Exploring metabolic profile differences between colorectal polyp patients and controls using seemingly unrelated regression

Chen Chen, Lingli Deng, Siwei Wei, G. A. Nagana Gowda, Haiwei Gu, Elena G. Chiorean, Mohammad Abu Zaid, Marietta L. Harrison, Joseph F. Pekny, Patrick J. Loehrer, Dabao Zhang, Min Zhang, Daniel Raftery

Research output: Contribution to journalArticle

13 Citations (Scopus)

Abstract

Despite the fact that colorectal cancer (CRC) is one of the most prevalent and deadly cancers in the world, the development of improved and robust biomarkers to enable screening, surveillance, and therapy monitoring of CRC continues to be evasive. In particular, patients with colon polyps are at higher risk of developing colon cancer; however, noninvasive methods to identify these patients suffer from poor performance. In consideration of the challenges involved in identifying metabolite biomarkers in individuals with high risk for colon cancer, we have investigated NMR-based metabolite profiling in combination with numerous demographic parameters to investigate the ability of serum metabolites to differentiate polyp patients from healthy subjects. We also investigated the effect of disease risk on different groups of biologically related metabolites. A powerful statistical approach, seemingly unrelated regression (SUR), was used to model the correlated levels of metabolites in the same biological group. The metabolites were found to be significantly affected by demographic covariates such as gender, BMI, BMI2, and smoking status. After accounting for the effects of the confounding factors, we then investigated potential of metabolites from serum to differentiate patients with polyps and age matched healthy controls. Our results showed that while only valine was slightly associated, individually, with polyp patients, a number of biologically related groups of metabolites were significantly associated with polyps. These results may explain some of the challenges and promise a novel avenue for future metabolite profiling methodologies.

Original languageEnglish (US)
Pages (from-to)2492-2499
Number of pages8
JournalJournal of Proteome Research
Volume14
Issue number6
DOIs
StatePublished - Jun 5 2015
Externally publishedYes

Fingerprint

Metabolome
Metabolites
Polyps
Colonic Neoplasms
Colorectal Neoplasms
Biomarkers
Demography
Aptitude
Valine
Serum
Healthy Volunteers
Colon
Smoking
Screening
Neoplasms
Nuclear magnetic resonance
Monitoring

Keywords

  • colorectal polyp
  • metabolic profiling
  • metabolomics
  • NMR spectroscopy
  • seemingly unrelated regression

ASJC Scopus subject areas

  • Biochemistry
  • Chemistry(all)

Cite this

Exploring metabolic profile differences between colorectal polyp patients and controls using seemingly unrelated regression. / Chen, Chen; Deng, Lingli; Wei, Siwei; Nagana Gowda, G. A.; Gu, Haiwei; Chiorean, Elena G.; Abu Zaid, Mohammad; Harrison, Marietta L.; Pekny, Joseph F.; Loehrer, Patrick J.; Zhang, Dabao; Zhang, Min; Raftery, Daniel.

In: Journal of Proteome Research, Vol. 14, No. 6, 05.06.2015, p. 2492-2499.

Research output: Contribution to journalArticle

Chen, C, Deng, L, Wei, S, Nagana Gowda, GA, Gu, H, Chiorean, EG, Abu Zaid, M, Harrison, ML, Pekny, JF, Loehrer, PJ, Zhang, D, Zhang, M & Raftery, D 2015, 'Exploring metabolic profile differences between colorectal polyp patients and controls using seemingly unrelated regression', Journal of Proteome Research, vol. 14, no. 6, pp. 2492-2499. https://doi.org/10.1021/acs.jproteome.5b00059
Chen, Chen ; Deng, Lingli ; Wei, Siwei ; Nagana Gowda, G. A. ; Gu, Haiwei ; Chiorean, Elena G. ; Abu Zaid, Mohammad ; Harrison, Marietta L. ; Pekny, Joseph F. ; Loehrer, Patrick J. ; Zhang, Dabao ; Zhang, Min ; Raftery, Daniel. / Exploring metabolic profile differences between colorectal polyp patients and controls using seemingly unrelated regression. In: Journal of Proteome Research. 2015 ; Vol. 14, No. 6. pp. 2492-2499.
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