#AI Reads Urine# Diagnostic Biomarkers for Colorectal Polyps Based on Noninvasive Urinary Metabolite Screening

This research centers on identifying urinary metabolite biomarkers for colorectal polyps (CRPs) and creating a diagnostic nomogram to enable non - invasive early CRP detection.

Colorectal cancer (CRC) is a major global health issue. CRPs are precursors to CRC, and early detection is crucial for prevention. However, colonoscopy, the current gold - standard for CRP detection, has drawbacks like invasiveness and high cost, limiting its use in mass screening. Thus, non - invasive diagnostic methods are in high demand.

The study recruited 192 participants, 64 with CRPs and 128 healthy controls. Urine samples were collected, pre - processed, and analyzed using gas chromatography - mass spectrometry (GC - MS) and ultra - performance liquid chromatography - mass spectrometry (UPLC - MS). These techniques can comprehensively analyze the urinary metabolome.

To find key metabolites related to CRPs, three methods were used: weighted gene co - expression network analysis (WGCNA), least absolute shrinkage and selection operator (LASSO), and support vector machine - recursive feature elimination (SVM - RFE). Through their intersection, seven potential CRP - related urinary metabolites were identified.

Multivariate logistic regression analysis showed that saccharin and N - omega - acetylhistamine were risk factors for CRPs, while N - methyl - L - proline, trimethylsilyl ester was a protective factor. Based on these, a diagnostic nomogram was constructed to estimate the probability of having CRPs.

The nomogram's performance was evaluated with ROC curves, calibration plots, and DCA. It had excellent discriminatory power, with high AUC values in both training and validation sets. The calibration plots and DCA confirmed its accuracy and clinical utility.

The identified urinary metabolites have potential as biomarkers. But the study has limitations. The sample size is small, especially in the validation cohort. Larger multicenter studies are needed to confirm the biomarkers' diagnostic accuracy and the nomogram's predictive power. The biological roles of the metabolites in CRP progression are unclear, and the lack of longitudinal data limits understanding of their causal relationship with CRP.

In summary, this study identified seven urinary metabolites as potential CRP biomarkers and constructed a nomogram. Despite the need for further research, it offers a new way for CRP detection and a useful tool for clinical risk assessment, which could improve early CRP diagnosis and patient outcomes.

Cancer Med. 2025 Apr;14(7):e70762. doi: 10.1002/cam4.70762.

Youhe Gao

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