Advertisement

Metabolomics

, 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

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

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

Notes

Acknowledgements

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)

References

  1. Ahrens, H. (1971). Multivariate analysis. Krishnaiah Paruchuri R. (Ed.) New York: Academic Press Inc.Google Scholar
  2. Aiken, L. S., & West, S. G. (1991). Multiple regression: testing and interpreting interactions. Newbury Park, CA: Sage Publications, Inc.Google Scholar
  3. Cancer Facts & Figs. 2013. American Cancer Society: Atlanta, GA, 2013. https://www.cancer.org/research/cancer-facts-statistics/all-cancer-facts-figures/cancer-facts-figures-2013.html. Accessed on June 14, 2017.
  4. Carroll, R. J., Midthune, D., Freedman, L. S., & Kipnis, V. (2006). Seemingly unrelated measurement error models, with application to nutritional epidemiology. Biometrics, 62(1), 75–84.CrossRefPubMedGoogle Scholar
  5. Chan, E. C. Y., Koh, P. K., Mal, M., Cheah, P. Y., Eu, K. W., Backshall, A., Cavill, R., Nicholson, J. K., & Keun, H. C. (2008). Metabolic profiling of human colorectal cancer using high-resolution magic angle spinning nuclear magnetic resonance (HR-MAS NMR) spectroscopy and gas chromatography mass spectrometry (GC/MS). Journal of Proteome Research, 8(1), 352–361.CrossRefGoogle Scholar
  6. Chen, C., Deng, L., Wei, S., Nagana Gowda, G. A., Gu, H., Chiorean, E. G., Abu Zaid, M., Harrison, M. L., Pekny, J. F., Loehrer, P. J., Zhang, D., Zhang, M., & Raftery, D. (2015). Exploring metabolic profile differences between colorectal polyp patients and controls using seemingly unrelated regression. Journal of Proteome Research, 14(6), 2492–2499.CrossRefPubMedPubMedCentralGoogle Scholar
  7. DeBerardinis, R. J., & Chandel, N. S. (2016). Fundamentals of cancer metabolism. Science Advances, 2(5), e1600200.CrossRefPubMedPubMedCentralGoogle Scholar
  8. Denkert, C., Budczies, J., Weichert, W., Wohlgemuth, G., Scholz, M., Kind, T., Niesporek, S., Noske, A., Buckendahl, A., Dietel, M., & Fiehn, O. (2008). Metabolite profiling of human colon carcinoma - deregulation of TCA cycle and amino acid turnover. Molecular Cancer, 7(1), 72.CrossRefPubMedPubMedCentralGoogle Scholar
  9. Eisner, R., Greiner, R., Tso, V., Wang, H., & Fedorak, R. N. (2013). A machine-learned predictor of colonic polyps based on urinary metabolomics. BioMed Research International, 2013, 303982.CrossRefPubMedPubMedCentralGoogle Scholar
  10. Gross, S., Cairns, R. A., Minden, M. D., Driggers, E. M., Bittinger, M. A., Jang, H. G., Sasaki, M., Jin, S., Schenkein, D. P., Su, S. M., Dang, L., Fantin, V. R., & Mak, T. W. (2010). Cancer-associated metabolite 2-hydroxyglutarate accumulates in acute myelogenous leukemia with isocitrate dehydrogenase 1 and 2 mutations. The Journal of Experimental Medicine, 207(2), 339–344.CrossRefPubMedPubMedCentralGoogle Scholar
  11. Jain, M., Nilsson, R., Sharma, S., Madhusudhan, N., Kitami, T., Souza, A. L., Kafri, R., Kirschner, M. W., Clish, C. B., & Mootha, V. K. (2012).Metabolite profiling identifies a key role for glycine in rapid cancer cell proliferation. Science, 336(6084), 1040–1044.CrossRefPubMedPubMedCentralGoogle Scholar
  12. Li, F., Qin, X., Chen, H., Qiu, L., Guo, Y., Liu, H., Chen, G., Song, G., Wang, X., Li, F., Guo, S., Wang, B., & Li, Z. (2013). Lipid profiling for early diagnosis and progression of colorectal cancer using direct infusion electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry. Rapid Communications in Mass Spectrometry, 27(1), 24–34.CrossRefPubMedGoogle Scholar
  13. Lin, J. S., Piper, M. A., Perdue, L. A., Rutter, C. M., Webber, E. M., O’Connor, E., Smith, N., & Whitlock, E. P. (2016). Screening for Colorectal Cancer: Updated Evidence Report and Systematic Review for the US Preventive Services Task Force. The Journal of the American Medical Association, 315(23), 2576–2594.CrossRefPubMedGoogle Scholar
  14. Ma, Y.-L., Qin, H.-L., Liu, W.-J., Peng, J.-Y., Huang, L., Zhao, X.-P., & Cheng, Y.-Y. (2009). Ultra-high performance liquid chromatography mass spectrometry for the metabolomic analysis of urine in colorectal cancer. Digestive Diseases and Sciences, 54(12), 2655–2662.CrossRefPubMedGoogle Scholar
  15. Munoz-Pinedo, C., El Mjiyad, N., & Ricci, J. E. (2012) Cancer metabolism: current perspectives and future directions. Cell Death and Disease 3, e248.CrossRefPubMedPubMedCentralGoogle Scholar
  16. Nishiumi, S., Kobayashi, T., Ikeda, A., Yoshie, T., Kibi, M., Izumi, Y., Okuno, T., Hayashi, N., Kawano, S., Takenawa, T., Azuma, T., & Yoshida, M. (2012). A novel serum metabolomics-based diagnostic approach for colorectal cancer. PLoS ONE, 7(7), e40459.CrossRefPubMedPubMedCentralGoogle Scholar
  17. Pickhardt, P. J. (2016). Emerging stool-based and blood-based non-invasive DNA tests for colorectal cancer screening: the importance of cancer prevention in addition to cancer detection. Abdominal Radiology, 41, 1441–1444.CrossRefPubMedPubMedCentralGoogle Scholar
  18. Qiu, Y., Cai, G., Su, M., Chen, T., Zheng, X., Xu, Y., Ni, Y., Zhao, A., Xu, L. X., Cai, S., & Jia, W. (2009). Serum metabolite profiling of human colorectal cancer using GC-TOFMS and UPLC-QTOFMS. Journal of Proteome Research, 8(10), 4844–4850.CrossRefPubMedGoogle Scholar
  19. Ritchie, S., Ahiahonu, P., Jayasinghe, D., Heath, D., Liu, J., Lu, Y., Jin, W., Kavianpour, A., Yamazaki, Y., Khan, A., Hossain, M., Su-Myat, K., Wood, P., Krenitsky, K., Takemasa, I., Miyake, M., Sekimoto, M., Monden, M., Matsubara, H., Nomura, F., & Goodenowe, D. (2010). Reduced levels of hydroxylated, polyunsaturated ultra long-chain fatty acids in the serum of colorectal cancer patients: implications for early screening and detection. BMC Medicine, 8(1), 13.CrossRefPubMedPubMedCentralGoogle Scholar
  20. Ritchie, S. A., Tonita, J., Alvi, R., Lehotay, D., Elshoni, H., Myat, S., McHattie, J., & Goodenowe, D. B. (2013) Low-serum GTA-446 anti-inflammatory fatty acid levels as a new risk factor for colon cancer. International Journal of Cancer, 132(2), 355–362.CrossRefPubMedGoogle Scholar
  21. Saint-Pierre, A., Kaufman, J. M., Ostertag, A., Cohen-Solal, M., Boland, A., Toye, K., Zelenika, D., Lathrop, M., de Vernejoul, M. C., & Martinez, M. (2011). Bivariate association analysis in selected samples: application to a GWAS of two bone mineral density phenotypes in males with high or low BMD. European Journal of Human Genetics, 19(6), 710–716.CrossRefPubMedPubMedCentralGoogle Scholar
  22. Schafer, J., & Strimmer, K. (2005). An Empirical Bayes Approach to Inferring Large-Scale Gene Association Networks. Bioinformatics, 21(6), 754–764.CrossRefPubMedGoogle Scholar
  23. Siegel, R., Miller, K. D., & Jemal, A. (2017). Cancer statistics, 2016. CA- Cancer Journal for Clinicians, 67, 7–30.CrossRefGoogle Scholar
  24. Sreekumar, A., Poisson, L. M., Rajendiran, T. M., Khan, A. P., Cao, Q., Yu, J., Laxman, B., Mehra, R., Lonigro, R. J., Li, Y., Nyati, M. K., Ahsan, A., Kalyana-Sundaram, S., Han, B., Cao, X., Byun, J., Omenn, G. S., Ghosh, D., Pennathur, S., Alexander, D. C., Berger, A., Shuster, J. R., Wei, J. T., Varambally, S., Beecher, C., & Chinnaiyan, A. M. (2009). Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression. Nature, 457(7231), 910–914.CrossRefPubMedPubMedCentralGoogle Scholar
  25. Tan, B., Qiu, Y., Zou, X., Chen, T., Xie, G., Cheng, Y., Dong, T., Zhao, L., Feng, B., Hu, X., Xu, L. X., Zhao, A., Zhang, M., Cai, G., Cai, S., Zhou, Z., Zheng, M., Zhang, Y., & Jia, W. (2013). Metabonomics identifies serum metabolite markers of colorectal cancer. Journal of Proteome Research, 12(6), 3000–3009.CrossRefPubMedGoogle Scholar
  26. Taylor, D. P., Cannon-Albright, L. A., Sweeney, C., Williams, M. S., Haug, P. J., Mitchell, J. A., & Burt, R. W. (2011). Comparison of compliance for colorectal cancer screening and surveillance by colonoscopy based on risk. Genetics in Medicine, 13(8), 737–743.CrossRefPubMedGoogle Scholar
  27. Warburg, O. (1956). On the origin of cancer cells. Science, 123, 309–314.CrossRefPubMedGoogle Scholar
  28. Ward, P. S., & Thompson, C. B. (2012). Metabolic reprogramming: A cancer hallmark even warburg did not anticipate. Cancer Cell, 21(3), 297–308.CrossRefPubMedPubMedCentralGoogle Scholar
  29. Wise, D. R., & Thompson, C. B. (2010). Glutamine addiction: A new therapeutic target in cancer. Trends in Biochemical Sciences, 35(8), 427–433.CrossRefPubMedPubMedCentralGoogle Scholar
  30. Zellner, A. (1962). An efficient method of estimating seemingly unrelated regressions and tests for aggregation bias. Journal of the American Statistical Association, 57(298), 348–368.CrossRefGoogle Scholar
  31. Zhu, J., Djukovic, D., Deng, L., Gu, H., Himmati, F., Chiorean, E. G., & Raftery, D. (2014). Colorectal cancer detection using targeted serum metabolic profiling. Journal of Proteome Research, 13(9), 4120–4130.CrossRefPubMedGoogle Scholar

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

Personalised recommendations