用于预测胶质瘤患者预后和免疫治疗反应的特征识别
Identification of a Signature for Predicting Prognosis and Immunotherapy Response in Patients with Glioma.
作者信息
Zong Wei-Feng, Liu Cui, Zhang Yi, Zhang Suo-Jun, Qu Wen-Sheng, Luo Xiang
机构信息
Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.
Neurorehabilitation Center, Beijing Rehabilitation Hospital of Capital Medical University, Beijing 100144, China.
出版信息
J Oncol. 2022 Aug 29;2022:8615949. doi: 10.1155/2022/8615949. eCollection 2022.
Glioma is a deadly tumor that accounts for the vast majority of brain tumors. Thus, it is important to elucidate the molecular pathogenesis and potential diagnostic and prognostic biomarkers of glioma. In the present study, gene expression profiles of GSE2223 were obtained from the Gene Expression Omnibus (GEO) database. Core modules and hub genes related to glioma were identified using weighted gene coexpression network analysis (WGCNA) and protein-protein interaction (PPI) network analysis of differentially expressed genes (DEGs). After a series of database screening tests, we identified 11 modules during glioma progression, followed by six hub genes (RAB3A, TYROBP, SYP, CAMK2A, VSIG4, and GABRA1) that can predict the prognosis of glioma and were validated in glioma tissues by qRT-PCR. The CIBERSORT algorithm was used to analyze the difference of immune cell infiltration between the glioma and control groups. Finally, Identification VSIG4 for immunotherapy response in patients with glioma demonstrating utility for immunotherapy research.
胶质瘤是一种致命的肿瘤，占脑肿瘤的绝大多数。因此，阐明胶质瘤的分子发病机制以及潜在的诊断和预后生物标志物非常重要。在本研究中，从基因表达综合数据库（GEO）中获取了GSE2223的基因表达谱。通过对差异表达基因（DEG）进行加权基因共表达网络分析（WGCNA）和蛋白质-蛋白质相互作用（PPI）网络分析，确定了与胶质瘤相关的核心模块和枢纽基因。经过一系列数据库筛选测试，我们在胶质瘤进展过程中确定了11个模块，随后确定了6个枢纽基因（RAB3A、TYROBP、SYP、CAMK2A、VSIG4和GABRA1），它们可以预测胶质瘤的预后，并通过qRT-PCR在胶质瘤组织中得到验证。使用CIBERSORT算法分析胶质瘤组和对照组之间免疫细胞浸润的差异。最后，鉴定出VSIG4对胶质瘤患者免疫治疗反应的效用，证明其在免疫治疗研究中的实用性。
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