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Updated: May 15, 2025
Evaluation of Colorectal Cancer Risk and Prevalence by Stool DNA Integrity Detection
Published on: June 8, 2020
Identification of Diagnostic Biomarkers for Colorectal Polyps Based on Noninvasive Urinary Metabolite Screening and
Yang Xie1,2, Yiyi Jin2, Zide Liu2
1Department of Gastroenterology, Jiangxi Province Hospital of Integrated Chinese and Western Medicine, Nanchang, Jiangxi, China.
Purpose/Backgrounds:
Colorectal 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 Methods:
A 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).
Results:
A 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.
Conclusions:
This 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.
