Analysis of urine biomarkers in urothelial carcinoma based on untargeted metabolomics

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 （<i>P</i>>0.05）. Untargeted metabolomic profiling was performed using liquid chromatography-tandem mass spectrometry （LC-MS/MS）. Principal component analysis （PCA） revealed no distinct cluster separation between groups. This result was potentially attributed to limited intergroup metabolic variations or restricted sample size. To enhance discriminatory capacity， supervised orthogonal partial least squares-discriminant analysis （OPLS-DA） was implemented for feature selection. This approach effectively addressed variable collinearity while minimizing non-biological noise interference. Differentially expressed metabolites were identified through variable importance in projection scores， fold change and adjusted P-values. Metabolic pathway analysis was conducted， incorporating pathway enrichment analysis. This multi-tiered analytical approach systematically prioritized UC-associated metabolic perturbations while controlling for confounding factors in specimen analysis. Supervised OPLS-DA was employed to identify differential metabolites and associated metabolic pathways. Significant urinary metabolic disparities were detected between UC patients and healthy controls. Alterations were observed in L-Histidine， <i>N</i>-Acetyltryptophan， 5'-methylthioadenosine， <i>N</i>-methylnicotinamide， L-octanoylcarnitine， 3-indolehydracrylic acid， <i>N</i>¹，<i>N</i>¹²-diacetylspermine， pantothenic acid and so on （<i>P</i><0.05）. Pathway enrichment analysis revealed perturbations spanning amino acid metabolism， nucleotide biosynthesis， vitamin cofactor utilization， and carbohydrate processing. The histidine metabolism pathway demonstrated the highest topological impact. It was followed by the arginine biosynthesis pathway， arginine and proline metabolism pathway， and the tryptophan catabolism pathway. Future validation in larger cohorts and mechanistic studies is warranted to confirm their clinical utility. The aberrant pathways may offer novel biomarkers and therapeutic targets， particularly for patients resistant to conventional therapies.