Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.
Clinic Medicine of 5-Year Program, Xiangya School of Medicine, Central South University, Changsha, China.
Front Immunol. 2021 Sep 13;12:731751. doi: 10.3389/fimmu.2021.731751. eCollection 2021.
Gliomas are a type of malignant central nervous system tumor with poor prognosis. Molecular biomarkers of gliomas can predict glioma patient's clinical outcome, but their limitations are also emerging. C-X-C motif chemokine ligand family plays a critical role in shaping tumor immune landscape and modulating tumor progression, but its role in gliomas is elusive. In this work, samples of TCGA were treated as the training cohort, and as for validation cohort, two CGGA datasets, four datasets from GEO database, and our own clinical samples were enrolled. Consensus clustering analysis was first introduced to classify samples based on CXCL expression profile, and the support vector machine was applied to construct the cluster model in validation cohort based on training cohort. Next, the elastic net analysis was applied to calculate the risk score of each sample based on CXCL expression. High-risk samples associated with more malignant clinical features, worse survival outcome, and more complicated immune landscape than low-risk samples. Besides, higher immune checkpoint gene expression was also noticed in high-risk samples, suggesting CXCL may participate in tumor evasion from immune surveillance. Notably, high-risk samples also manifested higher chemotherapy resistance than low-risk samples. Therefore, we predicted potential compounds that target high-risk samples. Two novel drugs, LCL-161 and ADZ5582, were firstly identified as gliomas' potential compounds, and five compounds from PubChem database were filtered out. Taken together, we constructed a prognostic model based on CXCL expression, and predicted that CXCL may affect tumor progression by modulating tumor immune landscape and tumor immune escape. Novel potential compounds were also proposed, which may improve malignant glioma prognosis.
神经胶质瘤是一种预后不良的恶性中枢神经系统肿瘤。神经胶质瘤的分子生物标志物可以预测神经胶质瘤患者的临床预后,但它们的局限性也逐渐显现出来。C-X-C 基序趋化因子配体家族在塑造肿瘤免疫景观和调节肿瘤进展方面起着关键作用,但它在神经胶质瘤中的作用仍不清楚。在这项工作中,TCGA 样本被视为训练队列,而 CGGA 的两个数据集、GEO 数据库的四个数据集和我们自己的临床样本被纳入验证队列。首先引入共识聚类分析根据 CXCL 表达谱对样本进行分类,然后应用支持向量机根据训练队列在验证队列中构建聚类模型。接下来,应用弹性网络分析根据 CXCL 表达计算每个样本的风险评分。与低风险样本相比,高风险样本与更恶性的临床特征、更差的生存结果和更复杂的免疫景观相关。此外,高风险样本中还观察到更高的免疫检查点基因表达,表明 CXCL 可能参与肿瘤逃避免疫监视。值得注意的是,高风险样本也表现出比低风险样本更高的化疗耐药性。因此,我们预测了针对高风险样本的潜在化合物。首次鉴定出两种新型药物 LCL-161 和 ADZ5582 作为神经胶质瘤的潜在化合物,并从 PubChem 数据库中筛选出五种化合物。总之,我们构建了一个基于 CXCL 表达的预后模型,并预测 CXCL 可能通过调节肿瘤免疫景观和肿瘤免疫逃逸来影响肿瘤进展。还提出了新的潜在化合物,可能改善恶性神经胶质瘤的预后。