ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025
Prediction of Lung Cancer Metastasis Risk Based on Single-Cell Metabolic Profiling of Circulating Tumor Cells.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Living single-cell metabolomicsChemical science · 2026Review
- Serum Metabolomics Study Reveals a Diagnostic Model for Lung Cancer Brain Metastasis.The clinical respiratory journal · 2026Article
- Circulating tumor cells: mechanisms and clinical significance in colorectal cancer metastasis.Molecular cancer · 2025Review
- Prediction of Lung Cancer Metastasis Risk Based on Single-Cell Metabolic Profiling of Circulating Tumor Cells.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Decoding cardiac metabolic reprogramming through single-cell multi-omics: from mechanisms to therapeutic applications.Frontiers in cell and developmental biology · 2025Review
Corrections and comments
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Authors and funding
15 authors.
Funding
Abstract
Lung cancer metastasis is a leading cause of cancer-related mortality, necessitating innovative approaches for early prediction and personalized clinical management. A novel strategy is present to predict lung cancer metastasis risk by combining single-cell metabolic profiling of circulating tumor cells (CTCs) with a self-developed CTC sorting and capture platform, enabling high-efficiency, high-viability CTC isolation from blood. Using nanoelectrospray ionization-atmospheric pressure chemical ionization mass spectrometry, single-cell 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). Prospective validation confirmed its metabolite-based classification accuracy for one-year metastasis risk prediction. This study highlights the potential of single-cell metabolomics to uncover novel therapeutic targets and prognostic markers, advancing liquid biopsy from quantitative counting to qualitative analysis. The approach represents a significant advancement in precision medicine for lung cancer management, offering a personalized strategy for predicting metastasis risk and guiding clinical treatment.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.