Leveraging proteomics and machine learning for mechanism and biomarker discovery on glioma progression and transformation: from LGG to GBM.

IF 4.1 3区医学 Q2 GENETICS & HEREDITY

Human Genomics Pub Date : 2026-03-10 DOI:10.1186/s40246-026-00945-8

Qinhong Huang, Hui Liang, Jie Liu, Shenbao Shi, Xinlin Sun, Yiquan Ke, Taoliang Chen

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引用次数: 0

Abstract

Background: Glioma was the most common malignant tumor of the central nervous system in adults. Low-grade gliomas (LGGs) have a potential of grade progression and histological transformation, and the relevant mechanisms of this malignant evolution were still unclear.

Methods: In this study, we used 61 primary-recurrent paired glioma samples from 28 patients for proteomics analysis based upon DIA-NN approach.

Results: Our results indicated that the Ras/p38-MAPK pathways were hub signaling pathways driving grade progression and histological transformation in LGG. The activation of non-MGMT dependent unspecific DNA damage repair system mediated by Replication Factor C (RFC) can be an important reason for the treatment resistance. Moreover, metabolic reprogramming was widely involved in the regulation of LGG grade progression and histological transformation. The enhanced synthesis of unsaturated fatty acids and the significantly activated peroxisomal fatty acid beta-oxidation pathways were metabolic characteristics of LGG after histological transformation. Last, we constructed a reliable LGG progression prediction model consisted of 2 proteins, which can be monitor biomarkers for LGG progression, with potential clinical translation value.

Conclusions: In summary, glioma grade progression and histological transformation were complex biological processes, which involved the abnormal up-regulation of Ras/p38-MAPK signalings, enhanced non-MGMT dependent unspecific DNA damage repair system regulated by RFC complex and metabolic reprogramming based on HK1-PFKP-ENO2 mediated glycolysis and peroxisomal FA beta oxidation. This study filled the gap in relevant research on the grade progression and histological transformation of LGG, providing novel and unique insights into the biological mechanisms of glioma progression.

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利用蛋白质组学和机器学习发现神经胶质瘤进展和转化的机制和生物标志物：从LGG到GBM。

背景：神经胶质瘤是成人最常见的中枢神经系统恶性肿瘤。低级别胶质瘤（LGGs）具有级别进展和组织学转化的潜力，其恶性演变的相关机制尚不清楚。方法：在本研究中，我们使用来自28例患者的61例原发性复发配对胶质瘤样本进行基于DIA-NN方法的蛋白质组学分析。结果：我们的研究结果表明，Ras/p38-MAPK通路是驱动LGG分级进展和组织学转化的枢纽信号通路。由复制因子C （Replication Factor C， RFC）介导的非mgmt依赖性非特异性DNA损伤修复系统的激活可能是产生治疗抗性的重要原因。此外，代谢重编程广泛参与LGG级进展和组织学转化的调节。不饱和脂肪酸合成增强和过氧化物酶体脂肪酸β -氧化途径显著激活是LGG组织学转化后的代谢特征。最后，我们构建了一个由2个蛋白组成的可靠的LGG进展预测模型，该模型可以作为监测LGG进展的生物标志物，具有潜在的临床翻译价值。结论：综上所述，胶质瘤的分级进展和组织学转化是一个复杂的生物学过程，涉及Ras/p38-MAPK信号的异常上调，RFC复合体调节的非mgmt依赖非特异性DNA损伤修复系统的增强，以及基于HK1-PFKP-ENO2介导的糖酵解和过氧化物酶体FA β氧化的代谢重编程。本研究填补了LGG分级进展和组织学转化相关研究的空白，为胶质瘤进展的生物学机制提供了新颖独特的见解。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Human Genomics GENETICS & HEREDITY-

CiteScore

6.00

自引率

2.20%

发文量

审稿时长

11 weeks

期刊介绍： Human Genomics is a peer-reviewed, open access, online journal that focuses on the application of genomic analysis in all aspects of human health and disease, as well as genomic analysis of drug efficacy and safety, and comparative genomics. Topics covered by the journal include, but are not limited to: pharmacogenomics, genome-wide association studies, genome-wide sequencing, exome sequencing, next-generation deep-sequencing, functional genomics, epigenomics, translational genomics, expression profiling, proteomics, bioinformatics, animal models, statistical genetics, genetic epidemiology, human population genetics and comparative genomics.