Cheng Quan, Duan Weiwei, He Shiqing, Li Chen, Cao Hui, Liu Kun, Ye Weijie, Yuan Bo, Xia Zhiwei
Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.
National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.
Front Cell Dev Biol. 2021 Jul 16;9:686909. doi: 10.3389/fcell.2021.686909. eCollection 2021.
The tumor immune microenvironment significantly affects tumor occurrence, progression, and prognosis, but its impact on the prognosis of low-grade glioma (LGG) patients with epilepsy has not been reported. Hence, the purpose of this study is to explore its effect on LGG patients with epilepsy.
The data of LGG patients derived from the TCGA database. The level of immune cell infiltration and the proportion of 22 immune cells were evaluated by ESTIMATE and CIBERSORT algorithms, respectively. The Cox and LASSO regression analysis was adopted to determine the DEGs, and further established the clustering and risk score models. The association between genomic alterations and risk score was investigated using CNV and somatic mutation data. GSVA was adopted to identify the immunological pathways, immune infiltration and inflammatory profiles related to the signature genes. The Tumor Immune Dysfunction and Exclusion (TIDE) algorithm and GDSC database were used to predict the patient's response to immunotherapy and chemotherapy, respectively.
The prognosis of LGG patients with epilepsy was associated with the immune score. Three prognostic DEGs (ABCC3, PDPN, and INA) were screened out. The expression of signature genes was regulated by DNA methylation. The clustering and risk score models could stratify glioma patients into distinct prognosis groups. The risk score was an independent predictor in prognosis, with a high risk-score indicating a poor prognosis, more malignant clinicopathological and genomic aberration features. The nomogram had the better predictive ability. Patients at high risk had a higher level of macrophage infiltration and increased inflammatory activities associated with T cells and macrophages. While the higher percentage of NK CD56bright cell and more active inflammatory activity associated with B cell were present in the low-risk patients. The signature genes participated in the regulation of immune-related pathways, such as IL6-JAK-STAT3 signaling, IFN-α response, IFN-γ response, and TNFA-signaling-via-NFKB pathways. The high-risk patients were more likely to benefit from anti-PD1 and temozolomide (TMZ) treatment.
An immune-related gene signature was established based on ABCC3, PDPN, and INA, which can be used to predict the prognosis, immune infiltration status, immunotherapy and chemotherapy response of LGG patients with epilepsy.
肿瘤免疫微环境显著影响肿瘤的发生、发展和预后，但尚未见其对伴有癫痫的低级别胶质瘤（LGG）患者预后影响的报道。因此，本研究旨在探讨其对伴有癫痫的LGG患者的影响。
LGG患者的数据来源于TCGA数据库。分别采用ESTIMATE和CIBERSORT算法评估免疫细胞浸润水平和22种免疫细胞的比例。采用Cox和LASSO回归分析确定差异表达基因（DEGs），并进一步建立聚类和风险评分模型。利用拷贝数变异（CNV）和体细胞突变数据研究基因组改变与风险评分之间的关联。采用基因集变异分析（GSVA）识别与特征基因相关的免疫途径、免疫浸润和炎症特征。分别使用肿瘤免疫功能障碍与排除（TIDE）算法和癌症药物敏感性基因组学（GDSC）数据库预测患者对免疫治疗和化疗的反应。
伴有癫痫的LGG患者的预后与免疫评分相关。筛选出3个预后相关的DEGs（ABCC3、PDPN和INA）。特征基因的表达受DNA甲基化调控。聚类和风险评分模型可将胶质瘤患者分为不同的预后组。风险评分是预后的独立预测因子，高风险评分表明预后不良，临床病理和基因组畸变特征更具恶性。列线图具有更好的预测能力。高风险患者巨噬细胞浸润水平较高，与T细胞和巨噬细胞相关的炎症活动增加。而低风险患者中NK CD56bright细胞百分比更高，与B细胞相关的炎症活动更活跃。特征基因参与免疫相关途径的调控，如IL6-JAK-STAT3信号通路、IFN-α反应、IFN-γ反应和TNFA通过NFKB信号通路。高风险患者更可能从抗PD1和替莫唑胺（TMZ）治疗中获益。
基于ABCC3、PDPN和INA建立了一个免疫相关基因特征，可用于预测伴有癫痫的LGG患者的预后、免疫浸润状态、免疫治疗和化疗反应。
