Department of Pharmacology, School of Pharmacy, Qingdao University, Qingdao 266071, China.
Department of Neurosurgery, the Affiliated Hospital of Qingdao University, Qingdao 266003, Shandong Province, China.
Pathol Res Pract. 2022 Dec;240:154225. doi: 10.1016/j.prp.2022.154225. Epub 2022 Nov 16.
Gliomas is the most common type of intracranial primary malignant tumor and it accounts for ∼80% of primary malignant tumors of the central nervous system. At present, surgical resection with adjuvant radiotherapy and temozolomide adjuvant chemotherapy combined with radiotherapy are the only standard treatments for glioma. However, but overall survival of patients is only 15 months. Glioma is resistant to radiotherapy and chemotherapy, and this malignant behavior leads to a high recurrence rate. Therefore, the use of therapeutics is usually ineffective. As a result, patients with glioma do not significantly benefit from standard treatment. There is therefore an urgent need to develop novel diagnostic approaches and, in particular, more effective treatment strategies. The application of gene expression microarrays provides a feasible and effective way to study gliomas. The present study therefore aimed to identify the key protein-coding genes of glioma using bioinformatics methods and thereby search, for novel biomarkers and therapeutic targets for the treatment of glioma. First, mRNA microarray datasets were selected and obtained from the Gene Expression Omnibus database to identify differentially expressed genes (DEGs) between gliomas and normal tissues. The DEGs were clarified using Gene Ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG), Protein-Protein Interaction (PPI) network and statistical analysis. Subsequently, reverse transcription-quantitative PCR (RT-qPCR)and western blot were used to verify the results of the bioinformatics analysis. A total of 400 DEGs were identified in glioma and they were enriched in several cancer-related GO and KEGG pathways. In the PPI network, it was observed that G-protein signal regulatory protein 4 (RGS4), thymidine phosphorylase, collagen type VI alpha-1, Src homology 2 domain-containing transforming protein1(SHC1) and ring finger protein 135 exhibited a strong protein-protein interaction. Furthermore, . Subsequently, brain damaged tissues and glioma cell lines were selected for RT-qPCR and western blotting analysis. The results demonstrated that RGS4 was highly expressed in glioma cell lines. In conclusion, RGS4 may be a key protein-coding gene in glioma. RGS4 should therefore be studied further to verify its feasibility and effectiveness as a potential glioma biomarker and therapeutic target.
神经胶质瘤是颅内原发性恶性肿瘤中最常见的类型,约占中枢神经系统原发性恶性肿瘤的 80%。目前,手术切除联合辅助放疗和替莫唑胺辅助化疗联合放疗是治疗神经胶质瘤的唯一标准治疗方法。然而,患者的总生存时间仅为 15 个月。神经胶质瘤对放疗和化疗具有抗性,这种恶性行为导致复发率很高。因此,治疗药物通常无效。因此,神经胶质瘤患者不能从标准治疗中显著获益。因此,迫切需要开发新的诊断方法,特别是更有效的治疗策略。基因表达微阵列的应用为研究神经胶质瘤提供了一种可行且有效的方法。本研究旨在通过生物信息学方法鉴定神经胶质瘤的关键蛋白编码基因,从而寻找治疗神经胶质瘤的新型生物标志物和治疗靶点。首先,从基因表达综合数据库中选择并获取 mRNA 微阵列数据集,以鉴定神经胶质瘤与正常组织之间的差异表达基因(DEGs)。使用基因本体论(GO)、京都基因与基因组百科全书(KEGG)、蛋白质-蛋白质相互作用(PPI)网络和统计分析来阐明 DEGs。随后,使用逆转录-定量 PCR(RT-qPCR)和蛋白质印迹法验证生物信息学分析的结果。在神经胶质瘤中鉴定出 400 个 DEGs,它们富集在几个与癌症相关的 GO 和 KEGG 通路中。在 PPI 网络中,观察到 G 蛋白信号调节蛋白 4(RGS4)、胸苷磷酸化酶、胶原 VI 型α-1、Src 同源 2 结构域包含转化蛋白 1(SHC1)和环指蛋白 135 表现出强烈的蛋白-蛋白相互作用。此外,使用基因表达微阵列对神经胶质瘤组织和细胞系进行验证。随后,选择脑损伤组织和神经胶质瘤细胞系进行 RT-qPCR 和蛋白质印迹分析。结果表明,RGS4 在神经胶质瘤细胞系中高表达。综上所述,RGS4 可能是神经胶质瘤的关键蛋白编码基因。因此,应进一步研究 RGS4,以验证其作为潜在神经胶质瘤生物标志物和治疗靶点的可行性和有效性。