer datasets, which predicted worse survival outcome. Sequencing data and immunohistochemistry of Xiangya glioma cohort confirmed the prognostic value of MScores. A prognostic model based on MScores demonstrated high accuracy rate.
Conclusion: Our findings strongly support a modulatory role of macrophages, especially M2 macrophages in glioma progression and warrants further experimental studies.
Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(© 2021 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.)