Identification and validation of MYC as an autophagy-related gene for diagnosis and immune infiltration in sepsis-induced myocardial dysfunction

Figures

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  Fig. 1. Identification of co-expression network of SIMD-associated genes. A. The result of soft threshold β in WGCNA. B. The result of gene module clustering, with a hierarchical clustering dendrogram of genes in the upper half and gene modules in the lower half. C. Eigengene adjacency heatmap. D. The correlation between gene modules and SIMD or control samples

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  Fig. 2. Identification of candidate genes correlated with autophagy and analysis of their function information in SIMD. A-B. The differentially expressed genes (DEGs) between SIMD and control samples in the GSE79962 dataset. C. The overlapping genes among 3658 SIMD-related genes, 1031 DEGs, and 222 ARGs. D-E. The top 10 KEGG and GO (BP, CC, and MF) enrichment pathways/terms of 12 genes

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  Fig. 3. MYC was selected target ARG in SIMD. A. A PPI network. B. Network diagram of the MCODE algorithm. C-E. The expression of MYC in SIMD and control samples in the GSE79962, GSE44363, and GSE40180 datasets. F-H. The receiver operating characteristic (ROC) in the GSE79962, GSE44363, and GSE40180 datasets

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  Fig. 4. Identification of the potential drugs. A. The expression of MYC was significantly negatively correlated with IC50 of drugs. B. The expression of MYC was significantly positively correlated with IC50 of drugs. C. MYC-drugs interaction network

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  Fig. 5. Identification of transcription factors (TFs) associated with MYC expression and prediction of transcription factor binding sites. A. The correlation between these 47 TFs and MYC. B. The correlation between MYC and EST2. C. A TFs-MYC interaction network. D. Chip-seq database results for ETS2

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  Fig. 6. Construction of the TF-miRNA-mRNA network. A. The target miRNAs of MYC in the starBase database. B. The target miRNAs of MYC in the miRWalk database. C. Seven target miRNAs of MYC coexisted in the starBase and miRWalk databases. D. TF-miRNA-mRNA co-regulatory network

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  Fig. 7. Functional enrichment analysis. A. The top 10 significantly activated and inhibited signaling pathways in the MYChigh group (GSEV). B. The TNF, P53, MAPK, IL-17 and ECM-receptor interaction signaling pathways in the MYChigh group. C. The result of GSVA

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  Fig. 8. MYC expression was correlated with immune cell infiltration in SIMD samples. A. The relative proportions of 22 different types of immune cells in SIMD and control samples in the GSE79962 dataset were calculated by CIBERSORT. B. The difference of 22 different types of immune cell infiltration between SIMD and control samples. C. The correlation between MYC expression and NK cell resting. D. The correlation between MYC expression and immune cell infiltration in the Xcell. E. The correlation between MYC expression and immune cell infiltration in the TISIDB database

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  Fig. 9. Myc knockdown attenuates LPS-induced autophagy flux and restores cell viability in the SIMD cellular model. A. Effects of varying concentrations of LPS (0, 0.1, 1, 10, and 100 µg/mL) on H9C2 cell viability were assessed by CCK-8 assay. B-C. Validation of Myc knockdown efficiency in H9C2 cells transfected with Si-Myc-1, Si-Myc-2, or Si-Myc-3 by RT-qPCR (B) and Western blot analysis (C). D. Cell viability was measured by CCK-8 assay in H9C2 cells subjected to Myc knockdown followed by LPS stimulation (10 µg/mL, 24 h). E. Representative Western blots and quantitative analysis of Myc, LC3-II/I ratio, and p62 protein levels in the indicated groups. GAPDH served as the loading control. Data are presented as mean ± SD (n = 3). *p < 0.5, **p < 0.01, ***p < 0.001