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  • br A visual representation of these affected pathways is


    A visual representation of these affected pathways is given in Fig. 7. Of particular interest is the high level of agreement in results observed among the statistical analyses employed in this study. Results of our enrichment analysis, factor analysis, and pathway analysis unanimously connote arginine and proline metabolism as being altered in breast cancer patients. In this pathway, levels of agmatine and glycocyamine, both closely related to arginine, were significantly reduced in stage III breast cancer patients; an even greater mean reduction in proline was observed in BC patients of all stages. In addition, the results of our factor and pathway analyses both showed tryptophan metabolism to be highly dysregulated in BC patients. Between cancer patients and con-trols, univariate testing showed indole and its product indole-3-acetate to be significantly up- and down-regulated, respectively. Finally, our results indicated fatty LDN 193189 metabolism as being significantly altered
    Fig. 3. Box plots of 6 differential metabolites with both q < 0.05 and VIP > 1 for comparison between BC patients and healthy controls: (A) Proline, (B) myoi-nositol, (C) 2-hydroxybenzoic acid, (D) gentisic acid, (E) hypoxanthine, and (F) 2,3-dihydroxybenzoic acid.
    Fig. 4. ROC curve illustrating the classification performance of biomarker panel with 6 differential metabolites and age for distinguishing between BC patients and healthy controls. [AUROC = 0.89, 95% CI: 0.85–0.93, sensitivity: 0.80, specificity: 0.75].
    Table 5
    Reduced factor loading matrix with significantly altered metabolites as vari-ables.
    Variable Factor 1 Factor 2 Factor 3
    (arginine/proline (fatty acid (tryptophan
    metabolism) biosynthesis) metabolism)
    between cancer patients and controls. Levels of palmitate were ubi-quitously higher in BC patients of all cancer stages. The effects of breast cancer on tryptophan and fatty acid metabolism were corroborated by two separate analytic techniques, while arginine and proline metabo-lism was shown to be significantly affected by all three bioinformatics analyses. Our results provided strong metabolic pathway candidates for future studies looking to investigate the underlying biological me-chanisms of breast cancer pathology.
    Nonsignificant metabolites of glycolysis and the TCA cycle are also graphed alongside significantly altered metabolites in Fig. 7. By and
    large, both lactate and alpha-ketoglutarate have been observed to be significantly increased in human breast cancer patients [29,35,36], and have been attributed to the Warburg effect and glutamine addiction, respectively. However, no such increase in the levels of either meta-bolite was detected in our data set. This could be due to a lack of sta-tistical power and may need to be validated with future studies.
    4. Discussion
    For the last 20 years, significant interest has grown in utilizing mass spectrometry for the detection and analysis of cancer-related metabolic alterations. In doing so, these efforts have borne highly valuable diag-nostic information and elucidation of probable biological mechanisms of cancer initiation and proliferation [12,29,48]. In the current study, we presented a combination of targeted metabolomics and multivariate statistics for the discovery of sensitive and specific BC metabolic bio-markers. Using this particular LC-MS/MS approach, 105 metabolites from many relevant metabolic pathways were reliably detected in both positive and negative ionization modes. Our multi-step biomarker se-lection, supervised model construction, and subsequent cross-validation have effectively demonstrated the robust diagnostic power of our me-tabolic profiling method in this study of 201 subjects.
    To date, a number of studies have implemented mass spectrometry-based methods for the detection of metabolic alterations linked to breast cancer [49–52]. Typically, these studies have effected global metabolic profiling approaches. Global analytical platforms tend to
    Fig. 6. Overlapping canonical pathway constructed using identified plasma metabolites showing the interrelationship between (A) arginine/proline/citrulline metabolism, and (B) affected mechanisms of tryptophan and purine metabolism, by way of the kynurenine pathway. Yellow, orange, and red mechanisms are reflective of small, medium, and large disturbances, respectively. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
    Fig. 7. Metabolic map showing significant differences in arginine/proline, tryptophan, and fatty acid metabolism between control subjects and staged BC patients. Detected metabolites in the pathways are graphed using normalized data. Solid arrows represent direct connections between metabolites; dashed lines designate abridged connections. *, p < 0.05; ***, p < 0.001 compared to healthy controls.