Identification of glycolysis- and myocardial fibrosis-related diagnostic biomarkers in heart failure: an integrative bioinformatics and machine learning study

Figures

• 

  Figure 1. Principal component analysis (PCA) of the GSE57338 dataset based on the normalized expression matrix. Samples are colored by group (red: heart failure, blue: control). The first two principal components (PC1 and PC2) are shown, with the percentage of variance explained indicated in parentheses. The PCA plot demonstrates the overall transcriptional separation between HF and control samples.

• 

  Figure 2. Differential expression analysis. (A) Volcano plot of differentially expressed genes (DEGs) between the HF and control groups in dataset GSE57338. (B) Venn diagram showing the intersection between DEGs and glycolysis- and myocardial fibrosis-related genes (GMFRGs) in dataset GSE57338. (C) Heatmap of glycolysis- and myocardial fibrosis-related differentially expressed genes (GMFRDEGs) in dataset GSE57338.

• 

  Figure 3. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA). A. Enrichment plot for Marzec Il2 signaling dn (NES=1.901, p=0.006). B. Enrichment plot for Gaba metabolism aka ghb (NES=1.780, p=0.021). C,D. Heatmap (C) and bidirectional bar plot (D) of GSVA results for dataset GSE57338 (HF vs. Control). E. Enrichment plot for Chen Lvad support of failing heart dn (NES=2.178, p=0.004). F. Enrichment plot for Nakamura cancer microenvironment up (NES=2.121, p=0.006). G. Enrichment plot for Medicus reference glycolysis (NES=2.098, p=0.024). H. Enrichment plot for Stambolsky targets of mutated tp53 dn (NES=2.057, p=0.009). I,J. Heatmap (I) and bidirectional bar plot (J) of GSVA results for the GSE76701 validation dataset (HF vs. Control). All enrichment plots were generated using the clusterProfiler package, and GSVA was performed using the GSVA package with the c2.cp.v2023.2.Hs.symbols gene set.

• 

  Figure 4. Forest plot and analysis via machine learning. A. Forest plot of 14 glycolysis- and myocardial fibrosis-related differentially expressed genes (GMFRDEGs) with p value < 0.05 in the logistic regression model. B,C. Visualization of the number of genes with the lowest error rate (B) and highest accuracy (C) obtained using the SVM-RFE model. D. Lollipop plot showing the average importance ranking for the top five genes with the highest accuracy obtained via the SVM-RFE model. E,F. Diagnostic model plot (E) and variable trajectory plot (F) of the LASSO regression model.

• 

  Figure 5. Diagnostic and validation analysis of the HF model. A. Receiver operating characteristic (ROC) curve of the logistic regression model for dataset GSE57338 (AUC=0.983, 95% CI: 0.968–0.999). B,C. Calibration curve plot (B) and decision curve analysis (DCA) (C) for the HF diagnostic model. D. Nomogram of the key genes. E. Functional similarity (Friends) analysis of key genes. F,G. ROC curve (F) and DCA (G) for the external validation dataset GSE76701. AUC > 0.9 indicates high accuracy; DCA y-axis: net benefit, x-axis: threshold probability. The ROC curves were generated using the pROC package, calibration curves were plotted using the rms package, and DCA was performed using the ggDCA package.

• 

  Figure 6. Differential expression, correlation analysis, and receiver operating characteristic (ROC) curves for key genes in the GSE57338 dataset. A. Comparison of key genes between the HF and control groups in dataset GSE57338 (purple: HF, yellow: control). B–D. ROC curves for HBB (AUC=0.831, 95% CI: 0.781–0.881) and LUM (AUC=0.982, 95% CI: 0.968–0.997) (B), NPPA (AUC=0.866, 95% CI: 0.822–0.910) and OGN (AUC=0.943, 95% CI: 0.916–0.970) (C), and SERPINA3 (AUC=0.998, 95% CI: 0.995–1.000) (D) in dataset GSE57338. E. Correlation heatmap of key genes (with red and blue indicating positive and negative correlations, respectively, and color intensity denoting the correlation strength). F–I. Scatter plots of pairwise correlations between LUM and OGN (F), LUM and SERPINA3 (G), OGN and SERPINA3 (H), and NPPA and LUM (I). Significance: ns p ≥ 0.05; * p < 0.05; ** p < 0.01; *** p < 0.001. AUC > 0.9: high accuracy. |r|: < 0.3 (weak/no correlation), 0.3–0.5 (weak), 0.5–0.8 (moderate), > 0.8 (strong).

• 

  Figure 7. Go and kEGG enrichment analysis of the key genes. A. Bubble plot of GO terms, i.e., biological processes (BP), cellular components (CC), and molecular functions (MF), and KEGG pathways. The bubble size and color indicate the gene count and adjusted p-value (red: lower p adjust), respectively. (B–E) Network plots for BP (B), CC (C), MF (D), and KEGG (E) The pink nodes denote GO terms, whereas the lines indicate term–gene relationships. The red and blue nodes indicate upregulated and downregulated genes, respectively. Significance threshold: p adjust < 0.05 and FDR < 0.25 (BH correction).

• 

  Figure 8. Analysis of protein–protein interactions and regulatory networks for key genes. A. Interaction network of functionally similar genes predicted using GeneMANIA. The circles indicate key genes and functionally similar genes, whereas colored lines denote interrelated functions. B. Protein–protein interaction (PPI) network for key genes based on the STRING database. C. mRNA–TF regulatory network. D. mRNA–miRNA regulatory network. E. mRNA–RBP regulatory network. F. mRNA–drug regulatory network for key genes. Color code: orange, mRNA; blue, transcription factor (TF); purple/gray, miRNA; green, RNA-binding protein (RBP); and pink, drug.

• 

  Figure 9. Differential expression, correlation analysis and receiver operating characteristic (ROC) curves for key genes in the GSE76701 dataset. A. Comparison of key genes between the HF and control groups (purple: HF, yellow: control). B–D. ROC curves for HBB (AUC=1.000, 95% CI: 1.000–1.000) and LUM (AUC=0.875, 95% CI: 0.515–1.000) (B), NPPA (AUC=0.813, 95% CI: 0.438–1.000) and OGN (AUC=0.688, 95% CI: 0.208–1.000) (C), and SERPINA3 (AUC=1.000, 95% CI: 1.000–1.000) (D) E Correlation heatmap of key genes (with red and blue indicating positive and negative correlations, respectively, and color intensity denoting the correlation strength). F–I. Scatter plots of correlations between OGN and LUM (F), HBB and SERPINA3 (G), LUM and SERPINA3 (H), and NPPA and SERPINA3 (I). Significance: ns p ≥ 0.05; * p < 0.05; ** p < 0.01; *** p < 0.001. AUC > 0.9: high accuracy. |r|: < 0.3 (weak/no correlation), 0.3–0.5 (weak), 0.5–0.8 (moderate), > 0.8 (strong).

• 

  Figure 10. Immune infiltration analysis by single-sample gene set enrichment analysis (ssGSEA). A. Comparison of immune cells between the low-risk and high-risk groups within the HF group (purple: high-risk, yellow: low-risk). B,C. Correlation matrices for the abundance of infiltrating immune cells in the low-risk (B) and high-risk (C) groups (with red and blue indicating positive and negative correlations, respectively, and color intensity denoting the correlation strength). D,E. Bubble plots of correlations between immune cell abundance and key genes in the low-risk (D) and high-risk (E) groups. The bubble size indicates p-value intervals (the larger the bubble, the more significant the correlation). Correlation strength (|r|): <0.3 (weak/no correlation), 0.3–0.5 (weak), 0.5–0.8 (moderate), > 0.8 (strong). Significance: ns p ≥ 0.05; * p < 0.05; ** p < 0.01; *** p < 0.001.

• 

  Figure 11. Gene set enrichment analysis for dataset GSE57338 A. Mountain plot of enriched biological functions in the HF group for dataset GSE57338 (color: adjusted p-value, red: smaller, blue: larger). B–E. Enrichment plots showing significant enrichment of Medicus reference Wnt signaling modulation Sost Lrp4 (B), Sakai tumor infiltrating monocytes up (C), Dilated cardiomyopathy (D), and Yauck Hedgehog signaling paracrine dn (E) Significance threshold: adjusted p < 0.05 and FDR < 0.05 (BH correction).

• 

  Figure 12. Gene set variation analysis (GSVA) of the high-risk and low-risk groups for dataset GSE57338. A. Heatmap of significantly enriched pathways (the blue and red colors indicate low and high enrichment, respectively). B. Bidirectional bar plot of GSVA results for the high-risk (purple) and low-risk (yellow) groups. The top 10 positively and top 10 negatively enriched pathways are shown (p < 0.05).