The Application Value of Lipoprotein Particle Numbers in the Diagnosis of HBV-Related Hepatocellular Carcinoma with BCLC Stage 0-A

Figure 1. The serum lipidomic profile discriminates between HCC patients and normal controls (NCs). (a) Score plot was generated by the OPLS-DA model. The horizontal coordinate (1.00484 * t [1]) represents the score value of the main component, and the vertical coordinate (1.22277 * to [1]) represents the score value of the orthogonal component. (b) Loading plot was generated by the OPLS-DA model. The horizontal coordinate (0.99337 * pq [1]) represents the predicted principal component, and the vertical coordinate (0.817815 * poso [1]) represents the orthogonal principal components. The corresponding (c) S-plot and (d) S-line plot for the model displaying the discriminant variables and the associated predictive loadings. The red circles indicate selected lipoprotein subfractions with VIP scores >1.0, and other variables with no difference are referred to as green circles in (b,c). (e) The selected lipoprotein subfractions with VIP scores >1.0. (f) Permutation test (999 times) of the OPLS-DA model.

Figure 2. The serum lipidomic profile discriminates between HCC patients and liver disease (LD). (a) Score plot was generated by the OPLS-DA model. The horizontal coordinate (1.00795 * t [1]) represents the score value of the main component, and the vertical coordinate (1.27104 * to [1]) represents the score value of the orthogonal component. (b) Loading plot was generated by the OPLS-DA model. The horizontal coordinate (0.96422 * pq [1]) represents the predicted principal component, and the vertical coordinate (0.786795 * poso [1]) represents the orthogonal principal components. The corresponding (c) S-plot and (d) S-line plot for the model displaying the discriminant variables and the associated predictive loadings. The red circles indicate selected lipoprotein subfractions with VIP scores >1.0, and other variables with no difference are referred to as green circles in (b,c). (e) The selected lipoprotein subfractions with VIP scores >1.0. (f) Permutation test (999 times) of the OPLS-DA model.

Figure 3. The serum lipidomic profile discriminates between AFP-negative HCC patients and NCs. (a) Score plot was generated by the OPLS-DA model. The horizontal coordinate (1.00453 * t [1]) represents the score value of the main component, and the vertical coordinate (1.2866 * to [1]) represents the score value of the orthogonal component. (b) Loading plot was generated by the OPLS-DA model. The horizontal coordinate (0.993661 * pq [1]) represents the predicted principal component, and the vertical coordinate (0.777245 * poso [1]) represents the orthogonal principal components. The corresponding (c) S-plot and (d) S-line plot for the model displaying the discriminant variables and the associated predictive loadings. The red circles indicate selected lipoprotein subfractions with VIP scores >1.0, and other variables with no difference are referred to as green circles in (b,c). (e) The selected lipoprotein subfractions with VIP scores >1.0. (f) Permutation test (999 times) of the OPLS-DA model.

Figure 4. The serum lipidomic profile discriminates between AFP-negative HCC patients and AFP-negative LD patients. (a) Score plot was generated by the OPLS-DA model. The horizontal coordinate (1.03844 * t [1]) represents the score value of the main component, and the vertical coordinate (1.12386 * to [1]) represents the score value of the orthogonal component. (b) Loading plot was generated by the OPLS-DA model. The horizontal coordinate (0.946694 * pq [1]) represents the predicted principal component, and the vertical coordinate (0.889789 * poso [1]) represents the orthogonal principal components. The corresponding (c) S-plot and (d) S-line plot for the model displaying the discriminant variables and the associated predictive loadings. The red circles indicate selected lipoprotein subfractions with VIP scores >1.0, and other variables with no difference are referred to as green circles in (b,c). (e) The selected lipoprotein subfractions with VIP scores >1.0. (f) Permutation test (999 times) of the OPLS-DA model.

Figure 5. Comparisons between the groups of diagnostic biomarkers. Histograms indicate the median, upper, and lower quartiles of the eight lipoprotein particle numbers used to construct the diagnostic panel. The significance of the values was assessed using the Kruskal–Wallis test (* p < 0.05, ** p < 0.001, *** p < 0.0001). HCC, all HCC patients in the training set; LD, all liver disease patients in the training set; AFP (-) HCC, serum AFP-negative expression HCC patients; AFP (-) LD, serum AFP-negative expression liver disease patients; NCs, normal controls.

Figure 6. ROC curve analysis following binary logistic regression. The diagnostic performance of the lipidomic biomarker panel in discriminating HCC from (a) normal controls (NCs) in the training set. Comparison of the diagnostic performance of the lipidomic biomarker panel with that of serum alpha-fetoprotein (AFP) and their combination in discriminating HCC from liver disease (LD) in the (b) training set and (c) validation set. The ROC curve of the lipidomic biomarker panel also showed excellent discriminability in discriminating AFP-negative HCC patients from (d) NCs and (e) AFP-negative LD patients. The red-, green-, and blue-colored lines indicate lipid biomarkers, AFP, and lipid biomarkers with AFP, respectively.

Figure 7. LPL levels are upregulated in HCC tissues and associated with poor prognosis. (a) LPL mRNA levels in HCC (red circle) and adjacent normal (blue square) tissues in the TCGA-LIHC dataset. The p-value was assessed using the Mann–Whitney U test (*** p < 0.0001). (b) Overall survival analysis was plotted using the TCGA database at the threshold p-value of < 0.05.