使用 LC-HRMS 和机器学习对脑膜瘤等级进行代谢组学和脂质组学表征,Journal of the American Society for Mass Spectrometry

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使用 LC-HRMS 和机器学习对脑膜瘤等级进行代谢组学和脂质组学表征
Journal of the American Society for Mass Spectrometry ( IF 3.1 ) Pub Date : 2023-09-14 , DOI: 10.1021/jasms.3c00158
Hoda Safari Yazd ₁ , Sina Feizbakhsh Bazargani ₁ , Garrett Fitzpatrick ₂ , Richard A Yost ₁ , Jesse Kresak ₂ , Timothy J Garrett ₂

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脑膜瘤是最常见的脑肿瘤之一，起源于大脑和脊髓的软脑膜覆盖层，约占所有中枢神经系统肿瘤的 37%。根据世界卫生组织的说法，脑膜瘤分为三种组织学亚型：良性、非典型和间变性。有时，组织学诊断为良性肿瘤的脑膜瘤表现出侵袭性肿瘤的临床特征和行为。在这项研究中，我们检查了脑膜瘤的代谢组学和脂质组学特征，重点是比较低级别和高级别肿瘤，并确定可以区分良性和恶性肿瘤的潜在标志物。采用高分辨率质谱联用液相色谱法对 85 个不同脑膜瘤级别的肿瘤活检样本进行非靶向代谢组学和脂质组学分析。然后，我们应用特征选择和机器学习技术来查找具有最高信息的特征，以帮助诊断脑膜瘤等级。确定了三种生物标志物来区分低级别和高级别脑膜瘤脑肿瘤。使用基于质谱的代谢组学和脂质组学与机器学习分析相结合来展望和表征与脑膜瘤分级相关的生物标志物可能为阐明潜在的治疗和预后目标铺平道路。

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Metabolomic and Lipidomic Characterization of Meningioma Grades Using LC–HRMS and Machine Learning

Meningiomas are among the most common brain tumors that arise from the leptomeningeal cover of the brain and spinal cord and account for around 37% of all central nervous system tumors. According to the World Health Organization, meningiomas are classified into three histological subtypes: benign, atypical, and anaplastic. Sometimes, meningiomas with a histological diagnosis of benign tumors show clinical characteristics and behavior of aggressive tumors. In this study, we examined the metabolomic and lipidomic profiles of meningioma tumors, focusing on comparing low-grade and high-grade tumors and identifying potential markers that can discriminate between benign and malignant tumors. High-resolution mass spectrometry coupled to liquid chromatography was used for untargeted metabolomics and lipidomics analyses of 85 tumor biopsy samples with different meningioma grades. We then applied feature selection and machine learning techniques to find the features with the highest information to aid in the diagnosis of meningioma grades. Three biomarkers were identified to differentiate low- and high-grade meningioma brain tumors. The use of mass-spectrometry-based metabolomics and lipidomics combined with machine learning analyses to prospect and characterize biomarkers associated with meningioma grades may pave the way for elucidating potential therapeutic and prognostic targets.

更新日期：2023-09-14

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