Identification of 3 subpopulations of tumor-infiltrating immune cells for malignant transformation of low-grade glioma
Identification of 3 subpopulations of tumor-infiltrating immune cells for malignant transformation of low-grade glioma
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低级别胶质瘤恶性转化中肿瘤浸润免疫细胞的 3 个亚群的鉴定
DOI：
10.1186/s12935-019-0972-1
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发表时间：
2019-10-11
影响因子：
5.8
通讯作者：
Wang, Huibo
中科院分区：
文献类型：
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作者：
Lu, Jiacheng;Li, Hailin;Wang, Huibo
背景肿瘤浸润免疫细胞（TIICs）与胶质瘤的临床预后密切相关。然而，以前的研究不能解释从低级别胶质瘤（LGG）到高级别胶质瘤（HGG）的恶性转化（MT）中免疫反应的多样性。方法从TCGA和CGGA数据库中获取转录组水平、基因组图谱及其与临床实践的关系。使用“通过估计RNA转录物的相对子集的细胞类型鉴定（CIBERSORT）”算法来估计22种免疫细胞类型的分数。采用随机数表法将TCGA和CGGA集分为实验集（n = 174）和验证集（n = 74）。对22个TIIC在LGG中的MT值进行单因素和多因素分析。绘制ROC曲线，计算曲线下面积（AUC）和临界值。结果TIIC之间存在组内和组间异质性。几种TIIC与肿瘤分级、分子亚型和存活率显著相关。筛选出T滤泡辅助（TFH）细胞、活化的NK细胞和M0巨噬细胞是LGG中MT的独立预测因子，并形成免疫风险评分（IRS）（AUC = 0.732，p < 0.001，95%CI 0.657-0.808截止值= 0.191）。此外，通过验证组、免疫组织化学（IHC）和功能富集分析来验证IRS模型。结论所提出的IRS模型为预测MT从LGG到HGG提供了有希望的新特征，并可能在未来几年内为胶质瘤免疫治疗研究带来更好的设计。
Background Tumor-infiltrating immune cells (TIICs) are highly relevant to clinical outcome of glioma. However, previous studies cannot account for the diverse functions that make up the immune response in malignant transformation (MT) from low-grade glioma (LGG) to high-grade glioma (HGG). Methods Transcriptome level, genomic profiles and its relationship with clinical practice were obtained from TCGA and CGGA database. The "Cell type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT)" algorithm was used to estimate the fraction of 22 immune cell types. We divided the TCGA and CGGA set into an experiment set (n = 174) and a validation set (n = 74) by random number table method. Univariate and multivariate analyses were performed to evaluate the 22 TIICs' value for MT in LGG. ROC curve was plotted to calculate area under curve (AUC) and cut-off value. Results Heterogeneity between TIICs exists in both intra- and inter-groups. Several TIICs are notably associated with tumor grade, molecular subtypes and survival. T follicular helper (TFH) cells, activated NK Cells and M0 macrophages were screened out to be independent predictors for MT in LGG and formed an immune risk score (IRS) (AUC = 0.732, p < 0.001, 95% CI 0.657-0.808 cut-off value = 0.191). In addition, the IRS model was validated by validation group, Immunohistochemistry (IHC) and functional enrichment analyses. Conclusions The proposed IRS model provides promising novel signatures for predicting MT from LGG to HGG and may bring a better design of glioma immunotherapy studies in years to come.