Revealing the role of SPP1 + macrophages in glioma prognosis and therapeutic targeting by investigating tumor-associated macrophage landscape in grade 2 and 3 gliomas

• Published in: Cell & bioscience 2024-03, Vol.14 (1), p.37-37, Article 37
• Main Authors: Tang, Wenshu, Lo, Cario W S, Ma, Wei, Chu, Annie T W, Tong, Amy H Y, Chung, Brian H Y
• Format: Article
• Language: English
• Subjects: Brain cancer; Brain tumors; Cells; Chemokines; Clinical trials; Cytokines; Development and progression; EGFR; Epidermal growth factor; Epidermal growth factor receptors; Genes; Genomics; Glioma; Glioma cells; Gliomas; Growth factors; Health aspects; Immune response; Immune suppression; Immunotherapy; Kinases; Ligands; Lymphocytes; Lymphocytes T; Macrophages; Malignancy; Medical prognosis; Mutation; Patients; Prognosis; Risk assessment; RNA; T cells; TAM-SPP1; Therapeutic targets; Transcriptomics; Tumor cells; Tumor microenvironment; Tumor-associated macrophages; Tumors; Vaccines
• ISSN: 2045-3701; 2045-3701
• DOI: 10.1186/s13578-024-01218-4

Glioma is a highly heterogeneous brain tumor categorized into World Health Organization (WHO) grades 1-4 based on its malignancy. The suppressive immune microenvironment of glioma contributes significantly to unfavourable patient outcomes. However, the cellular composition and their complex interplays within the glioma environment remain poorly understood, and reliable prognostic markers remain elusive. Therefore, in-depth exploration of the tumor microenvironment (TME) and identification of predictive markers are crucial for improving the clinical management of glioma patients. Our analysis of single-cell RNA-sequencing data from glioma samples unveiled the immunosuppressive role of tumor-associated macrophages (TAMs), mediated through intricate interactions with tumor cells and lymphocytes. We also discovered the heterogeneity within TAMs, among which a group of suppressive TAMs named TAM-SPP1 demonstrated a significant association with Epidermal Growth Factor Receptor (EGFR) amplification, impaired T cell response and unfavourable patient survival outcomes. Furthermore, by leveraging genomic and transcriptomic data from The Cancer Genome Atlas (TCGA) dataset, two distinct molecular subtypes with a different constitution of TAMs, EGFR status and clinical outcomes were identified. Exploiting the molecular differences between these two subtypes, we developed a four-gene-based prognostic model. This model displayed strong associations with an elevated level of suppressive TAMs and could be used to predict anti-tumor immune response and prognosis in glioma patients. Our findings illuminated the molecular and cellular mechanisms that shape the immunosuppressive microenvironment in gliomas, providing novel insights into potential therapeutic targets. Furthermore, the developed prognostic model holds promise for predicting immunotherapy response and assisting in more precise risk stratification for glioma patients.