Abstract
Colorectal cancer (CRC) is a growing cause of mortality in developing countries, warranting investigation into its earlier detection for optimal disease management. A metabolomics based approach provides potential for noninvasive identification of biomarkers of colorectal carcinogenesis, as well as dissection of molecular pathways of pathophysiological conditions. Here, proton nuclear magnetic resonance spectroscopy (1HNMR) -based metabolomic approach was used to profile fecal metabolites of 68 CRC patients (stage I/II=20; stage III=25 and stage IV=23) and 32 healthy controls (HC). Pattern recognition through principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) was applied on 1H-NMR processed data for dimension reduction. OPLS-DA revealed that each stage of CRC could be clearly distinguished from HC based on their metabolomic profiles. Successive analyses identified distinct disturbances to fecal metabolites of CRC patients at various stages, compared with those in cancer free controls, including reduced levels of acetate, butyrate, propionate, glucose, glutamine, and elevated quantities of succinate, proline, alanine, dimethylglycine, valine, glutamate, leucine, isoleucine and lactate. These altered fecal metabolites potentially involved in the disruption of normal bacterial ecology, malabsorption of nutrients, increased glycolysis and glutaminolysis. Our findings revealed that the fecal metabolic profiles of healthy controls can be distinguished from CRC patients, even in the early stage (stage I/II), highlighting the potential utility of NMR-based fecal metabolomics fingerprinting as predictors of earlier diagnosis in CRC patients.
Highlights
Colorectal cancer (CRC) is one of the most prevalent types of cancer, ranking as the 3rd most common malignancy and the 4th leading cause of cancer death worldwide [1]
Principal component analysis (PCA) was initially carried out to generate an overview of the variations between CRC patients and healthy controls, and some trends in differences were detected on the scores plot of first two principal components (PC) (Figure 2A)
The predictive www.impactjournals.com/oncotarget ability of the model was measured by internal validation (R2Y= 0.791, Q2 = 0.601, CV-ANOVA p-value < 0.01), suggesting that the model possessed a satisfactory fit with good predictive power, and the metabolite differences between the groups within the model were highly significant
Summary
Colorectal cancer (CRC) is one of the most prevalent types of cancer, ranking as the 3rd most common malignancy and the 4th leading cause of cancer death worldwide [1]. Patients with early stage CRC have significantly higher 5-year survival rates compared to patients diagnosed at later stages [2]. There is a need for better non-invasive clinical tools to improve detection of the disease in its early stages. Preventive screening and detection methods for CRC rely upon clinical, endoscopic, histologic, and radiographic techniques that can be time-consuming, invasive and costly. Colonoscopy remains the gold standard to diagnose CRC, it is invasive, expensive, and uncomfortable [3]. While non-invasive www.impactjournals.com/oncotarget stool-based tests, such as fecal occult blood test (FOBT) and fecal immunochemical test (FIT), are convenient methods for screening CRC, their sensitivity are low, which reduce their reliability [4]. It is essential to obtain an accurate, noninvasive, inexpensive and early diagnosis of CRC, for optimal disease management
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