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A novel strategy is present to predict lung cancer metastasis risk by combining single\u2010cell metabolic profiling of circulating tumor cells (CTCs) with a self\u2010developed CTC sorting and capture platform, enabling high\u2010efficiency, high\u2010viability CTC isolation from blood. Using nanoelectrospray ionization\u2010atmospheric pressure chemical ionization mass spectrometry, single\u2010cell metabolomic profiling on 301 CTCs derived from patients and animal models are performed. 390 unique metabolites are identified and discovered distinct metabolic signatures associated with different metastatic potentials (brain and bone). Based on these metabolic profiles, a classification model that categorizes CTCs into subgroups with distinct metastatic risks are constructed. The model outperformed traditional clinical indicators and total CTC counts, achieving AUCs of 0.74 (brain metastasis) and 0.92 (bone metastasis). 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