Prediction of diagnostic gene biomarkers for hypertrophic cardiomyopathy by integrated machine learning

Figures

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  Figure 1.. Identification of HCM-related DEGs in GSE36961. (a) Volcano plot of the distributions of all DEGs in HCM compared with healthy controls: 47 upregulated genes (red dots) and 109 downregulated genes (green dots). |Log2FC| >1 and adjusted P < 0.05 were the cut-off criteria. (b) Heat map of top 100 DEGs in HCM compared with healthy controls. Con, healthy control; HCM, hypertrophic cardiomyopathy; DEG, differentially expressed gene; FC, fold change; Sig, significance; Up, upregulation; Not, non-significant difference; Down, downregulation.

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  Figure 2.. Enrichment analyses for DEGs. (a–b) Partial visualization of GO and KEGG pathway enrichment analyses for DEGs, respectively. (c–d) Visualization of integrative enrichment analyses for DEGs via Metascape Online; bar graph and network show distributions and relationships of the different functions DEG, differentially expressed gene; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes.

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  Figure 3.. PPI sub-network and MCODE showing main modules in the DEG list. PPI, protein–protein interaction; MCODE, molecular complex detection; TUBA3C, tubulin alpha 3c; NFKBIA, nuclear factor kappa B inhibitor alpha; MAP3K14, mitogen-activated protein kinase kinase kinase 14; STAT3, signal transducer and activator of transcription 3; TKT, transketolase; ITGB2, integrin beta 2; TPM3, tropomyosin 3; ITGA5, integrin alpha 5; TIMP1, tissue inhibitor of metalloproteinases 1; THBS1, thrombospondin 1; F13A1, coagulation factor XIII A chain; SERPINE1, plasminogen activator inhibitor 1; SRGN, serglycin; PROS1, protein S; CFD, complement factor D; MAFB, MAF bZIP transcription factor B; CCL2, monocyte chemoattractant protein 1; FOS, Fos proto-oncogene, AP-1 transcription factor subunit.

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  Figure 4.. Screening of candidate diagnostic biomarkers for HCM. (a) Biomarker screening via LASSO model. (b) Biomarker screening via SVM-RFE algorithm. (c) Venn diagram showing five diagnostic biomarkers shared by the above two algorithms. LASSO, least absolute shrinkage and selection operator; SVM-RFE, support vector machine recursive feature elimination; RMSE, root mean square error.

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  Figure 5.. Validation of the expression patterns of five candidate diagnostic biomarkers in the GSE141910 and GSE36961 datasets. (a) RASD1 in GSE141910; (b) CDC42EP4 in GSE141910; (c) MYH6 in GSE141910; (d) FCN3 in GSE141910; (e) IRX2 in GSE141910; (f) RASD1 in GSE36961; (g) CDC42EP4 in GSE36961; (h) MYH6 in GSE36961; (i) FCN3 in GSE36961 and (j) IRX2 in GSE36961. Con, healthy control; HCM, hypertrophic cardiomyopathy; RASD1, Ras-related dexamethasone-induced 1; CDC42EP4, CDC42 effector protein 4; MYH6, myosin heavy chain 6; FCN3, ficolin 3; IRX2, Iroquois homeobox 2.

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  Figure 6.. ROC curves of the diagnostic efficacies of the four diagnostic biomarkers in the GSE36961 dataset. (a) RASD1; (b) CDC42EP4; (c) MYH6 and (d) FCN3. ROC, receiver operating characteristic; RASD1, Ras-related dexamethasone-induced 1; CDC42EP4, CDC42 effector protein 4; MYH6, myosin heavy chain 6; FCN3, ficolin 3; IRX2, Iroquois homeobox 2.

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  Figure 7.. ROC curves of the diagnostic efficacies of the four diagnostic biomarkers in the GSE141910 dataset. (a) RASD1; (b) CDC42EP4; (c) MYH6; (d) FCN3. ROC, receiver operating characteristic; RASD1, Ras-related dexamethasone-induced 1; CDC42EP4, CDC42 effector protein 4; MYH6, myosin heavy chain 6; FCN3, ficolin 3; IRX2, Iroquois homeobox 2.