ArticleSe pu = Chinese journal of chromatography2026
[Analysis of urine biomarkers in urothelial carcinoma based on untargeted metabolomics].
Article in Se pu = Chinese journal of chromatography, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Urothelial carcinoma （UC） is a globally prevalent malignancy lacking robust non-invasive biomarkers. Metabolic reprogramming is a recognized cancer hallmark. Untargeted metabolomics enables high-throughput and unbiased analysis of bodily fluids， offering a promising approach for discovering novel biomarkers in UC. This investigation employs untargeted metabolomic profiling to detect novel urinary biomarkers in UC cohorts. The analytical strategy prioritizes tumor-associated metabolic perturbations through pathway-centric characterization of dysregulated biochemical networks. This study systematically characterizes differential metabolites and associated pathway dysregulations in UC cohorts. The approach seeks to establish a reliable metabolic signature with diagnostic and prognostic value. The findings are expected to advance the development of novel clinical tools. UC biomarkers should optimally integrate preclinical identification， treatment response tracking， and precision-tailored interventions. This investigation provides a methodological framework for exploring cancer metabolism in UC. And it offers evidence-based insights to support translational research and precision medicine initiatives in oncology. This study was conducted at Beijing Chao-Yang Hospital， Capital Medical University， between January and December 2020. A total of 60 urine specimens were consecutively collected. They comprised 30 histologically confirmed UC patients and 30 healthy controls with normal urinalysis findings. Clinical data was prospectively collected via structured case report forms. It encompassed baseline demographics， comorbidities， anthropometric and behavioral factors， and UC pathological parameters. All urine samples were collected prior to invasive procedures. And they were labeled， snap-frozen in liquid nitrogen， and stored at -80 ℃ until analysis. Metabolic profiling was performed using a quadrupole-orbitrap high resolution mass spectrometer equipped with a heated electrospray ionization source. Mass spectrometric data processing was performed using Progenesis QI software. Data processing followed the following workflow： raw data import， spectral peak alignment， feature extraction， and deconvolution. The Progenesis QI software generated datasets containing retention time， peak intensity， and mass-to-charge ratios. Multivariate signal decomposition enabled independent resolution of adduct species， including protonated and sodium-adducted ions. Quality control measures included elimination of ion features demonstrating intra-batch coefficient of variation >15% across technical replicates. This rigorous preprocessing protocol ensured removal of unstable signals. And it preserved biologically relevant metabolic features for subsequent multivariate analysis. No statistically significant differences were observed in baseline clinical characteristics between UC and healthy control cohorts （
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