Serum Metabolomics Study of Papillary Thyroid Carcinoma Based on HPLC-Q-TOF-MS/MS

Abstract

This study examined metabolite profile differences between serum samples of thyroid papillary carcinoma (PTC) patients and healthy controls, aiming to identify candidate biomarkers and pathogenesis pathways in this cancer type. Serum samples were collected from PTC patients (n = 80) and healthy controls (n = 80). Using principal component analysis (PCA), partial least squares discrimination analysis(PLS-DA), orthogonal partial least square discriminant analysis (OPLS-DA), t-tests, and the volcano plot, a model of abnormal metabolic pathways in PTC was constructed. PCA, PLS-DA, and OPLS-DA analysis revealed differences in serum metabolic profiles between the PTC and control group. OPLS-Loading plot analysis, combined with Variable importance in the projection (VIP)>1, Fold change (FC) > 1.5, and p < 0.05 were used to screen 64 candidate metabolites. Among them, 22 metabolites, including proline betaine, taurocholic acid, L-phenylalanine, retinyl beta-glucuronide, alpha-tocotrienol, and threonine acid were upregulated in the PTC group; meanwhile, L-tyrosine, L-tryptophan, 2-arachidonylglycerol, citric acid, and other 42 metabolites were downregulated in this group. There were eight abnormal metabolic pathways related to the differential metabolites, which may be involved in the pathophysiology of PTC. Six metabolites yielded an area under the receiver operating curve of >0.75, specifically, 3-hydroxy-cis-5-tetradecenoylcarnitine, aspartylphenylalanine, l-kynurenine, methylmalonic acid, phenylalanylphenylalanine, and l-glutamic acid. The Warburg effect was observed in PTC. The levels of 3-hydroxy-cis-5-tetradecenoylcarnitine, aspartylphenylalanine, l-kynurenine, methylmalonic acid, phenylalanine, and L-glutamic acid may help distinguish PTC patients from healthy controls. Aspartic acid metabolism, glutamic acid metabolism, urea cycle, and tricarboxylic acid cycle are involved in the mechanism of PTC.

Keywords: metabolite profile; orthogonal partial least square discriminant analysis; principal component analysis; serum samples; thyroid papillary carcinoma.

Figure 1

(A,B) Volcano diagrams showing the changes in PTC and the most important metabolites of healthy subjects. (A) Represents the volcano map in positive ion mode, and (B) represents the volcano map in negative ion mode. The red dots in the figure represent differential metabolites, and the black dots represent no significant difference. The red dot on the right side of the figure represents the upregulated metabolite, and the red dot on the left side represents the downregulated metabolite. x-axis corresponds to log₂ (fold change) and y-axis corresponds to –log₁₀ (p-value). (C,D) Heatmap visualization of metabolomics data with hierarchical clustering analysis (HCA). (C) Represents the heatmap in positive ion mode, and (D) represents the heatmap in negative ion mode. The red color represents the peak value that is relatively large; the blue color represents the peak value that is relatively small; and the gray color represents the metabolite peak value of zero. The more similar the color, the more similar the peak value. The panel on the right represents the different metabolites. The upper dendritic structure is clustered according to the degree of metabolite similarity across samples. The red line below the dendritic structure represents the PTC group, and the green line represents the control group. PTC, papillary thyroid carcinoma; Control, healthy subject.

Figure 2

PCA (A,B), PLS-DA (C,D), and OPLS-DA (G,H) analysis score scatter plots illustrating that the metabolic profiles of PTC are distinct from those of healthy subjects. PLS-DA and OPLS-DA analysis score scatter plots for metabolic profiles of healthy subjects (green dots) and PTC patients (blue squares) showing clear discrimination between the two groups. (E,F,I,J) Permutation test was used to assess the reliability of the models. ROC curve analyses of the ability of six metabolites to predict PTC patients and healthy subject (K–P). PCA, principal component analysis; PLS-DA, partial least squares discrimination analysis; OPLS-DA, orthogonal partial least squares-discriminant analysis; QC, Quality Control; PTC, papillary thyroid carcinoma; Control, healthy subject. ROC, receiver operating characteristic.

Figure 3

(A) Results of pathway analysis of metabolomics data. Pathway analysis based on “Kyoto Encyclopedia of Genes and Genomes” (KEGG).The color and size of each circle is based on p-values (yellow: higher p-values and red: lower p-values) and pathway impact values (the larger the circle the higher the impact score) calculated from the topological analysis, respectively. Pathways were considered significantly enriched if p < 0.05, impact >0.1 and number of metabolite hits in the pathway >1. PTC, papillary thyroid carcinoma. (B) The significantly enriched pathways involved in the pathogenesis of papillary thyroid carcinoma, including Aspartate metabolism, Glutamate metabolism, Urea cycle, and TCA cycle.