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

Chen Chen, G. A. Nagana Gowda, Jiangjiang Zhu, Lingli Deng, Haiwei Gu, E. Gabriela Chiorean, Mohammad Abu Zaid, Marietta Harrison, Dabao Zhang, Min Zhang, Daniel Raftery

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Introduction: 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. Objectives: 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. Methods: 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 metabolites achieved improved differentiation and were significant for 26 out of 29 groups. Furthermore, the networks of correlated metabolites constructed for all groups of metabolites using the ParCorA algorithm, before or after application of the SUR model, showed significant alterations for CRC and polyp patients relative to healthy controls. Results: The results showed that demographic covariates, such as gender, BMI, BMI2, and smoking status, exhibit significant confounding effects on metabolite levels, which can be modeled effectively. Conclusion: These results not only provide new insights into addressing the major issue of confounding effects in metabolomics analysis, but also shed light on issues related to establishing reliable biomarkers and the biological connections between them in a complex disease.

Original languageEnglish (US)
Article number125
JournalMetabolomics
Volume13
Issue number11
DOIs
StatePublished - Nov 1 2017
Externally publishedYes

Fingerprint

Metabolomics
Metabolites
Polyps
Regression analysis
Colorectal Neoplasms
Regression Analysis
Biomarkers
Smoking
Mass Spectrometry
Healthy Volunteers
Alcohols
Demography
Technology
Serum
Mass spectrometry

Keywords

  • Clinical factors
  • Colorectal cancer
  • Colorectal polyp
  • Metabolic profiling
  • Metabolomics
  • Seemingly unrelated regression
  • Targeted mass spectrometry

ASJC Scopus subject areas

  • Endocrinology, Diabetes and Metabolism
  • Biochemistry
  • Clinical Biochemistry

Cite this

Altered metabolite levels and correlations in patients with colorectal cancer and polyps detected using seemingly unrelated regression analysis. / Chen, Chen; Nagana Gowda, G. A.; Zhu, Jiangjiang; Deng, Lingli; Gu, Haiwei; Chiorean, E. Gabriela; Abu Zaid, Mohammad; Harrison, Marietta; Zhang, Dabao; Zhang, Min; Raftery, Daniel.

In: Metabolomics, Vol. 13, No. 11, 125, 01.11.2017.

Research output: Contribution to journalArticle

Chen, C, Nagana Gowda, GA, Zhu, J, Deng, L, Gu, H, Chiorean, EG, Abu Zaid, M, Harrison, M, Zhang, D, Zhang, M & Raftery, D 2017, 'Altered metabolite levels and correlations in patients with colorectal cancer and polyps detected using seemingly unrelated regression analysis', Metabolomics, vol. 13, no. 11, 125. https://doi.org/10.1007/s11306-017-1265-0
Chen, Chen ; Nagana Gowda, G. A. ; Zhu, Jiangjiang ; Deng, Lingli ; Gu, Haiwei ; Chiorean, E. Gabriela ; Abu Zaid, Mohammad ; Harrison, Marietta ; Zhang, Dabao ; Zhang, Min ; Raftery, Daniel. / Altered metabolite levels and correlations in patients with colorectal cancer and polyps detected using seemingly unrelated regression analysis. In: Metabolomics. 2017 ; Vol. 13, No. 11.
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