¹H NMR-based metabolic profiling of human rectal cancer tissue

Abstract

Background: Rectal cancer is one of the most prevalent tumor types. Understanding the metabolic profile of rectal cancer is important for developing therapeutic approaches and molecular diagnosis.

Methods: Here, we report a metabonomics profiling of tissue samples on a large cohort of human rectal cancer subjects (n = 127) and normal controls (n = 43) using 1H nuclear magnetic resonance (1H NMR) based metabonomics assay, which is a highly sensitive and non-destructive method for the biomarker identification in biological systems. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and orthogonal projection to latent structure with discriminant analysis (OPLS-DA) were applied to analyze the 1H-NMR profiling data to identify the distinguishing metabolites of rectal cancer.

Results: Excellent separation was obtained and distinguishing metabolites were observed among the different stages of rectal cancer tissues (stage I = 35; stage II = 37; stage III = 37 and stage IV = 18) and normal controls. A total of 38 differential metabolites were identified, 16 of which were closely correlated with the stage of rectal cancer. The up-regulation of 10 metabolites, including lactate, threonine, acetate, glutathione, uracil, succinate, serine, formate, lysine and tyrosine, were detected in the cancer tissues. On the other hand, 6 metabolites, including myo-inositol, taurine, phosphocreatine, creatine, betaine and dimethylglycine were decreased in cancer tissues. These modified metabolites revealed disturbance of energy, amino acids, ketone body and choline metabolism, which may be correlated with the progression of human rectal cancer.

Conclusion: Our findings firstly identify the distinguishing metabolites in different stages of rectal cancer tissues, indicating possibility of the attribution of metabolites disturbance to the progression of rectal cancer. The altered metabolites may be as potential biomarkers, which would provide a promising molecular diagnostic approach for clinical diagnosis of human rectal cancer. The role and underlying mechanism of metabolites in rectal cancer progression are worth being further investigated.

Figures

Figure 1

600 MHz representative 1H NMR spectra (δ9.5–δ0.5) of tissue samples. A normal control, B stage I of rectal cancer, C stage II of rectal cancer, D stage III of rectal cancer, E stage IV of rectal cancer.

Figure 2

Metabolite profiles between rectal cancer tissues and normal controls. A PCA scores plot discriminates metabolites from the rectal cancer tissues and normal controls using 1H NMR. B OPLS-DA scores plot based on same samples. C The color map shows the significance of metabolite variations between the two classes. Peaks in the positive direction indicated the increased metabolites in rectal cancer tissues in comparison to normal control. Deceased metabolites in rectal cancer tissues were presented as peaks in the negative direction. D Statistical validation of the corresponding PLS-DA model using permutation analysis (200 times). R2 is the explained variance, and Q2 is the predictive ability of the model.

Figure 3

Metabolite profiles between different stages of rectal cancer tissues and normal controls. A OPLS-DA scores plots based on each stages of rectal cancer tissues and normal controls; black triangles represent normal controls (n = 43); red diamonds represent stage I (n = 35); blue diamond’s represent stage II (n = 37); green diamonds represent stage III (n = 37); yellow diamonds represent stage IV (n = 18). B Color map showed the significance of metabolite variations between the classes. Peaks in the positive direction indicated the increased metabolites in rectal cancer tissues. Decreased metabolites in rectal cancer tissues were presented as peaks in the negative direction. C Statistical validation of the corresponding PLS-DA models using permutation analysis (200 times). R2 is the explained variance, and Q2 is the predictive ability of the model. D Scores plots of OPLS-DA prediction model. 80% of samples were applied to construct the model, and then used it to predict the remaining 20% of samples.

Figure 4

Box-and-whisker plots illustrating discrimination between different stages of rectal cancers and normal controls. Horizontal line in the middle portion of the box, median; bottom and top boundaries of boxes, 25th and 75th percentiles, respectively; lower and upper whiskers, 5th and 95th percentiles, respectively.

Figure 5

Disturbed metabolic pathways of the most relevant metabolites between rectal cancers and normal controls. Green: lower concentration in rectal cancer patients than in normal controls. Red: higher concentration in rectal cancer patients than in normal controls.