通过综合调控模型解读糖基化驱动的胶质母细胞瘤预后见解和治疗前景

Xingyi Jin, Zhuo Chen, Hang Zhao
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摘要

多形性胶质母细胞瘤的肿瘤发生和发展与糖基化修饰有关,糖基化修饰是常见的蛋白质翻译后修饰。糖基转移酶发育异常会导致不规则的糖基化模式,这对胶质母细胞瘤的预后具有临床意义。通过利用单细胞和大容量数据,我们开发了一套评估GB糖基化水平的评分系统。此外,我们还创建了一个基于糖基化的特征来预测 GB 的预后和治疗反应性。这项研究开发了一个包含九个关键基因的糖基化模型。这一风险评估工具有效地将 GB 患者分为两个不同的组别。通过ROC分析、RMST和Kaplan-Meier(KM)生存分析进行的广泛验证强调了该模型强大的预测能力。此外,还构建了一个提名图来预测特定时间间隔的存活率。研究发现,低风险组和高危组之间的免疫细胞浸润存在巨大差异,其特点是免疫细胞丰度不同和免疫评分升高。值得注意的是,糖模型预测了对免疫检查点抑制剂和药物疗法的不同反应,高风险组偏爱免疫检查点抑制剂,并对药物疗法表现出卓越的反应。此外,研究还发现了两个潜在的药物靶点,并利用连接图分析确定了有前景的治疗药物。氯法拉滨和 YM155 被确定为治疗高风险 GB 的有效候选药物。我们精心设计的糖模型通过计算风险评分有效地区分了患者,准确地预测了 GB 的预后,并在确定治疗 GB 的新型免疫疗法和化疗策略的同时,显著增强了预后评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deciphering glycosylation-driven prognostic insights and therapeutic prospects in glioblastoma through a comprehensive regulatory model
The oncogenesis and development of glioblastoma multiforme have been linked to glycosylation modifications, which are common post-translational protein modifications. Abnormal glycosyltransferase development leads to irregular glycosylation patterns, which hold clinical significance for GB prognosis. By utilizing both single-cell and bulk data, we developed a scoring system to assess glycosylation levels in GB. Moreover, a glycosylation-based signature was created to predict GB outcomes and therapy responsiveness. The study led to the development of an glyco-model incorporating nine key genes. This risk assessment tool effectively stratified GB patients into two distinct groups. Extensive validation through ROC analysis, RMST, and Kaplan-Meier (KM) survival analysis emphasized the model’s robust predictive capabilities. Additionally, a nomogram was constructed to predict survival rates at specific time intervals. The research revealed substantial disparities in immune cell infiltration between low-risk and high-risk groups, characterized by differences in immune cell abundance and elevated immune scores. Notably, the glyco-model predicted diverse responses to immune checkpoint inhibitors and drug therapies, with high-risk groups exhibiting a preference for immune checkpoint inhibitors and demonstrated superior responses to drug treatments. Furthermore, the study identified two potential drug targets and utilized Connectivity Map analysis to pinpoint promising therapeutic agents. Clofarabine and YM155 were identified as potent candidates for the treatment of high-risk GB. Our well-crafted glyco-model effectively discriminates patients by calculating the risk score, accurately predicting GB outcomes, and significantly enhancing prognostic assessment while identifying novel immunotherapeutic and chemotherapeutic strategies for GB treatment.
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