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  • br To evaluate the usefulness

    2020-08-18


    To evaluate the usefulness of this LDN-193189 novel panel of 6 biomarkers in detecting early-stage breast cancer, control subjects were analyzed against only stage I and II BC patients using a combination of univariate testing, chemometric analysis, and ROC evaluation. As shown in Table 3, these 6 differential metabolites were significant at the 0.05 level when comparing stage I and II BC patients to controls, as de-termined by GLM univariate testing. Moreover, an orthogonal PLS-DA (OPLS-DA) model constructed using these 6 metabolites showed ap-preciable differences between groups (Fig. 5A). Furthermore, as pre-sented in Fig. 5B, ROC analysis of the OPLS-DA model showed good classification performance (AUROC = 0.87, 95% CI: 0.82–0.92). As indicated by these results, the biomarker panel presented herein serves not only to distinguish BC patients from healthy controls but is also capable of discriminating stage I and II patients with localized disease from healthy control subjects with relatively high diagnostic accuracy, comparable to that of the all-stage cancer model.
    3.3. Factor analysis of metabolic data
    A secondary aim of this study was to relate detected metabolites to affected pathways. To this end, metabolite data were subjected to EFA [47]. This multivariate technique was performed on a reduced
    Significant metabolites for comparison of BC patients at different stages and controls.
    Stage I vs. controls
    Stage II vs. controls
    Stage I & II vs. controlsa
    Stage III vs. controls
    p FC
    p FC
    p FC
    p FC
    2-Hydroxybenzoic acid
    Myoinositol
    Proline
    Palmitic acid
    Hypoxanthine
    Indole
    Gentisic acid
    5-Aminolevulinic acid
    4-Pyridoxic acid
    Cytidine
    Nonadecanoic acid
    Stearic acid
    Agmatine
    Indole-3-acetic acid
    Pantothenic acid
    2,3-Dihydroxybenzoic acid
    Glycocyamine
    a Early-stage BC is regarded as stages I and II.
    Table 4
    Differences in metabolites of patients between cancer stages, and different ER, PR, HER2 status.
    Metabolites Cancer stages
    ER status
    PR status
    HER2 status
    Betaine
    Palmitic acid
    Asparagine
    2,3-Dihydroxybenzoic acid
    3-Indoxylsulfate
    Indole-3-acetic acid
    Hypoxanthine
    Gentisic acid
    Stearic acid
    Taurine
    5-Aminolevulinic acid
    Proline
    Pantothenic acid
    Cytidine
    correlation matrix of the 30 metabolites used for between-group com-parisons in order to determine pathways (i.e., factors) related to BC. Spectral decomposition of the experimental data matrix revealed a maximum of 4 factors (i.e., Kaiser criterion). Parallel analysis revealed only three factors accounted for more variance than random, permuted data (Supplemental Fig. S4). Subsequently, 1-, 2-, and 3-factor models were extracted and rotated in conformity with oblique promax and 
    infomax criteria, totaling 6 possible factor models. Each model was comparatively examined for percentage of total variance explained, magnitude of factor loadings, number of variables loaded onto each factor, and potential for meaningful factor interpretation and sub-sequent factor assignment. The 3-factor infomax model yielded the most satisfactory solution (Table 5). The findings revealed that 3 me-tabolites loaded significantly (> 0.50) on the first factor, 3 metabolites loaded significantly on the second factor, and 3 metabolites loaded significantly on the third factor. These factors were found to be re-presentative of the arginine/proline pathway, fatty acid biosynthesis, and tryptophan metabolism, respectively, suggesting significant al-terations of these pathways in patients diagnosed with breast cancer.
    3.4. Pathway analysis of metabolic data
    To understand the possible connection among detected plasma metabolites, we constructed metabolic pathway maps using IPA soft-ware [44], as shown in Fig. 6. MetaboAnalyst 4.0 [45] was used to perform pathway enrichment and topology analysis (Supplemental Fig. S5). It is worth mentioning that all metabolic pathways identified by our EFA as being significantly altered in BC patients were also identified by one or both of our bioinformatics analyses as being significantly altered as well; for instance, disturbances in arginine and proline me-tabolism as identified by EFA (Table 5) were corroborated by the results of our pathway analysis (Fig. 6) and enrichment analysis (Supplemental Fig. S5). As the results of our pathway, enrichment, and exploratory factor analyses are highly commensurate with each other, we can be reasonably confident that arginine/proline synthesis and degradation, fatty acid biosynthesis, and tryptophan metabolism are dysregulated in BC patients. Future studies can further target these networks for the discovery of pathway-specific biomarkers and potential therapeutic targets.