Identification of Diagnostic Biomarkers for Colorectal Polyps Based on Noninvasive Urinary Metabolite Screening and Construction of a Nomogram

Purpose/BackgroundsColorectal polyps (CRPs) are precursors to colorectal cancer (CRC), and early detection is crucial for prevention. Traditional diagnostic methods are invasive, prompting a need for noninvasive biomarkers. This study aimed to identify urinary metabolite biomarkers for diagnosing CRPs and construct a diagnostic nomogram based on noninvasive urinary metabolite screening.Patients and MethodsA total of 192 participants, including 64 CRP patients and 128 healthy controls, were recruited. Urine samples were analyzed using untargeted gas chromatography-mass spectrometry (GC-MS) and ultra-performance liquid chromatography-mass spectrometry (UPLC-MS). Metabolite screening was performed using weighted gene coexpression network analysis (WGCNA), least absolute shrinkage and selection operator (LASSO), and support vector machine-recursive feature elimination (SVM-RFE). A diagnostic nomogram was developed based on identified metabolites, and its performance was evaluated using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA).ResultsA total of 350 metabolites were identified, with 7 key metabolites significantly associated with CRP. Multivariate logistic regression analysis identified Saccharin (OR = 48.3, 95% CI: 4.44-525.32) and N-omega-acetylhistamine (OR = 27.91, 95% CI: 2.31-337.06) as significant risk factors for CRP, while N-methyl-L-proline, trimethylsilyl ester (OR = 0.08, 95% CI: 0.01-0.8) was a protective factor. A nomogram incorporating these metabolites demonstrated strong discriminatory power, with AUC values of 0.974 and 0.960 in the training and validation sets, respectively. Calibration plots and DCA confirmed the model's accuracy and clinical utility.ConclusionsThis study successfully identified seven urinary metabolites as potential noninvasive biomarkers for CRP. The constructed diagnostic nomogram, based on these metabolites, offers high predictive accuracy and clinical applicability, providing a promising tool for the early detection of CRP.