Multiomics Analysis of Transcriptome, Epigenome, and Genome Uncovers Putative Mechanisms for Dilated Cardiomyopathy.

OBJECTIVE: Multiple genes have been identified to cause dilated cardiomyopathy (DCM). Nevertheless, there is still a lack of comprehensive elucidation of the molecular characteristics for DCM. Herein, we aimed to uncover putative molecular features for DCM by multiomics analysis.
METHODS: Differentially expressed genes (DEGs) were obtained from different RNA sequencing (RNA-seq) datasets of left ventricle samples from healthy donors and DCM patients. Furthermore, protein-protein interaction (PPI) analysis was then presented. Differentially methylated genes (DMGs) were identified between DCM and control samples. Following integration of DEGs and DMGs, differentially expressed and methylated genes were acquired and their biological functions were analyzed by the clusterProfiler package. Whole exome sequencing of blood samples from 69 DCM patients was constructed in our cohort, which was analyzed the maftools package. The expression of key mutated genes was verified by three independent datasets.
RESULTS: 1407 common DEGs were identified for DCM after integration of the two RNA-seq datasets. A PPI network was constructed, composed of 171 up- and 136 downregulated genes. Four hub genes were identified for DCM, including C3 (degree = 24), GNB3 (degree = 23), QSOX1 (degree = 21), and APOB (degree = 17). Moreover, 285 hyper- and 321 hypomethylated genes were screened for DCM. After integration, 20 differentially expressed and methylated genes were identified, which were associated with cell differentiation and protein digestion and absorption. Among single-nucleotide variant (SNV), C>T was the most frequent mutation classification for DCM. MUC4 was the most frequent mutation gene which occupied 71% across 69 samples, followed by PHLDA1, AHNAK2, and MAML3. These mutated genes were confirmed to be differentially expressed between DCM and control samples.
CONCLUSION: Our findings comprehensively analyzed molecular characteristics from the transcriptome, epigenome, and genome perspectives for DCM, which could provide practical implications for DCM.

1. Introduction

DCM is the most common inherited cardiomyopathies, characterized by left ventricular dilation and consecutive systolic dysfunction [1]. This disease is the third most common cause of heart failure [2]. About 70% of cases are considered idiopathic [2]. Many factors can induce the occurrence of DCM such as myocarditis, alcohol consumption, drugs, and other toxins [3]. Despite some progress in therapy and diagnosis, DCM patients' prognosis remains unsatisfactory. Given the high prevalence of DCM, understanding the potential molecular characteristics is of importance to reduce DCM-related morbidity and mortality.

Research on the genetics of DCM may provide an in-depth understanding of the pathogenesis of DCM, which assists make better clinical decisions, thereby speeding up the implementation of precision medicine [4]. In recent years, DNA methylation has been widely involved in the regulation of gene expression. Abnormal methylation is closely involved in the pathogenesis of DCM [5]. For example, nuclear DNA methylation in cardiomyocytes has a distinct relationship with left ventricular remodeling and heart failure for DCM patients [6]. Thus, genetic testing has become a promising and effective tool for screening main genetic or epigenetic changes in DCM. Genetic mutations include single nucleotide variants (SNVs), small insertion–deletion, copy number alterations, and translocations. Although it is heritable, DCM exhibits extensive genetic heterogeneity [7]. WES has become a robust diagnostic tool for DCM patients [8]. According to WES studies, a mutation (c.333+2T>C) of TNNI3K has been detected in a Chinese family with DCM [9].

The development of bioinformatics provides high-throughput data at the transcriptome, genome, and epigenome levels for DCM [10]. It is of significance to comprehensively analyze the multiomics to reveal synergistic interactions. Hence, in this study, we aimed to elucidate the molecular characteristics as therapeutic targets for DCM as well as their biological functions by multiomics analysis.

2. Materials and Methods

2.1. DCM Datasets

RNA sequencing (RNA-seq) data of left ventricle samples from 166 healthy donors and 166 DCM patients were obtained from the Gene Expression Omnibus (GEO) repository (https://www.ncbi.nlm.nih.gov/gds/; accession: GSE141910). The GSE141910 dataset was based on the GPL16791 platform. Furthermore, we also downloaded RNA-seq data of left ventricle tissues from 18 healthy donors and 15 DCM patients from the GSE126569 dataset on the GPL16791 and GPL20301 platforms. Raw data were normalized by quantile normalization using the normalizeBetweenArrays function in the limma package [11]. Cardiac DNA methylation profiles from 8 control and 9 DCM specimens were recruited from the GSE42510 dataset on the GPL8490 platform [12]. The correlations between different samples were calculated based on the gene expression and methylation profiles.

2.2. Differential Expression and Methylation Analysis

Differentially expressed (DEGs) or methylated (DEMs) genes between DCM and control left ventricle tissues were identified in line with the criteria of ∣fold change (FC) | ≥1.5 and adjusted p value < 0.05. All DEGs or DEMs were visualized into scatter plots, volcano plots, and heat maps.

2.3. Functional Enrichment Analysis

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of specified genes was carried out using the clusterProfiler package in R [13]. GO terms were composed of biological process, cellular component, and molecular function. Terms with adjusted p value < 0.05 were considered significantly enriched. The top ten terms were presented for each category.

2.4. A Protein-Protein Interaction (PPI) Network

Common DEGs were imported into the STRING database (version 11.0; https://string-db.org/) [14]. With the criteria of confidence > 0.7, the PPI network was visualized using the Cytoscape software (version 3.7.2; https://cytoscape.org/). GO and KEGG enrichment analysis was presented for genes in the PPI network.

2.5. Joint Analysis of RNA-seq and DNA Methylation

Upregulated and hypomethylated genes and downregulated and hypermethylated genes were obtained by joint analysis of DEGs and DEMs. The biological functions of differentially expressed and methylated genes were analyzed by GO and KEGG enrichment analysis.

2.6. Whole Exome Sequencing (WES)

Blood samples from 69 DCM patients were harvested from the Affiliated Hospital of Youjiang Medical University for Nationalities. The clinical features of these patients are listed in Table 1. WES was achieved by Wuhan Huada Medical Laboratory Co., Ltd. Our research was in line with the guidelines of the Declaration of Helsinki and was approved by the Ethics Committee of Affiliated Hospital of Youjiang Medical University for Nationalities (YYFY-LL-2016-01). All patients provided written informed consent. The following mutation data were filtered as follows: (1) 1000G_EAS mutation < 0.1; (2) homozygous mutation (Otherinfo = ‘hom′); and (3) the mutation type that exhibited the greatest influence on the same gene for the same specimen. The selected mutation data were saved into the Mutation Annotation Format (maf) format, which were visualized using the maftools package in R [15].

Table 1

Clinical features for 69 DCM patients.

Clinical features	Number of patients
Gender
Female	15
Male	54
Age	52.68 ± 12.46
Age of onset	48.37 ± 13.33
BMI (kg/m2)	23.02 ± 3.12
Number of onsets	3.41 ± 3.36
Hypertension
Yes	18
No	51
Drinking
Yes	35
No	34
Smoking
Yes	36
No	33
Left atrial diameter (LAD)	45.45 ± 7.97
Left ventricular internal diameter (LVIDd)	67.72 ± 9.40
Left ventricular ejection fraction (LVEF (%))	30 ± 11.45
RAS (mm)	43.84 ± 9.54
Right ventricular diameter (RVD (mm))	24.13 ± 5.41
Right ventricular outflow tract (RVOT (mm))	31.28 ± 5.96
Interventricular septal diameter (IVSd (mm))	8.75 ± 1.45
Left ventricular posterior wall diameter (LVPWd (mm))	8.80 ± 1.18

2.7. Validation of Mutant Genes in Independent Datasets

RNA-seq of blood samples from 8 DCM patients and 8 healthy participants was obtained from the GSE101585 dataset on the GPL20301platform. The expression of the top 5 genes with the highest mutation frequency according to the WES results was validated in the GSE101585, GSE141910, and GSE126569 datasets.

2.8. Statistical Analyses

Statistical analyses were carried out using R language packages (version 4.0.2; https://www.r-project.org/). Differences with p value < 0.05 were statistically significant.

3. Results

3.1. Screening DEGs for DCM

Two datasets including GSE141910 and GSE126569 were included for screening DEGs between DCM and control left ventricle specimens. In the GSE141910 dataset, there were high correlations between 166 healthy and 166 DCM specimens (Figure 1(a)). With the threshold of ∣FC | ≥1.5, 1535 genes were upregulated and 1438 were downregulated in DCM compared to control samples (Figure 1(b)). DEGs including 1535 up- and 1437 downregulated genes were screened for DCM under the criteria of ∣FC | ≥1.5 and adjusted p value < 0.05 (Figure 1(c)). Heat map depicted that these DEGs distinctly distinguished DCM samples from normal samples (Figure 1(d)). In the GSE126569 dataset, there were 18 healthy and 15 DCM left ventricle samples. The high correlation was found between these samples (Figure 2(a)). 2930 genes were upregulated, and 2380 genes were downregulated in DCM than in control specimens (Figure 2(b)). As shown in Figure 2(c), we identified DEGs between the two groups, composed of 2468 up- and 2062 downregulated genes. The differences in expression patterns of these DEGs are depicted in Figure 2(d).

Figure 1

Differentially expressed genes between DCM and control left ventricle specimens in the GSE141910 dataset. (a) Heat map depicting the correlation between DCM and control left ventricle samples. The closer to yellow, the higher the correlation coefficient. (b) Scatter plots for up- and downregulated genes with ∣FC | ≥1.5 between DCM and control left ventricle specimens. (c) Volcano plots for DEGs with ∣FC | ≥1.5 and adjusted p value < 0.05 between DCM and control left ventricle specimens. (d) Hierarchical clustering heat map for DEGs between DCM and control groups. Red: upregulated genes; blue: downregulated genes; grey: no differentially expressed genes.

Figure 2

Differentially expressed genes between DCM and control left ventricle specimens in the GSE126569 dataset. (a) Heat map for the correlation between DCM and control left ventricle samples. The closer to yellow, the higher the correlation coefficient. (b) Scatter plots showing up- and downregulated genes with ∣FC | ≥1.5 between DCM and control groups. (c) Volcano plots for DEGs with ∣FC | ≥1.5 and adjusted p value < 0.05 between DCM and control groups. (d) Heat map for DEGs between DCM and control groups. Red: upregulated genes; blue: downregulated genes; grey: no differentially expressed genes.

3.2. Potential Biological Functions of Common DEGs

After overlapping the DEGs in the GSE141910 and GSE126569 datasets, 1407 common DEGs were identified for DCM. Potential biological functions of up- and downregulated genes were separately analyzed. Our GO enrichment analysis results showed that upregulated genes were significantly enriched in extracellular matrix organization (Figure 3(a)), collagen trimer (Figure 3(b)), and extracellular matrix structural constituent (Figure 3(c)). Meanwhile, downregulated genes were distinctly enriched in secretion (Figure 3(d)), integral and intrinsic component of plasma membrane (Figure 3(e)), and organic anion transmembrane transporter activity (Figure 3(f)). KEGG enrichment analysis results demonstrated that upregulated genes were significantly associated with Th17 cell differentiation, protein digestion and absorption, Th1 and Th2 cell differentiation, Hippo signaling pathway, cytokine and cytokine receptor, and cell adhesion molecules (Figure 3(g)). In Figure 3(h), downregulated genes were significantly enriched in complement and coagulation cascades as well as phagosome.

Figure 3

GO and KEGG enrichment analysis for common DEGs in the GSE141910 and GSE126569 datasets. The top ten GO enrichment analysis results for upregulated genes including biological process (a), cellular component (b), and molecular function (c). The top ten GO-biological process (d), cellular component (e), and molecular function (f) results for downregulated genes. (g, h) The top ten KEGG pathway enrichment analysis results for up- and downregulated genes, respectively.

3.3. A PPI Network Based on Common DEGs

We further analyzed the interactions between common DEGs by the STRING database. The interactions were visualized into a PPI network via the Cytoscape software. As a result, there were 307 nodes in the PPI network, composed of 171 up- and 136 downregulated genes (Figure 4(a)). The top four genes with the highest degree were selected as hub genes, including C3 (degree = 24), GNB3 (degree = 23), QSOX1 (degree = 21), and APOB (degree = 17). The expression of C3, QSOX1, and APOB was significantly upregulated, and GNB3 was distinctly downregulated in DCM compared to controls (Figure 4(a)). GO enrichment analysis results indicated that the genes in the PPI network were distinctly enriched in response to stimulus (Figure 4(b)), cell periphery (Figure 4(c)), extracellular region (Figure 4(c)), and protein binding (Figure 4(d)). KEGG pathway enrichment analysis revealed that these genes had significant associations with pathways in cancer, Hippo, JAK-STAT, and PI3K-Akt signaling pathways (Figure 4(e)).

Figure 4

Construction of a PPI network for common DEGs and function annotation analysis. (a) A PPI network was established based on common DEGs. Red represents upregulation and green represents down-regulation. Nodes are proportional to the size of the circle. The top ten GO enrichment analysis results for genes in the network including biological process (b), cellular component (c), and molecular function (d). (e) The top ten KEGG pathway enrichment analysis results for genes in the network.

3.4. Identification of DMGs for DCM

We further analyzed the DNA methylation for DCM using the GSE42510 dataset. There were distinct correlations between 8 control and 9 DCM specimens (Figure 5(a)). With the threshold of ∣FC | ≥1.5, 1122 hypermethylated and 1314 hypomethylated genes were screened between DCM and control samples (Figure 5(b)). Differentially methylated genes with fold-change ≥ 1.5 and adjusted p value < 0.05 were identified for DCM compared to controls, including 285 hypermethylated and 321 hypomethylated genes (Figure 5(c)). The distinct differences in their methylation levels were found between DCM and control groups (Figure 5(d)).

Figure 5

Differentially methylated genes between DCM and control left ventricle tissues in the GSE42510 dataset. (a) Heat map showing the correlation between DCM and control samples. The closer to yellow, the higher the correlation coefficient. (b) Scatter plots depicting hyper- and hypomethylated genes with ∣FC | ≥1.5 between DCM and control groups. (c) Volcano plots for differentially methylated genes with ∣FC | ≥1.5 and adjusted p value < 0.05 between DCM and control groups. (d) Heat map for differentially methylated genes between the two groups. Red: hypermethylated genes; blue: hypomethylated genes; grey: no differentially methylated genes.

3.5. Exploring Underlying Biological Functions of DMGs

We analyzed which biological processes the DEMs were mainly involved in. GO enrichment analysis results showed that hypermethylated genes were significantly enriched in response to endogenous stimulus (Figure 6(a)), intracellular membrane-bounded organelle (Figure 6(b)), and enzyme binding (Figure 6(c)). Hypomethylated genes were distinctly associated with animal organ morphogenesis (Figure 6(d)), chordate embryonic development (Figure 6(d)), cell junction (Figure 6(e)), transcription regulatory region DNA binding (Figure 6(f)), and regulatory region nucleic acid binding (Figure 6(f)). As shown in KEGG pathway enrichment analysis, hypermethylated genes were mainly enriched in pathways in cancer, platinum drug resistance, and PI3K-Akt signaling pathway (Figure 6(g)). In Figure 6(h), hypomethylated genes were primarily enriched in calcium signaling pathway, adherens junction, and glutamatergic synapse.

Figure 6

GO and KEGG enrichment analysis of differentially methylated genes. The top ten GO enrichment analysis results for hypermethylated genes including biological process (a), cellular component (b), and molecular function (c). The top ten GO-biological process (d), cellular component (e), and molecular function (f) results for hypomethylated genes. (g, h) The top ten KEGG pathway enrichment analysis results for hyper- and hypomethylated genes.

3.6. Differentially Expressed and Methylated Genes for DCM

Conjoint analysis of DEGs and DEMs was further presented for DCM. As shown in Figure 7(a), 20 differentially expressed and methylated genes were identified between DCM and control left ventricle samples. GO enrichment analysis results showed that these differentially expressed and methylated genes were distinctly correlated to positive regulation of cell differentiation (Figure 7(b)), synapse (Figure 7(c)), and growth factor binding (Figure 7(d)). KEGG pathway enrichment analysis results indicated that these genes were significantly enriched in protein digestion and absorption (Figures 7(e) and 7(f)) and amoebiasis (Figures 7(e) and 7(g)).

Figure 7

Conjoint analysis of RNA-seq and DNA methylation for DCM. (a) Venn diagram depicting the 20 differentially expressed and methylated genes between DCM and control left ventricle samples. The top ten GO-biological process (b), cellular component (c), and molecular function (d) results for differentially expressed and methylated genes. (e) KEGG enrichment analysis including (f) protein digestion and absorption and (g) amoebiasis.

3.7. Single-Nucleotide Polymorphisms (SNP) in DCM

69 DCM blood samples were analyzed using WES. Missense mutation and nonsense mutation were the top two variant classification (Figure 8(a)). Table 2 lists the mutation frequency about different mutation types among these DCM samples. SNP was the most common variant type. C>T was the most common single-nucleotide variant (SNV) classification, followed by T>C. The median variants per sample were 65. The five mutated genes were as follows: AHNAK2, MUC4, PHLDA1, MAML3, and OR2T35. Figure 8(b) visualizes the top 100 genes with the highest mutation frequency, such as PHLDA1 and MUC4. In Figure 8(c), MUC4 (mainly missense mutation) and PHLDA1 (mainly in frame deletion) occupied the highest frequency mutation among 69 DCM samples (71%). We further explored the correlations in cooccurrence between different mutated genes. As shown in Figure 8(d), among 69 DCM blood samples, IRS4 mutation was positively correlated with COL4A5 mutation. MUC4 mutation had a significant correlation with PCDH11X mutation. PCDH11X mutation exhibited a distinct association with CYLC1 mutation.

Figure 8

Landscape of mutation for DCM. (a) Mutation classification, type, SNV class, variants per sample, variant classification summary, and the top ten mutated genes for DCM. (b) Map of the top 100 genes with mutation frequencies. The larger the font, the higher the mutation frequency. (c) Waterfall diagram depicting the mutations of the top 30 genes in each sample. (d) Correlation analysis among the top 30 genes in mutation frequency.

Table 2

WES results for 69 DCM blood samples.

ID	Summary	Mean	Median
Frame shift deletion	42	0.609	0
Frame shift insertion	14	0.203	0
In frame deletion	90	1.304	1
In frame insertion	16	0.232	0
Missense mutation	2426	35.159	34
Nonsense mutation	1861	26.971	26
Nonstop mutation	33	0.478	0
Splice site	68	0.986	1
Translation start site	1	0.014	0
Total	4551	65.957	65

3.8. Validation of the Expression of Mutant Genes in the Blood and Left Ventricle of DCM

The expression of mutant genes was detected and validated in blood and left ventricle samples of DCM patients and controls. In the GSE101585 dataset, there were distinct differences between DCM and control blood samples based on the gene expression profiles after preprocessing (Figure 9(a)). Heat map visualized the correlations between different samples at the mRNA expression levels (Figure 9(b)). In Figure 9(c), we found that there were 2517 up- and 3987 downregulated genes with ∣FC | >2 in DCM compared to control groups. With the threshold of ∣FC | >2 and adjusted p value < 0.05, 146 up- and 675 downregulated genes were identified for DCM (Figure 9(d)). There were distinct differences in the expression of these DEGs between DCM and control groups (Figure 9(e)). The mutant genes including AHNAK2, MAML3, MUC4, OR2T35, and PHLDA1 were differentially expressed in DCM compared to control blood samples (Figure 9(f)). In the GSE141910 dataset, AHNAK2 (p value = 3.7e − 15) was lowly expressed, and MAML3 (p value < 2.22e − 16) and PHLDA1 (p value = 0.0068) were highly expressed in DCM than in control left ventricle tissues (Figure 9(g)). Consistently, in the GSE126569 dataset, PHLDA1 (p value = 2.5e − 07) and MAML3 (p value = 0.0045) exhibited higher expression levels and AHNAK2 (p value = 0.0001) showed lower expression levels in DCM compared to control left ventricle samples (Figure 9(h)). No significant difference in MUC4 was found between the two groups.

Figure 9

Validation of the expression of mutant genes in blood and left ventricle of DCM. (a) Principal component analysis for 8 DCM and control blood samples from the GSE101585 dataset. Blue: DCM samples and green: healthy samples. (b) Heat map visualizing the correlation between DCM and control blood groups. The intensity of the color is proportional to the correlation coefficient. (c) Scatter plots showing up- and downregulated genes between DCM and control blood groups. (d) Volcano plots depicting all DEGs between the two groups. (e) Heat map visualizing the expression patterns of DEGs between the two groups. Red: upregulation; blue: downregulation. (f) Differences in expression of AHNAK2, MAML3, MUC4, OR2T35, and PHLDA1 between DCM and control blood samples in the GSE101585 dataset. (g) Abnormal expression of AHNAK2, MAML3, and PHLDA1 between DCM and control left ventricle samples in the GSE141910 dataset. (h) Dysregulated expression of AHNAK2, MAML3, MUC4, and PHLDA1 between DCM and control left ventricle samples in the GSE126569 dataset.

4. Discussion

Without the effective treatment strategies, DCM is the major cause of heart failure [16]. Diverse genetic and environment factors to the myocardium contribute to the occurrence of DCM [17]. From the transcriptome, genome, and epigenome perspectives, our study comprehensively analyzed molecular characteristics for DCM, which could deepen the understanding of the pathogenesis for DCM and assist clinicians to specify more reasonable clinical decisions.

Due to the wide heterogeneity of the population, we integrated the two datasets to obtain 1407 common DEGs between DCM and control left ventricle samples. Functional enrichment analysis was utilized to probe into the biological functions of up- and downregulated genes. Upregulated genes were distinctly associated with extracellular matrix and collagen trimer. It has been well acknowledged that myocardial fibrosis is the main feature of DCM, involving changes in the extracellular matrix [18]. A retrospective study has found fibrosis of extracellular matrix is associated with the duration of DCM [19]. Cardiac fibrosis has a significant association with nonischemic DCM, thereby increasing its morbidity as well as mortality [20]. It has been found that changes in various genes may mediate pathological cardiac fibrosis, such as WWP2 [21]. Collagen-derived peptides have been considered circulating biomarkers for DCM, which could be mediated by different genes such as Galectin-3 [22]. Thus, these upregulated genes could be involved in regulating extracellular matrix and collagen formation, which should be further explored. Furthermore, we found that these upregulated genes were significantly enriched in immune-related pathways like Th17 cell differentiation, Th1 and Th2 cell differentiation, and cytokine and cytokine receptor, indicating that these genes could be involved in regulating immune response during the progression of DCM. As a recent study [23], Th1 and Th17 have been proposed as targets for the treatment of inflammatory DCM that is the main cause of heart failure among children as well as young adults [24]. Downregulated genes were significantly enriched in complement and coagulation cascades and phagosome, which were consistent with a previous study [25]. Based on the DEGs, we established a PPI network, composed of 171 up- and 136 downregulated genes. Among them, four hub genes were identified for DCM, including C3, GNB3, QSOX1, and APOB. Previously, C3 and APOB have been proposed as markers for stress cardiomyopathy [26]. QSOX1 exhibits high expression in myocardium tissues following acute heart failure [27]. Thus, these hub genes play a vital role in the biological processes, which may affect the expression of other genes in the PPI network.

DNA methylation is an important mechanism of epigenetic regulation [28]. Thus, identification of abnormal methylation is of clinical significance for DCM. Herein, 285 hyper- and 321 hypomethylated genes were screened for DCM. Hypermethylated genes were mainly enriched in PI3K-Akt pathway, while hypomethylated genes were primarily enriched in the calcium pathway. It has been confirmed that the PI3K-Akt pathway is in association with DCM development, which is activated or inactivated by different genes like PTEN [29]. There is an elevated myocyte calcium sensitivity for pediatric DCM in the late stage [30]. Imbalance of calcium homeostasis is closely related to DCM as well as heart failure [31]. DNA methylation may regulate gene expression. Herein, 20 differentially expressed and methylated genes were identified following integration of DEGs and DMGs. These genes had significant correlations with cell differentiation and protein digestion and absorption. More studies should be presented in further studies.

Genetic inheritance occurs in 30%-48% of patients [32]. We presented WES for 69 DCM patients in our cohort. Our results showed that MUC4 was the most frequent mutation gene which occupied 71% across 69 samples, followed by PHLDA1, AHNAK2, and MAML3. In the three independent datasets, we confirmed that PHLDA1 and MAML3 were highly expressed and AHNAK2 was lowly expressed in blood and left ventricle samples from DCM compared to control, indicating that the genetic mutation could lead to their abnormal expression. However, their expression and functions remain unclear in DCM.

5. Conclusion

Taken together, this study roundly expounded the molecular features and relevant biological functions for DCM from the transcriptome, genome, and epigenome perspectives, which may deepen the understanding for the pathogenesis of DCM. The key genes identified from different omics such as PHLDA1, MAML3, and AHNAK2 as potential therapeutic targets toward DCM will be further validated in our future studies.