Fig 1.
(A) Unsupervised principal component analysis (PCA) clearly distinguishing DCM patients from normal controls. The blue spheres refer to DCM patients, and red normal controls. (B) Heatmap of genes that were significantly modulated in DCM. Hierarchical clustering distinguished individuals as either DCM or normal controls. Red and green denote highly and weakly expressed genes, respectively.
Fig 2.
(A) Venn diagram representing the overlapping between the differentially expressed proteins and genes. Sixteen proteins/genes were commonly identified in both global gene and protein profiling. (B) Unsupervised principal component analysis (PCA) of the commonly dysregulated proteins/genes separating samples into DCM patients and normal controls. The blue spheres refer to DCM patients and red normal controls. (C-D) Unsupervised two-dimensional hierarchical clustering of proteomic and transcriptomic datasets using commonly dysregulated proteins/genes (C and D, respectively). The hierarchical clustering revealed two main clusters, one composed of DCM patients and another composed of normal controls. Highly expressed genes are indicated in red, intermediate in black, and weakly expressed in green.
Table 1.
The 16 overlapping differential expressed genes/proteins in transcriptomic and proteomic data in the study.
Fig 3.
Functional interaction network of 16 genes.
Genes were clustered according to their associated pathways, which are shaded with a different color. Green nodes indicate down-regulated, red, up-regulated, and linker genes (non-colored nodes). The edges represent interactions between genes, with arrows indicating directed interactions and dotted lines indicating predicted relationships.
Fig 4.
Gene interaction network analyses of 16 commonly dysregulated proteins/genes based on the Ingenuity knowledge base.
Green indicates down-regulated, and red, up-regulated. The color intensity is correlated with fold change. Straight and dashed lines represent direct or indirect gene to gene interactions, respectively.
Fig 5.
Validation analyses using independently performed microarray and RNAseq datasets.
(A) The PCA and (B) unsupervised hierarchical clustering using our 16 gene set discriminated individuals as DCM and controls in Barth et al.’s [16] microarray data. Samples are in the columns and genes are in the rows (gene symbols are listed on the right). The expression level of each gene across samples is scaled to [−3, 3] interval. The expression levels are depicted using a color scale as shown at the top of the figure. (C) PCA analysis using RNA-Seq dataset for (non-ischemic cardiomyopathy (NICM) (n = 8) and normal controls (n = 8) from Yang et al [26]. (D) Venn diagram representing the genes common to DEGs in our DCM patients (Colak et al[15]) with DEGs in validation datasets from datasets from Yang et al [26] and Liu et al [23] for RNA-Seq data for independent samples from human failing heart.
Fig 6.
GO Biological Process and pathway analyses of differentially expressed genes (DEGs) and proteins (DEPs) using the PANTHER classification system.
(A-B) Pie charts displaying significantly enriched biological processes respectively, and (C-D) signaling pathways associated with DEGs and DEPs.
Fig 7.
Venn diagrams representing overlap of (A) predicted upstream regulators, (B) enriched GO biological processes, and (C) KEGG pathways between differentially expressed genes and proteins.
Table 2.
Overlapping KEGG pathways for mRNA and proteomics in in dilated cardiomyopathy.