Plasmonic Heterojunction Nanomaterials for Noninvasive Pan-Cancer Metabolomic Screening

IF 24.5 Q1 CHEMISTRY, PHYSICAL
Interdisciplinary Materials Pub Date : 2026-04-01 Epub Date: 2026-03-01 DOI:10.1002/idm2.70036
Jia Qi, Hairu Lin, Caiyun Fang, Jia Li, Fangying Shi, Chunhui Deng
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Abstract

Cancer remains a major global health challenge, underscoring the urgent need for early diagnostic strategies. Altered cancer metabolism can be captured through serum analysis by laser desorption/ionization mass spectrometry (LDI-MS). Here, we report the design of gold nanoparticle-modified manganese–cobalt oxide heterojunctions (Au/MCOHs) as a high-performance LDI-MS nano-matrix. The Au/MCOHs combine strong ultraviolet absorption, enhanced charge separation, and favorable surface potential to achieve efficient laser energy conversion and ionization. Leveraging this platform, we performed serum metabolomic analysis on 698 participants, comprising healthy controls and patients with six different cancer types. Machine learning models distinguished cancer patients from healthy controls with area under the curve (AUC) values up to 0.955. Critically, early-stage cancers were also detected, with AUCs ranging from 0.776 to 0.938. Thirteen cancer-associated metabolites were identified, and a combined diagnostic panel achieved an AUC of 0.865. This study demonstrates how rationally engineered plasmonic heterojunction nanomatrices can bridge materials innovation and clinical translation, offering a robust, non-invasive route for pan-cancer metabolomic screening.

Abstract Image

Abstract Image

等离子体异质结纳米材料用于无创泛癌代谢组学筛查
癌症仍然是一项重大的全球卫生挑战,因此迫切需要制定早期诊断战略。通过激光解吸/电离质谱(LDI-MS)的血清分析,可以捕获癌症代谢的改变。在这里,我们报道了金纳米粒子修饰锰钴氧化物异质结(Au/MCOHs)作为高性能LDI-MS纳米基质的设计。Au/MCOHs结合了强大的紫外吸收,增强的电荷分离和良好的表面电位,实现了高效的激光能量转换和电离。利用这个平台,我们对698名参与者进行了血清代谢组学分析,包括健康对照组和六种不同癌症类型的患者。机器学习模型将癌症患者与健康对照区分开来,曲线下面积(AUC)值高达0.955。关键的是,早期癌症也被检测到,auc范围从0.776到0.938。鉴定出13种癌症相关代谢物,联合诊断组的AUC为0.865。这项研究证明了合理设计的等离子异质结纳米基质如何能够在材料创新和临床转化之间架起桥梁,为泛癌症代谢组学筛查提供了一种强大的、非侵入性的途径。
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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