. 2017 Nov;13(11):125.

doi: 10.1007/s11306-017-1265-0. Epub 2017 Sep 15.

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

Chen Chen¹, G A Nagana Gowda², Jiangjiang Zhu³, Lingli Deng⁴, Haiwei Gu², E Gabriela Chiorean^{5

6

7}, Mohammad Abu Zaid⁵, Marietta Harrison⁸, Dabao Zhang¹, Min Zhang^{1

9

10}, Daniel Raftery^{2

7

11}

Affiliations

¹ Department of Statistics, Purdue University, West Lafayette, IN 47907, USA.
² Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA 98109, USA.
³ Department of Chemistry & Biochemistry, Miami University, Oxford, OH 45056, USA.
⁴ Department of Electronic Science and Communication Engineering, State Key Laboratory for Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen 361005, Fujian, China.
⁵ Indiana University Melvin and Bren Simon Cancer Center, 535 Barnhill Drive, Indianapolis, IN 46202, USA.
⁶ Department of Medicine, University of Washington, 825 Eastlake Ave East, Seattle, WA 98109, USA.
⁷ Fred Hutchinson Cancer Research Center, 1100 Fairview Ave North, Seattle, WA 98109, USA.
⁸ Department of Medicinal Chemistry, Purdue University, West Lafayette, IN 47907, USA.
⁹ Bioinformatics Center, School of Biomedical Engineering, Capital Medical University, Beijing 100069, China.
¹⁰ Beijing Institute for Brain Disorders, Capital Medical University, Beijing 100069, China.
¹¹ Department of Chemistry, Purdue University, West Lafayette, IN 47907, USA.

PMID: 30814918
PMCID: PMC6388625
DOI: 10.1007/s11306-017-1265-0

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

Chen Chen et al. Metabolomics. 2017 Nov.

. 2017 Nov;13(11):125.

doi: 10.1007/s11306-017-1265-0. Epub 2017 Sep 15.

Authors

Affiliations

¹ Department of Statistics, Purdue University, West Lafayette, IN 47907, USA.
² Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA 98109, USA.
³ Department of Chemistry & Biochemistry, Miami University, Oxford, OH 45056, USA.
⁴ Department of Electronic Science and Communication Engineering, State Key Laboratory for Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen 361005, Fujian, China.
⁵ Indiana University Melvin and Bren Simon Cancer Center, 535 Barnhill Drive, Indianapolis, IN 46202, USA.
⁶ Department of Medicine, University of Washington, 825 Eastlake Ave East, Seattle, WA 98109, USA.
⁷ Fred Hutchinson Cancer Research Center, 1100 Fairview Ave North, Seattle, WA 98109, USA.
⁸ Department of Medicinal Chemistry, Purdue University, West Lafayette, IN 47907, USA.
⁹ Bioinformatics Center, School of Biomedical Engineering, Capital Medical University, Beijing 100069, China.
¹⁰ Beijing Institute for Brain Disorders, Capital Medical University, Beijing 100069, China.
¹¹ Department of Chemistry, Purdue University, West Lafayette, IN 47907, USA.

PMID: 30814918
PMCID: PMC6388625
DOI: 10.1007/s11306-017-1265-0

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.

Keywords: Clinical factors; Colorectal cancer; Colorectal polyp; Metabolic profiling; Metabolomics; Seemingly unrelated regression; Targeted mass spectrometry.

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Conflict of interest statement

Compliance with ethical standards Conflict of interest The authors declare that they have no competing interests.

Figures

**Fig. 1**
Comparison of the number of significant correlations as indicated by the connections between metabolites for the constructed networks of Group 14 metabolites in cancer, polyp patients and healthy subjects. An overall reduction of the number of significant correlations is observed from healthy control to CRC

**Fig. 2**
Comparison of the number of significant correlations as indicated by the connections between metabolites for the constructed networks of Group 15 metabolites in cancer, polyp patients and healthy subjects. SUR analysis affects a number of the significant correlations between metabolites, but the overall pattern of higher correlation between metabolites in healthy controls continues to hold

See this image and copyright information in PMC

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

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

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