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Literature DB >> 31151939

Urinary Metabolomics to Identify a Unique Biomarker Panel for Detecting Colorectal Cancer: A Multicenter Study.

Lu Deng¹, Kathleen Ismond^2,3, Zhengjun Liu⁴, Jeremy Constable⁵, Haili Wang³, Olusegun I Alatise⁶, Martin R Weiser⁵, T P Kingham⁵, David Chang^2,3.

Abstract

BACKGROUND: Population-based screening programs are credited with earlier colorectal cancer diagnoses and treatment initiation, which reduce mortality rates and improve patient health outcomes. However, recommended screening methods are unsatisfactory as they are invasive, are resource intensive, suffer from low uptake, or have poor diagnostic performance. Our goal was to identify a urine metabolomic-based biomarker panel for the detection of colorectal cancer that has the potential for global population-based screening.
METHODS: Prospective urine samples were collected from study participants. Based upon colonoscopy and histopathology results, 342 participants (colorectal cancer, 171; healthy controls, 171) from two study sites (Canada, United States) were included in the analyses. Targeted liquid chromatography-mass spectrometry (LC-MS) was performed to quantify 140 highly valuable metabolites in each urine sample. Potential biomarkers for colorectal cancer were identified by comparing the metabolomic profiles from colorectal cancer versus controls. Multiple models were constructed leading to a good separation of colorectal cancer from controls.
RESULTS: A panel of 17 metabolites was identified as possible biomarkers for colorectal cancer. Using only two of the selected metabolites, namely diacetylspermine and kynurenine, a predictor for detecting colorectal cancer was developed with an AUC of 0.864, a specificity of 80.0%, and a sensitivity of 80.0%.
CONCLUSIONS: We present a potentially "universal" metabolomic biomarker panel for colorectal cancer independent of cohort clinical features based on a North American population. Further research is needed to confirm the utility of the profile in a prospective, population-based colorectal cancer screening trial. IMPACT: A urinary metabolomic biomarker panel was identified for colorectal cancer with the potential of clinical application. ©2019 American Association for Cancer Research.

Entities: Chemical Disease Gene Species

Mesh：

Substances：
Biomarkers, Tumor

Year: 2019 PMID： 31151939 PMCID： PMC6677589 DOI： 10.1158/1055-9965.EPI-18-1291

Source DB: PubMed Journal: Cancer Epidemiol Biomarkers Prev ISSN： 1055-9965 Impact factor: 4.254

45 in total

1. Public expectations and attitudes for annual physical examinations and testing.

Authors: Sylvia K Oboler; Allan V Prochazka; Ralph Gonzales; Stanley Xu; Robert J Anderson
Journal: Ann Intern Med Date: 2002-05-07 Impact factor: 25.391

2. Kynurenine in disease, with particular reference to cancer.

Authors: M SPACEK
Journal: Can Med Assoc J Date: 1955-08-01 Impact factor: 8.262

3. Feasibility of identifying pancreatic cancer based on serum metabolomics.

Authors: Oliver F Bathe; Rustem Shaykhutdinov; Karen Kopciuk; Aalim M Weljie; Andrew McKay; Francis R Sutherland; Elijah Dixon; Nicole Dunse; Dina Sotiropoulos; Hans J Vogel
Journal: Cancer Epidemiol Biomarkers Prev Date: 2010-11-23 Impact factor: 4.254

4. N(1),N(12)-Diacetylspermine as a sensitive and specific novel marker for early- and late-stage colorectal and breast cancers.

Authors: Kyoko Hiramatsu; Keiichi Takahashi; Taturo Yamaguchi; Hiroshi Matsumoto; Hidenori Miyamoto; Souichi Tanaka; Chikako Tanaka; Yoshiko Tamamori; Mari Imajo; Masashi Kawaguchi; Masakazu Toi; Takeo Mori; Masao Kawakita
Journal: Clin Cancer Res Date: 2005-04-15 Impact factor: 12.531

5. Urine diacetylspermine as a novel tumour maker for pancreatobiliary carcinomas.

Authors: K Yamaguchi; M Nakamura; K Shirahane; H Konomi; N Torata; N Hamasaki; M Kawakita; M Tanaka
Journal: Dig Liver Dis Date: 2004-12-15 Impact factor: 4.088

6. Regularization Paths for Generalized Linear Models via Coordinate Descent.

Authors: Jerome Friedman; Trevor Hastie; Rob Tibshirani
Journal: J Stat Softw Date: 2010 Impact factor: 6.440

Review 7. Cigarette smoking and colorectal cancer incidence and mortality: systematic review and meta-analysis.

Authors: Peter S Liang; Ting-Yi Chen; Edward Giovannucci
Journal: Int J Cancer Date: 2009-05-15 Impact factor: 7.396

8. Evaluation of urine N1,N12-Diacetylspermine as potential tumor marker for urinary bladder cancer.

Authors: David Stejskal; Viera Humenanska; Zuzana Hanulova; Richard Fiala; Radovan Vrtal; Pavlina Solichova; Michal Karpisek
Journal: Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub Date: 2006-11 Impact factor: 1.245

9. Clinical significance of urinary N1,N12-diacetylspermine levels in patients with hepatocellular carcinoma.

Authors: M Enjoji; M Nakamuta; E Arimura; S Morizono; M Kuniyoshi; M Fukushima; K Kotoh; H Nawata
Journal: Int J Biol Markers Date: 2004 Oct-Dec Impact factor: 3.248

10. Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems.

Authors: Kim-Anh Lê Cao; Simon Boitard; Philippe Besse
Journal: BMC Bioinformatics Date: 2011-06-22 Impact factor: 3.169

6 in total

Urinary Metabolomics to Identify a Unique Biomarker Panel for Detecting Colorectal Cancer: A Multicenter Study.

1. Public expectations and attitudes for annual physical examinations and testing.

2. Kynurenine in disease, with particular reference to cancer.

3. Feasibility of identifying pancreatic cancer based on serum metabolomics.

4. N(1),N(12)-Diacetylspermine as a sensitive and specific novel marker for early- and late-stage colorectal and breast cancers.

5. Urine diacetylspermine as a novel tumour maker for pancreatobiliary carcinomas.

6. Regularization Paths for Generalized Linear Models via Coordinate Descent.

Review 7. Cigarette smoking and colorectal cancer incidence and mortality: systematic review and meta-analysis.

8. Evaluation of urine N1,N12-Diacetylspermine as potential tumor marker for urinary bladder cancer.

9. Clinical significance of urinary N1,N12-diacetylspermine levels in patients with hepatocellular carcinoma.

10. Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems.

Review 1. Biomarkers for detecting colorectal cancer non-invasively: DNA, RNA or proteins?

2. Urinary charged metabolite profiling of colorectal cancer using capillary electrophoresis-mass spectrometry.

Review 3. The Application of Metabolomics in Recent Colorectal Cancer Studies: A State-of-the-Art Review.

4. Discrimination between Pancreatic Cancer, Pancreatitis and Healthy Controls Using Urinary Polyamine Panel.

Review 5. Biomarkers to Detect Early-Stage Colorectal Cancer.

Review 6. Optimal Strategies for Colorectal Cancer Screening.