Yang Xie, Yiyi Jin, Zide Liu, Jun Li, Qing Tao, Yonghui Wu, Youxiang Chen, Chunyan Zeng
{"title":"基于无创尿液代谢物筛选和Nomogram构建的结直肠息肉诊断生物标志物鉴定","authors":"Yang Xie, Yiyi Jin, Zide Liu, Jun Li, Qing Tao, Yonghui Wu, Youxiang Chen, Chunyan Zeng","doi":"10.1002/cam4.70762","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Purpose/Backgrounds</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Patients and Methods</h3>\n \n <p>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).</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>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.</p>\n </section>\n </div>","PeriodicalId":139,"journal":{"name":"Cancer Medicine","volume":"14 7","pages":""},"PeriodicalIF":2.9000,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cam4.70762","citationCount":"0","resultStr":"{\"title\":\"Identification of Diagnostic Biomarkers for Colorectal Polyps Based on Noninvasive Urinary Metabolite Screening and Construction of a Nomogram\",\"authors\":\"Yang Xie, Yiyi Jin, Zide Liu, Jun Li, Qing Tao, Yonghui Wu, Youxiang Chen, Chunyan Zeng\",\"doi\":\"10.1002/cam4.70762\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Purpose/Backgrounds</h3>\\n \\n <p>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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Patients and Methods</h3>\\n \\n <p>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).</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusions</h3>\\n \\n <p>This study successfully identified seven urinary metabolites as potential noninvasive biomarkers for CRP. 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Identification of Diagnostic Biomarkers for Colorectal Polyps Based on Noninvasive Urinary Metabolite Screening and Construction of a Nomogram
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.
期刊介绍：
Cancer Medicine is a peer-reviewed, open access, interdisciplinary journal providing rapid publication of research from global biomedical researchers across the cancer sciences. The journal will consider submissions from all oncologic specialties, including, but not limited to, the following areas:
Clinical Cancer Research
Translational research ∙ clinical trials ∙ chemotherapy ∙ radiation therapy ∙ surgical therapy ∙ clinical observations ∙ clinical guidelines ∙ genetic consultation ∙ ethical considerations
Cancer Biology:
Molecular biology ∙ cellular biology ∙ molecular genetics ∙ genomics ∙ immunology ∙ epigenetics ∙ metabolic studies ∙ proteomics ∙ cytopathology ∙ carcinogenesis ∙ drug discovery and delivery.
Cancer Prevention:
Behavioral science ∙ psychosocial studies ∙ screening ∙ nutrition ∙ epidemiology and prevention ∙ community outreach.
Bioinformatics:
Gene expressions profiles ∙ gene regulation networks ∙ genome bioinformatics ∙ pathwayanalysis ∙ prognostic biomarkers.
Cancer Medicine publishes original research articles, systematic reviews, meta-analyses, and research methods papers, along with invited editorials and commentaries. Original research papers must report well-conducted research with conclusions supported by the data presented in the paper.
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