Gong Xiaoxiang, Liu Lingjuan, Xiong Jie, Li Xingfang, Xu Jie, Xiao Yangyang, Li Jian, Luo Xuemei, Mao Dingan, Liu Liqun
Department of Pediatrics, The Second Xiangya Hospital, Central South University, Changsha 410011, Hunan, China.
Childern's Brain Development and Brain Injury Research Office, The Second Xiangya Hospital, Central South University, Changsha 410011, Hunan, China.
J Oncol. 2021 Mar 13;2021:6620159. doi: 10.1155/2021/6620159. eCollection 2021.
Tumor microenvironment (TME) is closely related to the progression of glioma and the therapeutic effect of drugs on this cancer. The aim of this study was to develop a signature associated with the tumor immune microenvironment using machine learning.
We downloaded the transcriptomic and clinical data of glioma patients from the Chinese Glioma Genome Atlas (CGGA) databases (mRNAseq_693). The single-sample Gene Set Enrichment Analysis (ssGSEA) database was used to quantify the relative abundance of immune cells. We divided patients into two different infiltration groups via unsupervised clustering analysis of immune cells and then selected differentially expressed genes (DEGs) between the two groups. Survival-related genes were determined using Cox regression analysis. We next randomly divided patients into a training set and a testing set at a ratio of 7 : 3. By integrating the DEGs into least absolute shrinkage and selection operator (LASSO) regression analysis in the training set, we were able to construct a 15-gene signature, which was validated in the testing and total sets. We further validated the signature in the mRNAseq_325 dataset of CGGA.
We identified 74 DEGs associated with tumor immune infiltration, 70 of which were significantly associated with overall survival (OS). An immune-related gene signature was established, consisting of 15 key genes: adenosine triphosphate (ATP)-binding cassette subfamily C member 3 (), collagen type IV alpha 1 chain (), podoplanin (), annexin A1 (), , insulin-like growth factor binding protein 2 (), serpin family A member 3 (), CXXC-type zinc finger protein 11 (), junctophilin 3 (), secretogranin III (), secreted protein acidic and rich in cysteine (SPARC)-related modular calcium-binding protein 1 (), Cluster of Differentiation 14 (), , S100 calcium-binding protein A4 (), and transforming growth factor beta 1 (). The OS of patients in the high-risk group was worse than that of patients in the low-risk group. GSEA showed that interleukin-6 ()/Janus kinase ()/signal transducer and activator of transcription () signaling, interferon gamma (IFN-) response, angiogenesis, and coagulation were more highly enriched in the high-risk group and that oxidative phosphorylation was more highly enriched in the low-risk group.
We constructed a stable gene signature associated with immune infiltration to predict the survival rates of glioma patients.
肿瘤微环境（TME）与胶质瘤的进展以及药物对该癌症的治疗效果密切相关。本研究的目的是利用机器学习开发一种与肿瘤免疫微环境相关的特征。
我们从中国胶质瘤基因组图谱（CGGA）数据库（mRNAseq_693）下载了胶质瘤患者的转录组和临床数据。使用单样本基因集富集分析（ssGSEA）数据库来量化免疫细胞的相对丰度。通过对免疫细胞进行无监督聚类分析，我们将患者分为两个不同的浸润组，并选择两组之间的差异表达基因（DEG）。使用Cox回归分析确定与生存相关的基因。接下来，我们以7∶3的比例将患者随机分为训练集和测试集。通过将DEG整合到训练集中的最小绝对收缩和选择算子（LASSO）回归分析中，我们构建了一个由15个基因组成的特征，并在测试集和总集中进行了验证。我们还在CGGA的mRNAseq_325数据集中进一步验证了该特征。
我们鉴定出74个与肿瘤免疫浸润相关的DEG，其中70个与总生存期（OS）显著相关。建立了一个免疫相关基因特征，由15个关键基因组成：三磷酸腺苷（ATP）结合盒亚家族C成员3、IV型胶原α1链、血小板内皮细胞黏附分子、膜联蛋白A1、、胰岛素样生长因子结合蛋白2、丝氨酸蛋白酶抑制剂家族A成员3、CXXC型锌指蛋白11、连接蛋白3、分泌粒蛋白III、富含半胱氨酸的酸性分泌蛋白（SPARC）相关模块化钙结合蛋白1、分化簇14、、S100钙结合蛋白A4和转化生长因子β1。高风险组患者的OS比低风险组患者差。基因集富集分析（GSEA）显示，白细胞介素-6（IL-6）/Janus激酶（JAK）/信号转导子和转录激活子（STAT）信号通路、干扰素γ（IFN-γ）反应、血管生成和凝血在高风险组中富集程度更高，而氧化磷酸化在低风险组中富集程度更高。
我们构建了一个与免疫浸润相关的稳定基因特征，以预测胶质瘤患者的生存率。
