Altered metabolite levels and correlations in patients with colorectal cancer and polyps detected using seemingly unrelated regression analysis
- 436 Downloads
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.
KeywordsSeemingly 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.
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.
- Ahrens, H. (1971). Multivariate analysis. Krishnaiah Paruchuri R. (Ed.) New York: Academic Press Inc.Google Scholar
- Aiken, L. S., & West, S. G. (1991). Multiple regression: testing and interpreting interactions. Newbury Park, CA: Sage Publications, Inc.Google Scholar
- 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.
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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