Identification of Metabolism-Related Hub Genes in Heart Failure via Comprehensive Transcriptome Analysis

Figures

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  Figure 1. Flow chart.

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  Figure 2. The identification of DEGs in HF. (A) A volcano plot showing significantly upregulated and downregulated genes in HF; (B) a heatmap showing the expression of the top 200 DEGs via hierarchical clustering; (C) a chord diagram showing the GO enrichment analysis results for DEGs in HF; (D) a Sankey diagram showing the pathway of KEGG enrichment analysis for DEGs in HF. DEGs: differentially expressed genes; GO: Gene Ontology; NF: non-failing; HF: heart failure; KEGG: Kyoto Encyclopedia of Genes and Genomes.

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  Figure 3. Identification and enrichment analysis of metabolism-related hub genes in HF. (A) The Venn diagram shows the overlap of DEGs (red circle) and metabolism-related genes (blue circle); (B) enrichment results for the CC category in GO analysis; (C) enrichment results for the MF category in GO analysis; (D) enrichment results for the BP category in GO analysis; (E) the results of enrichment analysis of KEGG pathways. CC: cellular component, DEGs: differentially expressed genes, GO: Gene Ontology, HF: heart failure; KEGG: Kyoto Encyclopedia of Genes and Genomes; MF: molecular function; BP: biological process.

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  Figure 4. Screening of hub genes using machine learning. (A) Key feature genes were screened by performing LASSO regression analysis; (B) the top 10 most important genes identified by Random Forest based on Mean Decrease Gini; (C) genes ranked by importance based on their Gain in the XGBoost method; (D) a Venn diagram illustrating the overlap of important genes identified through the three machine learning methods. LASSO: least absolute shrinkage and selection operator; XGBoost: Extreme Gradient Boosting.

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  Figure 5. The identification of gene modules in HF using WGCNA. (A) A similarity heatmap of co-expression modules of feature genes; (B) a gene module hierarchical clustering tree; (C) a module–trait relationship heatmap showing the correlation between different modules and traits; (D) the relationship between MM and GS in the turquoise module. WGCNA: Weighted Gene Co-expression Network Analysis; MM: module membership; GS: gene significance; HF: heart failure; NF: non-failing.

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  Figure 6. SDC2 is a key gene associated with HF. (A) A Venn diagram showing the overlap of important genes selected using machine learning and WGCNA; (B) a violin plot of SDC2 gene mRNA expression; (C) cardiac function of AngII-induced HF mouse (n = 6 per group); (D) SDC2 mRNA expression was experimentally detected in the myocardial tissue of a mouse HF model using RT-qPCR (n = 6 per group); (E) SDC2 protein expression was experimentally detected in the myocardial tissue of a mouse HF model using a western blot (n = 6 per group). Data are presented as mean ± SD. HF: heart failure; NF: non-failing; RT-qPCR: reverse transcription quantitative PCR; SDC2: Syndecan 2; WGCNA: Weighted Gene Co-expression Network Analysis. * p < 0.05, ** p < 0.01.

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  Figure 7. The association between metabolic changes and SDC2 expression in HF. (A) Correlation analysis of SDC2 expression with metabolism pathways. (B) Differential metabolites in the HF group compared with the NF group. (C) KEGG enrichment analysis of differential metabolites between HF and NF; (D) ROC curves of SDC2 in the GSE116250 and integrated dataset showing the HF diagnostic performance. HF: heart failure; KEGG: Kyoto Encyclopedia of Genes and Genomes; NF: non-failing; ROC: receiver operating characteristic; SDC2: Syndecan 2.