Metabolic fingerprinting enables rapid, label-free histopathology in gastric cancer diagnosis and prognostic prediction

作者：Fei Teng, Juxiang Zhang, Yida Huang, Xu Wei, Wanshan Liu, Liming Sun, Meng Yan, Jiao Wu, Rui Wang, Shouzhi Yang, Lin Huang, Zhengying Gu, Haiyang Su, Xiaoyu Xu, Dingyitai Liang, Ning Ren, Chunmeng Ding, Yanyan Li, Qiongzhu Dong, Lingchuan Guo, Shaoqun Liu, Xuefei Wang, Kun Qian · 发表于：Cell Reports Medicine · 年份：2025 · DOI：10.1016/j.xcrm.2025.102238 · 被引用次数：17 · 研究领域：Molecular Biology Techniques and Applications、Metabolomics and Mass Spectrometry Studies、Advanced Proteomics Techniques and Applications

Histopathological evaluation is a cornerstone of cancer identification but often involves time-consuming labeling processes (∼days per sample) and experience-dependent interpretation. Herein, we introduce a rapid (∼40 min per sample) and label-free histopathological method based on metabolic fingerprinting of tissue using nanoparticle-enhanced laser desorption/ionization mass spectrometry. Applied to gastric cancer (GC, n = 284 paired tissue), this approach distinguishes malignant from benign tissues (area under the curve [AUC] of 0.979), identifies tumor subtypes (AUC of 0.963), and assesses prognosis (p < 0.05) without specialized pathologists. External validation on 238 samples from an independent cohort confirmed its robustness. This method advances histopathological analysis, offering potential for scalable clinical use.