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. 2015 Jun 5;14(6):2492-9.
doi: 10.1021/acs.jproteome.5b00059. Epub 2015 May 13.

Exploring Metabolic Profile Differences between Colorectal Polyp Patients and Controls Using Seemingly Unrelated Regression

Chen Chen, Lingli Deng  1 , Siwei Wei, G A Nagana Gowda  2 , Haiwei Gu  2 , Elena G Chiorean  3   4 , Mohammad Abu Zaid  3 , Marietta L Harrison, Joseph F Pekny, Patrick J Loehrer  3 , Dabao Zhang, Min Zhang, Daniel Raftery  2   5
Affiliations

Exploring Metabolic Profile Differences between Colorectal Polyp Patients and Controls Using Seemingly Unrelated Regression

Chen Chen et al. J Proteome Res. .

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, BMI(2), 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.

Keywords: NMR spectroscopy; colorectal polyp; metabolic profiling; metabolomics; seemingly unrelated regression.

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Figure 1
Flowchart for the analysis of metabolomics data using seemingly unrelated regression.
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