Identification and validation of ANXA3 and SOCS3 as biomarkers for acute myocardial infarction related to sphingolipid metabolism

Abstract

Background: Sphingolipid metabolism (SM) is linked to acute myocardial infarction (AMI), but its role remains unclear. This study explored SM-related genes (SMRGs) in AMI to support clinical diagnosis.

Methods: We analyzed datasets GSE48060 and GSE123342 to identify differentially expressed genes (DEGs) and key module genes. Protein-protein interaction (PPI) network analysis and machine learning were used to screen potential biomarkers, which were validated via receiver operating characteristic (ROC) curves and expression assessment. Further analyses included artificial neural networks (ANN), enrichment analysis, immune infiltration, drug prediction, and molecular docking. Single-cell RNA sequencing (scRNA-seq) identified key cell types and their functions. Biomarkers were validated via reverse transcription quantitative polymerase chain reaction (RT-qPCR).

Results: Intersection of 95 DEGs and 2,196 module genes yielded 20 genes, with ANXA3 and SOCS3 identified as biomarkers. The ANN model showed superior diagnostic performance compared to individual markers. Biomarkers were enriched in the toll-like receptor (TLR) signaling pathway. Immune infiltration analysis revealed differences in five immune cell types between AMI and control groups. ANXA3 correlated positively with neutrophils and negatively with resting memory CD4 T cells. Drugs targeting ANXA3 included ethanolamine, difluocortolone, and fluocinolone acetonide, with strong binding affinity. scRNA-seq identified B cells and monocytes as key cells; ANXA3 and SOCS3 expression increased during monocyte differentiation before decreasing, while B cells showed no significant changes.

Conclusion: ANXA3 and SOCS3 were identified as SM-related biomarkers in AMI, providing insights for clinical diagnosis.

Keywords: Acute myocardial infarction; Immune microenvironment; Machine learning; Single cell RNA analysis; Sphingolipid metabolism.

Fig. 1

Differential gene expression analysis and WGCNA in AMI (a) Volcano plot depicting differentially expressed genes (DEGs) between patients with AMI and control samples, highlighting upregulated (red) and downregulated (blue) genes. (b) Heatmap displaying the number of upregulated versus downregulated DEGs in the AMI dataset. (c) Plot showing the selection of the soft-thresholding power for constructing a scale-free network in WGCNA. (d) Cluster dendrogram from WGCNA identifying gene modules associated with sphingolipid metabolism in AMI. (e) Heatmap illustrating the correlation between identified gene modules and SMRGs

Fig. 2

Identification and functional analysis of candidate genes (a) Venn diagram depicting the intersection of DEGs and sphingolipid metabolism-related module genes. (b) Chord diagram illustrating GO term enrichment of the intersecting genes, focusing on biological processes, molecular functions, and cellular components. (c) Bar graph showing the KEGG pathway enrichment of intersecting genes. (d) Protein-protein interaction (PPI) network of the intersecting genes, highlighting significant interactions. (e) Identification of top candidate genes using network analysis tools, emphasizing centrality measures. (f) Interaction network of top candidate genes, outlining potential regulatory roles in AMI. *p < 0.05, **p < 0.01, ***p < 0.001 represented comparison of AMI with control group

Fig. 3

Validation of ANXA3 and SOCS3 as biomarkers (a) Feature selection using the SVM-REF algorithm for candidate genes. (b) Feature selection via the XGBoost algorithm for candidate genes. (c) Venn diagram illustrating the overlap of candidate biomarkers (d, e) Box plots showing significant differences in expression levels between AMI and control groups (f, g) AUC values for ANXA3 and SOCS3, validating their efficacy as biomarkers. (h, i) AUC values for CD34 and FLTS. (j) The expression profile of biomarkers in the external validation set, with yellow representing AMI patients and blue representing healthy controls; orange indicates up-regulated expression, blue indicates down-regulated expression, and gray indicates non-significant expression. (k) ROC analysis of biomarkers in the validation set, with specificity as the x-axis and sensitivity as the y-axis. The area under the curve represents the AUC value, indicating accuracy (predictive performance)

Fig. 4

Artificial neural network (ANN) model evaluation (a) Schematic representation of the ANN model based on ANXA3 and SOCS3. (b) Contribution of each biomarker to the predictive accuracy of the ANN model. (c) Confusion matrix illustrating the classification accuracy of the ANN model. (d) ROC curve demonstrating the overall diagnostic performance of the ANN model. (e) Correlation heatmap among biomarkers, revealing their synergistic effects in AMI diagnosis. (f) Visualization of the artificial neural network model. The node labeled “B” here is called the bias unit. The leftmost layer or layer 1 is the input layer, the middle layer or layer 2 is the hidden layer, and the rightmost layer or layer 3 is the output layer. It can be said that the above figure has 2 input units (excluding the bias unit), 2 output units, and 2 hidden units (excluding 1 bias unit). The thickness of the connecting lines represents the weights, which can be considered as the degree of contribution of that variable to the next node. The color of the lines indicates positive or negative contributions, with red representing positive contributions and gray representing negative contributions. (g) ROC analysis of the model. (h) The importance of independent variables to model prediction outcomes. (i) ROC analysis of 5-fold cross-validation

Fig. 5

Pathway enrichment analysis of biomarkers (a) Network diagram showing genes associated with biomarkers in significant pathways. (b) Pathway enrichment analysis for ANXA3 using GSEA. (c) Pathway enrichment analysis for SOCS3, illustrating differentially regulated pathways

Fig. 6

Immune infiltration in AMI (a) Overview of immune cell infiltration levels in AMI versus control samples. (b) Bar graph showing significant differences in specific immune cell types between AMI and control groups. (c) Correlation analysis among various immune cell types. (d) Correlation analysis between biomarkers and differentially infiltrated immune cell types, highlighting the immunological role in AMI. *p < 0.05, **p < 0.01 represented comparison of AMI with control group. (e) Correlation scatter plot

Fig. 7

TF-miRNA-mRNA regulatory network, network illustrating interactions among transcription factors, miRNAs, and mRNA of biomarkers. This analysis highlights the regulatory impact of key transcription factors and miRNAs on ANXA3 and SOCS3

Fig. 8

Drug target analysis and molecular docking (a) List of small molecule drugs targeting biomarkers, including their binding affinities. (b, c) Molecular docking results showing the interaction between ANXA3 and targeted drugs, with detailed binding energy value

Fig. 9

Single-cell RNA sequence analysis (a-1) Single-cell data analysis before quality control. (a-2) Single-cell data analysis after quality control. (b) Screening of high-resolution genes. (c) PCA results. (d) Elbow plot of PCA analysis. (e) UMAP clustering analysis results. (f) Expression of key genes across cell clusters. (g) Cellular annotation results. (h) Abundance of cell types across all samples

Fig. 10

Cellular communication network analysis (a) Expression of biomarkers in key cell clusters. (b) Cellular communication network analysis in Plaque Rupture(PR) and without plaque rupture (NPR). (c) Cell connection weight network in PR and NPR. (d) Probability of ligand-receptor pair-regulated communication from specific cell populations to other cell groups. *p < 0.05, **p < 0.01, ***p < 0.001 represented comparison of PR with NPR group

Fig. 11

Monocyte and B cell analysis (a) Cluster analysis of monocytes. (b) Cluster analysis of B cells. (c) Monocyte cell trajectory analysis. (d) B cell trajectory analysis. (e) Expression of biomarkers at different stages of monocyte differentiation. (f) Expression of biomarkers at various stages of B cell differentiation

Fig. 12

Validation of biomarker expression (a, b) RT-qPCR results validating the differential expression of ANXA3 and SOCS3 in AMI versus control samples, with statistical significance. *p < 0.05 represented comparison of AMI with control group