近日，华中科技大学Xinghua Liu团队研究了AuNCs@Co(OH)x纳米结构的SERS致癌物质检测和癌细胞分类平台。相关论文发表在2026年7月2日出版的《结构化学》杂志上。

构建能够同时传感致癌物和识别癌细胞的多功能表面增强拉曼散射（SERS）基底，为实现集成化癌症风险预警和诊断应用提供了巨大潜力。

研究组在中空氢氧化钴纳米笼表面原位生长了金纳米簇（AuNCs，<2 nm），成功制备了一种新型SERS基底（AuNCs@Co(OH)?），可同时用于分析检测和生物细胞鉴别。AuNCs在费米能级（EF）附近具有更高的态密度（DOS），更有利于促进基底与探针分子之间的界面电荷转移。同时，Co(OH)_x纳米笼的多孔结构提供了丰富的吸附位点，共同提升了SERS性能。作为一种多功能SERS平台，AuNCs@Co(OH)_x实现了对致癌芳香胺的超灵敏传感，并通过多元分析有效区分了结构相似的分子。

更重要的是，通过将SERS光谱与基于主成分分析-支持向量机（PCA-SVM）的机器学习框架相结合，该平台实现了对胃肠道癌细胞系和白细胞（WBCs）的无标记精确分类，涵盖胃癌（HGC）、肝癌（HepG2）和胰腺癌（PANC-1）。该方法实现了高分类准确率和特异性（宏平均F1分数为95.47%），能够可靠区分恶性细胞和非恶性细胞。

值得注意的是，非恶性WBCs以完美的精确率和召回率（1.00）被识别，且未出现被误分类为恶性细胞的假阳性情况。该多功能SERS平台保持了样品完整性，无需外源性标记，且具有强大的鲁棒性，凸显了其在癌症风险预测、实时癌症诊断和复杂样品中特定分子检测方面的应用潜力。

附：英文原文

Title: AuNCs@Co(OH)x nanocage-based SERS platform for carcinogen detection and cancer cell classification

Author: anonymous

Issue&Volume: 2026-07-02

Abstract: Constructing multifunctional surface-enhanced Raman scattering (SERS) substrates capable of sensing carcinogenic and identifying cancer cells offers great potential for realizing integrated cancer risk warning and diagnostic applications. Hence, we in situ grew Au nanoclusters (AuNCs, <2 nm) on the surface of hollow cobalt hydroxide nanocages, and successfully fabricated a novel SERS substrate (AuNCs@Co(OH)x) for analytical detection and biological cell discrimination simultaneously. AuNCs exhibit a higher density of states (DOS) near the Fermi level (EF), which is more conducive to promoting interfacial charge transfer between the substrate and probe molecules. Meanwhile, the porous structure of Co(OH)x nanocages provides abundant adsorption sites, collectively boosting the high SERS performance. As a multifunctional SERS platform, AuNCs@Co(OH)x realized the ultrasensitive sensing of carcinogenic aromatic amines and effective discrimination of structurally similar molecules through multivariate analysis. More importantly, by integrating SERS spectroscopy with a principal component analysis-support vector machine (PCA-SVM)-based machine learning framework, the platform achieves label-free and accurate classification of gastrointestinal cancer cell lines and white blood cells (WBCs), including gastric cancer (HGC), liver cancer (HepG2), and pancreatic cancer (PANC-1). Achieving high classification accuracy and specificity (macro-averaged F1-score of 95.47%), the method enables reliable differentiation between malignant and non-malignant cells. Notably, non-malignant WBCs are identified with perfect precision and recall (1.00), with no false-positive classification as malignant cells. This multifunctional SERS platform preserves sample integrity, requires no exogenous labeling, and demonstrates strong robustness, highlighting its potential in predicting cancer risks, real-time cancer diagnosis, and specific molecular detection in complex samples.

DOI: 10.1016/j.cjsc.2026.101039

Source: https://cjsc.ac.cn/cms/issues/1092