Kai Xiao1, Shushan Zhao2, Jian Yuan1, Yimin Pan1, Ya Song2, Lanhua Tang3,4. 1. Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, People's Republic of China. 2. Department of Orthopedics, Xiangya Hospital, Central South University, Changsha, People's Republic of China. 3. Department of Oncology, Xiangya Hospital, Central South University, Changsha, People's Republic of China. 4. National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, People's Republic of China.
Abstract
OBJECTIVES: To identify the molecular subtypes of glioblastoma multiforme (GBM) related to M2 macrophage-based prognostic genes, then to preliminarily explore their biological functions and construct immunotherapy response gene models. MATERIAL AND METHODS: We used R language to analyze GBM microarray data, and other tools, including xCell and CIBERSORTx, to identify subtypes of GBM that related to M2 macrophages. The process started with the exploration of biological functions of the two subtypes by pathway analyses and GSEA, and continued with a combined procedure of constructing an M2 macrophage-related prognostic gene model and exploring the immune treatment response for GBM. RESULTS: A high abundance of M2 macrophages in GBM was associated with poor prognosis. According to M2 macrophage-related prognostic genes, GBM was divided into two subtypes (cluster A and cluster B). The differential gene enrichment analysis of the two clusters showed that cluster A was less enriched in M2 macrophages and had immunopotential. The M2score, which was constructed based on M2 macrophage-related prognostic genes, was not only related to the survival and prognosis of patients with GBM, but also predictive of the effectiveness of immunotherapy in these patients. This result has been effectively verified in an external data set. CONCLUSION: GBM was successfully divided into two subtypes according to M2-macrophage-related prognostic genes. In GBM, a high M2score may indicate better clinical outcome and enhancement of the immunotherapy response.
OBJECTIVES: To identify the molecular subtypes of glioblastoma multiforme (GBM) related to M2 macrophage-based prognostic genes, then to preliminarily explore their biological functions and construct immunotherapy response gene models. MATERIAL AND METHODS: We used R language to analyze GBM microarray data, and other tools, including xCell and CIBERSORTx, to identify subtypes of GBM that related to M2 macrophages. The process started with the exploration of biological functions of the two subtypes by pathway analyses and GSEA, and continued with a combined procedure of constructing an M2 macrophage-related prognostic gene model and exploring the immune treatment response for GBM. RESULTS: A high abundance of M2 macrophages in GBM was associated with poor prognosis. According to M2 macrophage-related prognostic genes, GBM was divided into two subtypes (cluster A and cluster B). The differential gene enrichment analysis of the two clusters showed that cluster A was less enriched in M2 macrophages and had immunopotential. The M2score, which was constructed based on M2 macrophage-related prognostic genes, was not only related to the survival and prognosis of patients with GBM, but also predictive of the effectiveness of immunotherapy in these patients. This result has been effectively verified in an external data set. CONCLUSION: GBM was successfully divided into two subtypes according to M2-macrophage-related prognostic genes. In GBM, a high M2score may indicate better clinical outcome and enhancement of the immunotherapy response.
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