Altered metabolite levels and correlations in patients with colorectal cancer and polyps detected using seemingly unrelated regression analysis.

ABSTRACT: Metabolomics technologies enable the identification of putative biomarkers for numerous diseases; however, the influence of confounding factors on metabolite levels poses a major challenge in moving forward with such metabolites for pre-clinical or clinical applications. To address this challenge, we analyzed metabolomics data from a colorectal cancer (CRC) study, and used seemingly unrelated regression (SUR) to account for the effects of confounding factors including gender, BMI, age, alcohol use, and smoking. A SUR model based on 113 serum metabolites quantified using targeted mass spectrometry, identified 20 metabolites that differentiated CRC patients (n = 36), patients with polyp (n = 39), and healthy subjects (n = 83). Models built using different groups of biologically related metab

SUBMITTER: Chen C 

PROVIDER: S-EPMC6388625 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature