Biopharmaceuticals and Biomarkers Discovery Lab, School of Biochemistry and Biotechnology, University of the Punjab, Lahore, 54590, Pakistan.
Riphah College of Rehabilitation and Allied Health Sciences, Riphah International University, Lahore, 54770, Pakistan.
Interdiscip Sci. 2024 Dec;16(4):854-871. doi: 10.1007/s12539-024-00642-x. Epub 2024 Sep 27.
Diagnosing and classifying central nervous system tumors such as gliomas or glioblastomas pose a significant challenge due to their aggressive and infiltrative nature. However, recent advancements in metabolomics and magnetic resonance spectroscopy (MRS) offer promising avenues for differentiating tumor grades both in vivo and ex vivo. This study aimed to explore tissue-based metabolic signatures to classify/distinguish between low- and high-grade gliomas. Forty-six histologically confirmed, intact solid tumor samples from glioma patients were analyzed using high-resolution magic angle spinning nuclear magnetic resonance (HRMAS-NMR) spectroscopy. By integrating machine learning (ML) algorithms, spectral regions with the most discriminative potential were identified. Validation was performed through univariate and multivariate statistical analyses, along with HRMAS-NMR analyses of 46 paired plasma samples. Amongst the various ML models applied, the logistics regression identified 46 spectral regions capable of sub-classifying gliomas with accuracy 87% (F1-measure 0.87, Precision 0.82, Recall 0.93), whereas the extra-tree classifier identified three spectral regions with predictive accuracy of 91% (F1-measure 0.91, Precision 0.85, Recall 0.97). Wilcoxon test presented 51 spectral regions significantly differentiating low- and high-grade glioma groups (p < 0.05). Based on sensitivity and area under the curve values, 40 spectral regions corresponding to 18 metabolites were considered as potential biomarkers for tissue-based glioma classification and amongst these N-acetyl aspartate, glutamate, and glutamine emerged as the most important markers. These markers were validated in paired plasma samples, and their absolute concentrations were computed. Our results demonstrate that the metabolic markers identified through the HRMAS-NMR-ML analysis framework, and their associated metabolic networks, hold promise for targeted treatment planning and clinical interventions in the future.
诊断和分类中枢神经系统肿瘤，如神经胶质瘤或胶质母细胞瘤，由于其侵袭性和浸润性的性质，是一项重大挑战。然而，代谢组学和磁共振波谱（MRS）的最新进展为体内和体外区分肿瘤分级提供了有希望的途径。本研究旨在探索基于组织的代谢特征，以对低级别和高级别神经胶质瘤进行分类/区分。使用高分辨率魔角旋转核磁共振（HRMAS-NMR）光谱分析了 46 例经组织学证实的神经胶质瘤患者完整的实体瘤样本。通过整合机器学习（ML）算法，确定了具有最大区分潜力的光谱区域。通过单变量和多变量统计分析以及 46 对配对血浆样本的 HRMAS-NMR 分析进行验证。在所应用的各种 ML 模型中，逻辑回归确定了 46 个光谱区域，能够以 87%的准确度对神经胶质瘤进行亚分类（F1 度量 0.87，精度 0.82，召回率 0.93），而 Extra-Tree 分类器则确定了三个具有 91%预测准确性的光谱区域（F1 度量 0.91，精度 0.85，召回率 0.97）。Wilcoxon 检验呈现出 51 个光谱区域可显著区分低级别和高级别神经胶质瘤组（p<0.05）。基于敏感性和曲线下面积值，考虑了 40 个对应于 18 种代谢物的光谱区域作为基于组织的神经胶质瘤分类的潜在生物标志物，其中 N-乙酰天冬氨酸、谷氨酸和谷氨酰胺成为最重要的标志物。这些标志物在配对血浆样本中进行了验证，并计算了它们的绝对浓度。我们的结果表明，通过 HRMAS-NMR-ML 分析框架确定的代谢标志物及其相关代谢网络，为未来的靶向治疗计划和临床干预提供了希望。
