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Identification of differentially expressed long non-coding RNAs associated with dilated cardiomyopathy using integrated bioinformatics approaches

long Non-coding RNAs, biomarker, dilated cardiomyopathy, bioinformatics analysis The aim of this study was to identify novel long non-coding RNA (lncRNA) biomarkers associated with dilated cardiomyopathy (DCM) and reveal the potential molecular mechanisms of DCM development using bioinformatics approaches. The array data of GSE5406, including 108 DCM samples and 16 non-failing control samples, were obtained from the Gene Expression Omnibus database. The differentially expressed lncRNAs were identified using limma package in R. Pearson's correlation analyses were performed between the differentially expressed lncRNAs and protein-coding genes based on their expression levels. Pathway enrichment of these lncRNAs was conducted based on the significantly co-expressed genes. From the receiver operating characteristic (ROC) curve, the area under the ROC curve (AUC) value was obtained and used for evaluating discriminatory ability. IDI2-AS1 and XIST were differentially expressed in DCM patients. A total of 510 co-expressed genes were identified. The enriched functions and pathways of the co-expressed genes mainly included NADH dehydrogenase activity, cardiac muscle contraction, and oxidative phosphorylation. The ROC curve analysis indicated that the two lncRNAs have favorable diagnostic values in DCM. The AUC values of XIST, IDI2-AS1, and the combination of XIST and IDI2-AS1 were 0.733 (95% CI: 0.646-0.809), 0.796 (95% CI: 0.715-0.863), and 0.823 (95% CI: 0.745-0.886), respectively. This study identified IDI2-AS1 and XIST lncRNAs and related pathways involved in the pathogenesis of DCM, thus providing potential diagnostic and therapeutic targets for DCM.


Introduction
Dilated cardiomyopathy (DCM) is the most common type of cardiomyopathy worldwide, characterized by the primary presence of left ventricular (LV) dilation and reduced systolic function in the absence of abnormal loading conditions or coronary artery disease (1,2). DCM may result in progressive endstage heart failure and sudden cardiac arrest, which is closely associated with excessive morbidity and premature mortality (3). Despite the rapid advancement in therapeutic modalities, the 5-year mortality for DCM remains up to 20%, and most of them die from heart failure and ventricular arrhythmias (4). Moreover, patients with DCM are usually asymptomatic at the early stage, and the period of diagnosis and treatment may be considerably delayed (5). In the majority of cases of idiopathic DCM, the etiology remains still unknown.
Recently, an increasing number of studies have confirmed that lncRNAs play important roles in the heart, including DCM-induced chronic heart failure initiation and progression at the posttranscriptional level (6). LncRNAs represent a cluster of RNAs that are >200 nucleotides in length without protein coding potential (7). LncRNAs can perform multiple biological functions, such as RNA processing, regulation of chromatin remodeling, marker of cell fate, and as a competing endogenous RNA (8)(9)(10)(11). Previous studies have reported that lncRNAs play critical roles in the progression as well as diagnostic and therapeutic targets of cardiovascular diseases (12,13). Nevertheless, further studies required to explore the potential functions of lncRNAs in the development of DCM. In this study, we aimed to explore more lncRNAs involved in DCM. We screened differentially expressed lncRNAs based on GSE5406, followed by a series of bioinformatics analyses to identify key lncRNAs involved in DCM.

Microarray gene expression
GSE5406 gene expression profile was obtained from Gene Expression Omnibus (http://www.ncbi.nlm. nih.gov/geo) database, based on the platform of the PL96 Affymetrix Human Genome U133A expression beadchip. The dataset was an mRNA expression profile chip, which contained 16 non-failing control samples and 108 samples from DCM patients. The platform contains both mRNAs and lncRNAs; therefore, we obtained the lncRNA expression profile by repurposing the probes in the mRNA expression profiles to lncRNAs according to the annotation in the GENCODE version GRCh38 (http://www. gencodegenes.org). The probe without a gene symbol was removed. For different multiple probe sets mapping to the same mRNA or lncRNA, the mean value of different probes was selected as the final expression value.

Differentially expressed lncRNA identification
The LIMMA package (Linear Models for Microarray Data) in R software (version 3.5.1) with multiple testing corrections based on the Benjamini & Hochberg method was used to screen out the differentially expressed lncRNAs between DCM and control groups (14). Adjusted false discovery rate p values < 0.05, along with fold change ≥ 1.5, were used as the thresholds for difference analysis. Then, cluster analysis was carried out using the Pheatmap package in R to further analyze the differentially expressed lncRNAs.

LncRNA-related protein-coding gene identification and enrichment analysis
The Pearson's correlation coefficients between the differentially expressed lncRNAs and the protein-coding genes were calculated to determine co-expression relationships (15). The protein-coding genes positively or negatively correlated with the differentially expressed lncRNAs were considered as lncRNA-related proteincoding genes (|Pearson's correlation coefficient| > 0.6 and p-value < 0.001). Gene Ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were also conducted using the biological tool. The clusterProfiler package was used to explore the biological meaning and pathways behind numerous genes (16).

Diagnostic value of differentially expressed lncRNAs in DCM
To test the predictive value of the differentially expressed lncRNAs, we generated a receiver operating characteristic (ROC) curve using the lncRNA expression data from 108 DCM patients and 16 controls. The area under the ROC curve (AUC) value was utilized to determine the diagnostic effectiveness in discriminating patients with DCM patients from normal individuals.

Identification of differentially expressed lncRNAs
According to the selection criteria, a total of 107 lncRNAs were obtained, and two differentially expressed lncRNAs were identified between the DCM and control groups, including upregulated IDI2-AS1 and downregulated XIST. As shown in the heat map (Figure 1), the differentially expressed lncRNAs were predominantly separated into two clusters.

Functional enrichment analysis of pathways correlated with the identified lncRNAs in DCM
We examined the expression correlation between identified lncRNAs and genes by calculating the Pearson's correlation coefficient through expression profiles in 108 patients with DCM. Co-expression between 510 genes and the two lncRNAs (Pearson's correlation coefficient > 0.6 and p-value < 0.001) was found. The highest Pearson's correlation coefficient of determination between two lncRNAs and mRNA matrix is shown in Figure 2. We performed GO and KEGG enrichment analyses to uncover specific functional categories of the co-expressed genes. As a result, 510 coexpressed genes clustered most significantly in NADH dehydrogenase activity, cytochrome-c oxidase activity, cofactor binding, and oxidoreductase activity ( Figure  3A). KEGG pathway enrichment showed that genes were mainly involved in oxidative phosphorylation and cardiac muscle contraction ( Figure 3B).

Discussion
DCM is the third most common cause of congestive heart failure and sudden cardiac arrest (17). Previous studies have revealed that lncRNAs play important roles in cardiac development and participate in cardiac homeostasis and regeneration as epigenetic regulators of heart gene expression (18,19). Recently, increasing attention has been paid to improving the treatment of DCM. However, there remains a long road to better understand of the underlying mechanism. In this study, dysregulated lncRNAs between patients with DCM and non-failing controls were analyzed by RNA sequencing. In total, a differentially expressed lncRNA steadily upregulated and another steadily downregulated. In particular, based on the lncRNA-related protein-coding genes, the following functional enrichment analysis indicated that lncRNA-related co-expressed mRNAs were mainly enriched in NADH activity, cytochrome c oxidase activity, and oxidoreductase activity. KEGG pathway enrichment showed that these genes were mainly involved in oxidative phosphorylation and cardiac muscle contraction.
In recent years, several studies have explored the functions and clinical significance of lncRNAs in HCM (20,21). A recent study investigated 14 lncRNAs deregulated in patients with non-end-stage dilated hypokinetic ischemic cardiomyopathy, such as CDKN2B-AS1, EGOT, and H19 (22). A wholetranscriptome analysis of heart biopsy specimens from patients with DCM and healthy heart donors confirmed that myocardial infarction associated transcript was highly expressed in patients with DCM (23). To date, many lncRNAs have been revealed to be associated with myocardial pathophysiology, and research on the biological function and mechanism of lncRNAs has only begun and remained unclear (24,25). LncRNA X inactive-specific transcript (XIST) is indispensable for transcriptional silencing of the X chromosome in female mammals, which play vital role in the inactivation of the X chromosome (26). It has been widely used as an oncogene or a tumor suppressor in many malignancies (27). XIST has also been demonstrated to be remarkably upregulated in the hypertrophic hearts of mice induced by transaortic constriction and phenylephrine-treated rat cardiomyocytes (28). Furthermore, it was shown that XIST could inhibit myocardial cell proliferation and promote apoptosis. It was also reported that XIST contributed to tumor proliferation and suppressed tumor apoptosis in lung cancer and hepatocellular carcinoma (29,30). All these findings help to explain the potential contribution of XIST to DCM. As for isopentenyldiphosphate delta isomerase 2 antisense RNA 1 (IDI2-AS1), its function in DCM has not been reported in the literature. Overexpression of IDI2-AS1 sensitized human cells to cell death in response to various stresses, such as ultraviolet irradiation, cycloheximide, hydrogen peroxide, and mercury II chloride (31). Therefore, its function should be explored in more studies.
Systolic dysfunction is the first hallmark pathophysiologic sign of DCM (32). In the normal heart, contraction is synchronous within the myocardium, with a normal symmetric distribution of negative strain across the wall. Conversely, in the heart with DCM, the contraction pattern is obviously dyssynchronous (33). Therefore, heart contraction may be a vital indicator of DCM. In heart failure, deterioration of cytosolic Ca 2+ and Na + handling hinders mitochondrial Ca 2+ uptake and the subsequent Krebs cycle-induced regeneration of the decreased forms of NADH and NADPH, leading to energetic deficit and oxidative stress (34). These results were consistent with the enrichment analysis that lncRNAs were mainly enriched in NADH and oxidoreductase activity and cardiac muscle contraction. This finding was confirmed by the fact that reduced sarcomere contractility can increase ventricular volumes to maintain cardiac output through the Frank-Starling mechanism, generating a thin-walled LV appearance that is discovered in DCM (35). DCM is usually diagnosed at ages between 30 and 40 years of age. However, owing to the vague symptoms and delay in diagnosis, the outcome for DCM is poor (36). DCM accounts for about one-third of heart failure patients. A recent study reported that lncRNAs ENST00000532365 and ENST00000507296 could serve as biomarkers for the diagnosis of DCM-related heart failure (36). In the present study, we found that XIST presented an AUC of 0.733, and IDI2-AS1 exhibited an AUC of 0.796. Moreover, when the two lncRNAs were combined, the ROC curve yielded an AUC of 0.823 with a sensitivity of 86.11% and specificity of 75.00%. Therefore, based on these results, XIST combined with IDI2-AS1 can be used as a novel diagnostic biomarker in DCM. However, the present study has some limitations. First, the sample size was relatively small. In addition, this study was performed based on bioinformatic methods, and the findings should be confirmed by experimental validation.
In summary, the present study identified IDI2-AS1 and XIST and related pathways that can help us understand the molecular mechanisms underlying the pathogenesis of DCM. XIST combined with IDI2-AS1 may be a novel promising novel biomarker for DCM diagnosis.

Data availability
The data that support the findings of this study are available from the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/), accession number GSE5406.