背景:胶质母细胞瘤(GBM)是一种恶性脑肿瘤,经常与其他中枢神经系统(CNS)疾病一起发生。GBM细胞的分泌组包含释放到细胞外空间的各种蛋白质,影响肿瘤微环境。这些蛋白质可以作为GBM的潜在生物标志物,因为它们参与关键的生物过程。探索GBM研究中的分泌组生物标志物代表了一种前沿策略,具有提高诊断精度的巨大潜力,治疗监测,并最终改善这种具有挑战性的脑癌患者的预后。
目的:本研究旨在通过生物信息学分析探讨分泌组生物标志物及其通路在GBM中的作用。
结果:使用来自基因表达综合和癌症基因组图谱数据集的数据-其中分析了健康和癌症样品-我们使用定量分析框架来鉴定差异表达基因(DEG)和可能与GBM相关的细胞信号传导途径。然后,我们在发现疾病-基因连接网络和信号通路后,进行了基因本体论研究和枢纽蛋白鉴定,以评估这些DEGs的作用.使用GEPIA比例危险模型和Kaplan-Meier估计器,我们扩大了我们的分析范围,以确定可能在GBM患者的进展和生存中起作用的重要基因.总的来说,890DEG,包括475和415上调和下调,分别。我们的结果显示SQLE,DHCR7,δ-1磷脂酶C(PLCD1),和MINPP1基因高表达,烯醇化酶2(ENO2)和己糖激酶-1(HK1)基因低表达。
结论:因此,我们的发现提示了影响GBM发育发生的新机制,增长,和/或建立,也可以作为GBM预后的分泌性生物标志物和可能的治疗靶标。所以,在这一领域的持续研究可能会发现治疗干预的新途径,并有助于正在进行的努力,以有效地打击GBM。
BACKGROUND: Glioblastoma (GBM) is a malignant brain tumor that frequently occurs alongside other central nervous system (CNS) conditions. The
secretome of GBM cells contains a diverse array of proteins released into the extracellular space, influencing the tumor microenvironment. These proteins can serve as potential biomarkers for GBM due to their involvement in key biological processes, exploring the
secretome biomarkers in GBM research represents a cutting-edge strategy with significant potential for advancing diagnostic precision, treatment monitoring, and ultimately improving outcomes for patients with this challenging brain cancer.
OBJECTIVE: This study was aimed to investigate the roles of
secretome biomarkers and their pathwayes in GBM through bioinformatics analysis.
RESULTS: Using data from the Gene Expression Omnibus and the Cancer Genome Atlas datasets-where both healthy and cancerous samples were analyzed-we used a quantitative analytical framework to identify differentially expressed genes (DEGs) and cell signaling pathways that might be related to GBM. Then, we performed gene ontology studies and hub protein identifications to estimate the roles of these DEGs after finding disease-gene connection networks and signaling pathways. Using the GEPIA Proportional Hazard Model and the Kaplan-Meier estimator, we widened our analysis to identify the important genes that may play a role in both progression and the survival of patients with GBM. In total, 890 DEGs, including 475 and 415 upregulated and downregulated were identified, respectively. Our results revealed that SQLE, DHCR7, delta-1 phospholipase C (PLCD1), and MINPP1 genes are highly expressed, and the Enolase 2 (ENO2) and hexokinase-1 (HK1) genes are low expressions.
CONCLUSIONS: Hence, our findings suggest novel mechanisms that affect the occurrence of GBM development, growth, and/or establishment and may also serve as secretory biomarkers for GBM prognosis and possible targets for therapy. So, continued research in this field may uncover new avenues for therapeutic interventions and contribute to the ongoing efforts to combat GBM effectively.