Tan Yin Qiu, Li Yun Tao, Yan Teng Feng, Xu Yang, Liu Bao Hui, Yang Ji An, Yang Xue, Chen Qian Xue, Zhang Hong Bo
Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, China.
Department of Neurosurgery, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
Front Immunol. 2020 Dec 21;11:606164. doi: 10.3389/fimmu.2020.606164. eCollection 2020.
The immunotherapy of Glioma has always been a research hotspot. Although tumor associated microglia/macrophages (TAMs) proves to be important in glioma progression and drug resistance, our knowledge about how TAMs influence glioma remains unclear. The relationship between glioma and TAMs still needs further study.
We collected the data of TAMs in glioma from NCBI Gene Expression Omnibus (GEO) that included 20 glioma samples and 15 control samples from four datasets. Six genes were screened from the Differential Expression Gene through Gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, protein-protein interaction (PPI) network and single-cell sequencing analysis. A risk score was then constructed based on the six genes and patients' overall survival rates of 669 patients from The Cancer Genome Atlas (TCGA). The efficacy of the risk score in prognosis and prediction was verified in Chinese Glioma Genome Atlas (CGGA).
Six genes, including CD163, FPR3, LPAR5, P2ry12, PLAUR, SIGLEC1, that participate in signal transduction and plasma membrane were selected. Half of them, like CD163, FPR3, SIGLEC1, were mainly expression in M2 macrophages. FPR3 and SIGLEC1 were high expression genes in glioma associated with grades and IDH status. The overall survival rates of the high risk score group was significantly lower than that of the low risk score group, especially in LGG.
Joint usage of the 6 candidate genes may be an effective method to diagnose and evaluate the prognosis of glioma, especially in Low-grade glioma (LGG).
胶质瘤的免疫治疗一直是研究热点。尽管肿瘤相关小胶质细胞/巨噬细胞(TAMs)在胶质瘤进展和耐药性中被证明很重要,但我们对TAMs如何影响胶质瘤的了解仍不清楚。胶质瘤与TAMs之间的关系仍需进一步研究。
我们从NCBI基因表达综合数据库(GEO)收集了胶质瘤中TAMs的数据,其中包括来自四个数据集的20个胶质瘤样本和15个对照样本。通过基因本体(GO)分析、京都基因与基因组百科全书(KEGG)通路分析、蛋白质-蛋白质相互作用(PPI)网络和单细胞测序分析,从差异表达基因中筛选出6个基因。然后基于这6个基因和来自癌症基因组图谱(TCGA)的669例患者的总生存率构建风险评分。在中国胶质瘤基因组图谱(CGGA)中验证了该风险评分在预后和预测方面的有效性。
选择了6个参与信号转导和质膜的基因,包括CD163、FPR3、LPAR5、P2ry12、PLAUR、SIGLEC1。其中一半,如CD163、FPR3、SIGLEC1,主要在M2巨噬细胞中表达。FPR3和SIGLEC1是与胶质瘤分级和异柠檬酸脱氢酶(IDH)状态相关的高表达基因。高风险评分组的总生存率显著低于低风险评分组,尤其是在低级别胶质瘤(LGG)中。
联合使用这6个候选基因可能是诊断和评估胶质瘤预后的有效方法,尤其是在低级别胶质瘤(LGG)中。