Weighted gene co-expression identification of CDKN1A as a hub inflammation gene following cardiopulmonary bypass in children with congenital heart disease

Figures

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  Figure 1. The workflow of data preparation, processing, analysis and validation.

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  Figure 2. Sample clustering to detect outliers. GSM3852140 is considered a significant outlier and the data from this sample is excluded under the condition that abline equals 100.

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  Figure 3. Gene dendrogram and module colors. After calculating eigengene, hierarchical clustering of modules, and merging the more similar modules, only 19 modules remain.

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  Figure 4. Diagram of Module-trait relationships. As shown in the figure, the brown module has the highest correlation with CPB_after(P = 0.75 × 104e−8).

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  Figure 5. Protein protein Interaction Network Diagram. After removed disconnected nodes in the network, only 18 genes remained.They were MCL1, JUNB, ZFP36, CDKN1A, NR4A1, MYC, IRF1, ATF3, MAFF, EGR3, SOCS3, GADD45B, DUSP5, CXCL8, SLC2A14,SLC2A3, NAMPT and HBEGF.

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  Figure 6. Flower plot for cytohubb results. Each petal represents one of cytohubb's algorithms, and only CDKN1A generemains after taking the intersection.

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  Figure 7. Enrichment of genes matching membership term: inflammation. The outer pie shows the number and the percentage of genes in the background that are associated with the inflammation (in black); the inner pie shows the number and the percentage of genes in the brown module gene list that are associated with the inflammation. The P-value indicates whether the membership is statistically significantly enriched in the brown module gene list.

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  Figure 8. Bar graph of the top 20 GO and KEGG pathways (http://www.genome.jp/kegg/) across input brown module genes, colored by p-values.

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  Figure 9. Disease enrichment plot. (A) GWAS_Catalog_2019, (B) PhenGenI_Association_2021, (C) PheWeb_2019, (D) Rare_Diseases_AutoRIF_ARCHS4_Predictions, (E) Rare_Diseases_GeneRIF_Gene_Lists. Ranked by P-values. The genes in the brown module that were significantly enriched in inflammatory diseases, including inflammatory bowel disease (A,B); infection/inflammation of internal prosthetic device, implant and graft (C); post-infectous myocarditis (D); inflammatory breast cancer (E).

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  Figure 10. Immune infiltration analysis plot. There was a significant change in macrophages M2 (P < 0.01), mast cells resting (P < 0.001), NK cells activated (P < 0.05), T cells follicular helper (P < 0.001), mast cells activated (P < 0.01) and eosinophils (P < 0.001) in the post-CPB group compared to the pre-CPB group. “*”, “**”, “***” represent P < 0.05, P < 0.01, P < 0.001, respectively.

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  Figure 11. Box plots of CDKN1A gene expression in the validation set. The expression of CDKN1A in the GSE12486 validation dataset was significantly lower in the pre-CPB group than in the post-CPB group (Wilcoxon, P = 0.0079; T-test, P = 0.006).

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  Figure 12. Box plots of CDKN1A gene expression in the GSE 132176 dataset. The expression of CDKN1A was significantly lower in the pre-CPB group than in the post-CPB group (Wilcoxon, P = 9.9 × 10−9; T-test, P = 8.5 × 10−9).