Abstract LB528: A microglia associated gene signature predicts survival risk in glioma patients

Abstract

Abstract Background: Gliomas are common tumors that affect the brain and spinal cord. A complex tumor microenvironment is one of the leading reasons for a poor prognosis in glioma patients. Microglia, as part of the immune microenvironment of gliomas, may facilitate glioma growth and invasion. However, the correlation between the microglia abundance and glioma prognosis has not been clarified. Method: Our goal was to examine the relationship between microglia abundance and glioma prognosis. We analyzed the single-cell RNA sequences of human and mouse glioma cells to identify microglia marker genes. Then, we built a LASSO Cox regression model of microglia abundance signatures in gliomas. The Cancer Genome Atlas (TCGA) dataset (Low-grade gliomas, LGG) was used as the training cohort. The Repository of Molecular Brain Neoplasia Data (REMBRANDT) dataset and Chinese Glioma Genome Atlas (CGGA) dataset was used to validate the model as the validation cohorts. Findings: Overall survival was significantly lower in those with a high level of microglia infiltration. Additionally, we found that microglia could interact with the tumor microenvironment and genomic features of gliomas, making microglia abundance negatively associated with the prognosis of glioma patients. Microglia abundance was positively correlated with immune genes expression and immune-related pathways. By applying our LASSO Cox regression model to gliomas, we found that patients with high-risk scores had significantly shorter overall survival (OS) than those with low-risk scores. Conclusions: Taken together, our findings suggest that microglia abundance may be negatively associated with survival in gliomas. Based on the novel microglia abundance signatures, we developed a high accuracy LASSO Cox regression model. In this study, we demonstrated that the model accurately predicted the prognosis of glioma patients and could offer new therapeutic targets for microglial-directed therapies. Keywords: Gliomas; Microglia abundance; LASSO Cox regression; TCGA; Signature.- 1 - Citation Format: Chongming Jiang, Evelien Schaafsma, Yanding Zhao, Thinh Nguyen, Kenneth Zhu, Chao Cheng. A microglia associated gene signature predicts survival risk in glioma patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB528.