Glioma is the most prevalent type of primary central nervous system cancer, while glioblastoma (GBM) is its most aggressive variant, with a median survival of only 15 months when treated with maximal surgical resection followed by chemoradiation therapy (CRT). CD133 is a potentially significant GBM biomarker. However, current clinical biomarker studies rely on invasive tissue samples. These make prolonged data acquisition impossible, resulting in increased interest in the use of liquid biopsies. Our study, analyzed 7289 serum proteins from 109 patients with pathology-proven GBM obtained prior to CRT using the aptamer-based SOMAScan®proteomic assay technology. We developed a novel methodology that identified 24 proteins linked to both serum CD133 and 12-month overall survival (OS) through a multi-step machine learning (ML) analysis. These identified proteins were subsequently subjected to survival and clustering evaluations, categorizing patients into five risk groups that accurately predicted 12-month OS based on their protein profiles. Most of these proteins are involved in brain function, neural development, and/or cancer biology signaling, highlighting their significance and potential predictive value. Identifying these proteins provides a valuable foundation for future serum investigations as validation of clinically applicable GBM biomarkers can unlock immense potential for diagnostics and treatment monitoring.
胶质瘤是最常见的原发性中枢神经系统肿瘤，其中胶质母细胞瘤（GBM）是其最具侵袭性的亚型，即使经过最大范围手术切除联合放化疗（CRT）治疗，患者中位生存期也仅为15个月。CD133是GBM潜在的重要生物标志物，然而目前的临床生物标志物研究依赖于侵入性组织样本，无法实现长期数据采集，这促使液体活检技术日益受到关注。本研究采用基于适配体的SOMAScan®蛋白质组学检测技术，对109例经病理证实的GBM患者在CRT治疗前采集的血清样本进行了7289种蛋白质分析。通过多步骤机器学习（ML）分析，我们开发了一种新方法，鉴定出24种与血清CD133水平和12个月总生存期（OS）均相关的蛋白质。对这些蛋白质进行生存分析和聚类评估后，将患者划分为五个风险组，能根据其蛋白质谱准确预测12个月OS。这些蛋白质大多参与脑功能、神经发育和/或癌症生物学信号传导，凸显了其重要性和潜在预测价值。鉴定这些蛋白质为未来血清研究奠定了重要基础，验证临床适用的GBM生物标志物将为诊断和治疗监测开启巨大潜力。
肿瘤(癌症)患者之家