, 13:125 | Cite as

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 ZhangEmail author
  • Daniel RafteryEmail author
Original Article



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 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.


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.


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.


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



The authors gratefully acknowledge the support of the Cancer Care Engineering (CCE) project, a joint effort between the Oncological Sciences Center (Purdue Center for Cancer Research, NCI P30CA023168) in the Purdue University Discovery Park and the Indiana University Melvin and Bren Simon Cancer Center (NCI P30CA082709). Support for the CCE project is gratefully acknowledged from the Walther Cancer Foundation, NIH (UL1RR025761), DOD (USAMRMC (CDMRP) W81XWH-008-1-0065, 9107003) and the Regenstrief Foundation. Additional financial support from NIH (R03CA211831 to N.G.), the Walther Cancer Foundation Bioinformatics grant, and the Cancer Center Support Grant P30CA015704-40) is also gratefully acknowledged.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no competing interests.

Ethical statement

Recruitment of patients and blood collections was made with written informed consent as per the approved Institutional Review Board protocols from Purdue University and Indiana University School of Medicine.

Supplementary material

11306_2017_1265_MOESM1_ESM.doc (219 kb)
Supplementary material 1 (DOC 219 KB)


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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Department of StatisticsPurdue UniversityWest LafayetteUSA
  2. 2.Northwest Metabolomics Research Center, Department of Anesthesiology and Pain MedicineUniversity of WashingtonSeattleUSA
  3. 3.Department of Chemistry & BiochemistryMiami UniversityOxfordUSA
  4. 4.Department of Electronic Science and Communication Engineering, State Key Laboratory for Physical Chemistry of Solid SurfacesXiamen UniversityXiamenChina
  5. 5.Indiana University Melvin and Bren Simon Cancer CenterIndianapolisUSA
  6. 6.Department of MedicineUniversity of WashingtonSeattleUSA
  7. 7.Department of Medicinal ChemistryPurdue UniversityWest LafayetteUSA
  8. 8.Bioinformatics Center, School of Biomedical EngineeringCapital Medical UniversityBeijingChina
  9. 9.Beijing Institute for Brain DisordersCapital Medical UniversityBeijingChina
  10. 10.Fred Hutchinson Cancer Research CenterSeattleUSA
  11. 11.Department of ChemistryPurdue UniversityWest LafayetteUSA

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