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. 2024 Feb 15;16(2):637–653. doi: 10.62347/AXOY3338

Transcriptomics data integration and analysis to uncover hallmark genes in hypertrophic cardiomyopathy

Peng Chen 1, Warda Yawar 2, Ayesha Rida Farooqui 3, Saqib Ali 4, Nida Lathiya 5, Zeeshan Ghous 6, Rizwana Sultan 7, Majid Alhomrani 8,9, Saleh A Alghamdi 8, Abdulraheem Ali Almalki 8, Ahmad A Alghamdi 8, Naif ALSuhaymi 10, Muhammad Razi Ul Islam Hashmi 11, Yasir Hameed 12
PMCID: PMC10918138  PMID: 38463581

Abstract

Introduction: Hypertrophic cardiomyopathy (HCM) is a heterogeneous disease that mainly affects the myocardium. In the current study, we aim to explore HCM-related hub genes through the analysis of differentially expressed genes (DEGs) between HCM and normal sample groups. Methods: The GSE68316 and GSE36961 expression profiles were obtained from the Gene Expression Omnibus (GEO) database for the identification of DEGs, to explore hub genes, and to perform their expression analysis. Clinical HCM and control tissue samples were taken for expression and promoter methylation validation analysis via RNA-sequencing (RNA-seq) and targeted bisulfite sequencing (bisulfite-seq) analyses. Then, other different bioinformatics tools were employed to perform STRING, lncRNA-miRNA-mRNA regulatory networks, gene enrichment, and drug prediction analyses. Results: In total, the top 20 DEGs, including 10 up-regulated and 10 down-regulated, were obtained from GSE68316. Out of the 20 DEGs, we subsequently identified the 8 most important hub genes including 5 up-regulated genes (EPB42, UQCRH, CA1, PFDN5, and LSM5) and 3 down-regulated genes (RPS24, TNS1, and RPL26). Expression and promoter methylation dysregulation of these genes were further validated on clinical HCM samples paired with controls. Next, we further investigated hub genes’ regulatory 6 miRNAs (has-mir-1-3p, has-mir-129-5p, has-mir-16-5p, has-mir-23b-3p, has-mir-27-3p, and has-mir-182-5p) and miRNAs regulatory 4 lncRNAs (NUTMB2-AS1, NEAT1, XIST, and GABPB1-AS1) in this study via the lncRNA-cricRNA-miRNA-mRNA regulatory network. Later on, gene enrichment analysis revealed that hub genes were enriched in various important pathways including Nitrogen metabolism, Ribosome, RNA degradation, Cardiac muscle contraction, and Coronavirus disease, etc. Finally, the drug prediction analysis highlighted different potential candidate drugs for altering the expression of hub genes in the treatment of HCM. Conclusion: In summary, the identification of key hub genes and their enrichment analysis in the current study may shed light on the mechanisms behind the occurrence and development of HCM.

Keywords: Hypertrophic cardiomyopathy, DEGs, hub gene, miRNA

Introduction

Hypertrophic cardiomyopathy (HCM), a complex cardiovascular genetic disorder, is reported in one out of every 500 individuals worldwide [1,2]. In recent years, awareness of HCM has significantly improved in clinical settings [3,4]. However, HCM diagnosis is still a complex process, just like other diseases including asthma, mitral regurgitation, and coronary artery disease, resulting in a high mortality rate [5]. From a clinical perspective, the prevalent pathological characteristics of HCM include myocyte hypertrophy, disarray, and interstitial fibrosis [6]. It is important to highlight that approximately 25% of individuals diagnosed with HCM experience left ventricular outflow tract obstruction [7]. The clinical symptoms of HCM vary from patient to patient, due to the natural complex history of the disease [8]. However, if this disease was diagnosed precisely, the patients suffering from HCM could manage this disease more effectively to improve their survival duration.

Currently, echocardiography and some imaging methods such as cardiac resonance imaging are important tools for diagnosing HCM. However, the detection accuracies of these tools are still very much compromised [6]. Moreover, it was also observed that different physical exercise methods have also made great contributions to the management of HCM even without increasing its further risk [9]. The surgical treatment method is a preliminary choice for clinicians to treat HCM. However, due to the increased mortality rate during surgeries, clinicians are still hesitant to choose this method [10].

Recently, a lot of emphasis has been given to research exploring the underlying molecular mechanism of HCM [11,12]. Genetic testing techniques have now become more accessible at the clinical level. The use of this technique in diagnosing HCM is increasing nowadays [13]. It is important here to understand that HCM has various genotypic and phenotypic variations. HCM was linked with approximately 1400 genetic mutations across more than 10 genes responsible for producing cardiac sarcolemmal protein [2]. Mostly, HCM developed due to a single heterozygous mutation [14,15]. However, multiple mutations can also lead to a severe form of HCM [16]. According to current research findings, scientists are of the opinion that gene mutations play a significant role in HCM [12]. Various sacromeric genes, namely MYH7, MYBPC3, TPM1, TNNT2, and TNNI3, have been identified as crucial genetic alterations in 34% of individuals with HCM [17-19]. These mutations are typically observed in familial cases with a hereditary pattern. However, it should be noted that gene mutations have not been confirmed in approximately 70% of HCM patients, leaving the underlying genetic causes unclear.

It was also reported in the medical literature that by exploring differentially expressed genes (DEGs) among HCM and normal control samples, potential HCM-associated candidate genes could be figured out [20]. The up-regulation of KRT1 and down-regulation of CYP1A1 at the mRNA level were reported to play a significant role in the development and progression of this disease [21,22]. Moreover, 3-methylcholanthrene and benzo(a)pyrene-induced cardiac hypertrophy was also associated with the overexpression of CYP1A1 [23].

In this paper, the Gene Expression Omnibus (GEO) datasets related to HCM were analyzed to identify the key hub genes having diagnostic and therapeutic importance for HCM patients. In summary, the results of our study would be helpful to improve the diagnosis and treatment methods for HCM patients.

Methods

Data collection

Normalized gene expression data of HCM patients (n = 7) and normal individuals (n = 5) were obtained from the GSE68316 dataset [24] by using the GEO database (https://www.ncbi.nlm.nih.gov/geo/). Moreover, another GEO dataset GSE36961 consisting of 106 HCM and 39 normal samples was used to obtain the normalized gene expression data for further expression validation. HCM samples in these datasets were collected prior to any clinical intervention.

DEGs identification

For identifying DEGs among HCM and control samples in this study, the R-based limma (Version 3.40.6) package in Bioconductor was utilized [25]. Genes showing p-values less than 0.01 and fold changes (FCs) greater than 1.2 were regarded as DEGs. A total of the top 20 genes including 10 up-regulated and 10 down-regulated genes were selected for further study in this manuscript.

Protein-protein interaction (PPI) network construction and hub genes exploration

The PPI network of selected 20 DEGs was constructed via the STRING database [26]. This database was built to provide vital information about predicted and experimental proven protein-protein interactions. In the present study, the PPI of the DEGs having interaction scores greater than 0.7 was considered for further analysis. Subsequently, the top eight hub genes (highly interacting genes) based on the degree method from the obtained PPI network were explored using Cytohubba [27], which is a plugin application in Cytoscape [28].

The lncRNA-miRNA-mRNA regulatory network

To develop the potential lncRNA-miRNA-mRNA regulatory network of the hub genes we used the following different online databases. The miRNAs were predicted via miRTarBase (http://mirtarbase.mbc.nctu.edu.tw/) [29], and miRDB (http://www.mirdb.org) [30] databases. The MiRcode (http://www.mircode.org/) repository [31] was utilized to predict lncRNAs targeting miRNAs. Ultimately, the lncRNA-miRNA-mRNA regulatory network was incorporated using Cytoscape.

Enrichment analysis

Gene Ontology (GO), BP (biological processes), CC (cellular components), MF (molecular function), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of the hub genes were carried out using DAVID (https://david.ncifcrf.gov/). This is a famous online enrichment annotation tool to explore the biological significance of a given list of genes [32,33].

Drug prediction analysis

DrugBank (http://www.drugbank.ca) database [34], which contains around seven thousand drug entries and four thousand proteins data, was used in this study to evaluate hub genes-associated potential targeted drugs.

Clinical HCM and normal control tissue sample collection

Following the approval of the ethics committee, we conducted a prospective collection of 25 pairs of HCM tissue and corresponding normal samples from patients who visited the Institute of Nishtar Hospital, Multan between August 2022 and May 2023. Prior to their participation, all individuals provided informed consent by signing consent forms. All patients included in the study were diagnosed with HCM and had not undergone any type of therapy.

Total RNA, DNA extraction, RNA sequencing (RNA-seq) and targeted bisulfite sequencing (targeted bisulfite-seq) analyses

Total RNA extraction from all these HCM and control tissues was done using the TRIzol® reagent method [35], and DNA extraction was done via the organic method [36-38]. RNA and DNA samples were sent to the Beijing Genomics Institute (BGI) Company for RNA-sequencing and targeted bisulfite-sequencing analysis.

Following RNA-seq and targeted bisulfite-seq analyses, the gene expression values of the hub genes were normalized using reads per kilo base million reads (RPKM) and fragments per kilo base million reads (FPKM). While methylation values were normalized as beta values. The obtained FPKM and beta values against hub genes in HCM and normal control samples were compared to identify differences in the expression and methylation levels.

Statistics of bioinformatics analysis

DEGs were identified using a t-test [39,40]. For GO and KEGG enrichment analysis, we used Fisher’s Exact test for computing statistical difference [41]. All the analyses were carried out in R version 3.6.3 software.

Results

The overall study design is given in Figure 1.

Figure 1.

Figure 1

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The overall workflow sheet diagram of our study.

DEGs and hub genes between HCM patients and normal individuals

The variations in the gene expression pattern between normal individuals and patient group in disease may be closely associated with the occurrence of that disease [42]. The normalized gene expression data were taken from the GSE68316 dataset via the GEO database and analyzed to discover DEGs specifically associated with HCM. By selecting p-values less than 0.01 and FC greater than 1.2 as the screening thresholds, a total of 850 DEGs were explored between HCM patients and healthy controls (Figure 2A). From the obtained DEGs, there were a total of 636 genes that were up-regulated and 270 genes that were down-regulated in HCM samples relative to healthy controls (Figure 2B). We selected the top 20 DEGs including 10 up-regulated and 10 down-regulated genes for further study in this manuscript (Table 1). After constructing a PPI of these 20 DEGs (Figure 2C), we further shortlisted the eight most important genes as the hub genes including Erythrocyte Membrane Protein Band 42 (EPB42), Ribosomal Protein S24 (RPS24), Ubiquinol-Cytochrome C Reductase Hinge Protein (UQCRH), Tensin 1 (TNS1), Ribosomal Protein L26 (RPL26), Carbonic Anhydrase 1 (CA1), Prefoldin Subunit 5 (PFDN5), and U6 SnRNA-Associated Sm-Like Protein (LSM5) (Figure 2D).

Figure 2.

Figure 2

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This figure illustrates key aspects of the analysis, incorporating visual representations of differentially expressed genes (DEGs) and hub genes. (A) showcases a volcano graph depicting the DEGs identified in the GSE68316 dataset. (B) presents an overarching heatmap displaying the expression patterns of DEGs identified in the GSE133054 dataset. (C and D) depict protein-protein interaction (PPI) networks for the DEGs and hub genes, respectively.

Table 1.

The top 20 dysregulated genes from the GSE68316 dataset

Sr. No Gene symbol Expression Status Log FC Adjust P-value
1 SELENBP1 Up-regulation 6.481 2.22E-17
2 LSM5 Up-regulation 6.360 2.91E-13
3 HBG1 Up-regulation 5.848 3.36E-06
4 TROAP Up-regulation 5.734 6.80E-17
5 BTN3A2 Up-regulation 5.246 4.26E-17
6 UQCRH Up-regulation 5.117 1.72E-18
7 CA1 Up-regulation 5.066 1.24E-13
8 SLCA4A1 Up-regulation 4.797 8.42E-15
9 PFDN5 Up-regulation 4.557 2.08E-18
10 PDE4DIP Up-regulation 4.386 7.35E-19
11 COMMD6 Down-regulation 3.843 2.02E-19
12 P2RY14 Down-regulation 3.299 7.18E-13
13 CCDC7 Down-regulation 3.080 4.90E-17
14 KIAA1841 Down-regulation 3.010 1.26E-19
15 TNS1 Down-regulation 2.997 2.31E-17
15 RPL26 Down-regulation 2.995 6.68E-20
17 RPS24 Down-regulation 2.902 6.91E-19
18 FURIN Down-regulation 2.793 1.51E-13
19 EPB42 Down-regulation 2.764 5.80E-17
20 HBM Down-regulation 2.682 2.17E-17
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Validation of hub genes using additional GEO dataset

To validate the expression levels of hub genes in another GEO expression dataset (GSE36961), the normalized gene expression data from 106 HCM patients and 39 healthy samples were obtained from the GEO database. Expression analysis of the hub genes using GSE36961 revealed that EPB42, UQCRH, CA1, PFDN5, and LSM5 hub genes were significantly up-regulated while RPS24, TNS1, and RPL26 hub genes were significantly down-regulated in HCM samples relative to healthy controls (Figures 3 and 4). Therefore, the re-analysis of hub gene expression further proves the important roles of these genes in HCM occurrence and progression. In summary, it is suggested that these hub genes between HCM and healthy individuals may be aberrantly expressed genes related to HCM.

Figure 3.

Figure 3

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The mRNA expression levels of the shortlisted 4 hub genes were validated using the GSE36961 dataset. (A) EPB42, (B) RPS24, (C) UQCRH, and (D) TNS1. A P < 0.01 was regarded as the selection criteria. EPB42 = Erythrocyte Membrane Protein Band 42, RPS24 = Ribosomal protein S24, UQCRH = Ubiquinol-cytochrome c reductase hinge protein, TNS1 = Tensin 1.

Figure 4.

Figure 4

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The mRNA expression levels of the shortlisted 4 hub genes were validated using the GSE36961 dataset. (A) RPL25, (B) CA1, (C) PFDN5, and (D) LSM5. A P < 0.01 was regarded as the selection criteria. RPL25 = Ribosomal Protein L26, CA1 = CA1 carbonic anhydrase 1, PFDN5 = Prefoldin subunit 5, LSM5 = U6 SnRNA-Associated Sm-Like Protein.

lncRNA-miRNA-mRNA regulatory network analysis

We constructed a lncRNA-miRNA-mRNA regulatory network of the hub genes using different online databases (referred to methodology) in the current study. We noticed in the current study that there were 189 miRNAs that target all hub genes including EPB42, RPS24, UQCRH, TNS1, RPL26, CA1, PFDN5, and LSM5 (Figure 5A). Further interaction analysis showed that there were 8 miRNAs (has-mir-23b-3p, has-mir-124-3p, has-mir-129-5p, has-mir-182-5p, has-mir-16-5p, has-mir-1-3p, has-mir-7b-5p, and has-mir-27a-23p) in total predicted 189 miRNAs which predicted to be targeting all the hub genes collectively (Figure 5B). Additional analysis revealed that, out of the 8 miRNAs, 6 miRNAs (has-mir-1-3p, has-mir-129-5p, has-mir-16-5p, has-mir-23b-3p, has-mir-27-3p, and has-mir-182-5p) were also found targeted by 246 lncRNAs (Figure 6A) and out of which only 4 lncRNAs including NUTMB2-AS1, NEAT1, XIST, and GABPB1-AS1 revealed to be collectively targeting all 6 miRNAs (Figure 6B).

Figure 5.

Figure 5

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PPI networks highlighting associations between miRNAs and identified hub genes. A. A network of overall predicted miRNAs targeting hub genes, and B. A PPI network between meaningful 8 miRNAs and hub genes. The red nodes are hub genes, green nodes are the miRNAs, and blue nodes are the meaningful miRNAs. PPI = Protein-protein interaction.

Figure 6.

Figure 6

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PPI networks highlighting associations between lncRNAs and identified miRNAs. (A) A network of overall predicted lncRNAs targeting miRNAs, and (B) A PPI network between meaningful 4 lncRNAs and miRNAs. Pink nodes are lncRNAs and blue nodes are the miRNAs. PPI = Protein-protein interaction.

GO enrichment analysis

GO analysis for predicting BP, CC, and MF of the hub genes was conducted via the DAVID tool. The identified hub genes were highly enriched in Reg. of DNA damage response, signal transduction, by p53 class mediator resulting, Pos. reg. of intrinsic apoptotic signaling pathway by p53 class mediator, Pos. reg. of intrinsic apoptotic signaling pathway in response to DNA damage, SRP-dependent cotranslational protein targeting to membrane, Protein targeting to ER, Establishment of protein localization to endoplasmic reticulum, Erythrocyte homeostasis, Nuclear-transcribed mRNA catabolic processes, nonsense-mediated decay, and Protein targeting to endoplasmic reticulum, etc. BP terms (Figure 7A).

Figure 7.

Figure 7

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GO enrichment analysis of the hub genes via the DAVID. (A) BP terms, (B) CC terms, and (C) MF terms. A P < 0.01 was regarded as the selection criteria. GO = Gene Ontology, BP = Biological process, CC = Cellular components, MF = Molecular function.

Concerning CC, the identified hub genes were mainly enriched in Lsm-7-Pat complex, Prefoldin complex, Lsm2-8 complex, Mitochondrial respiratory chain complex III, Respiratory chain complex III, U6 snRNP, Cytosolic ribosome, Ribosomal subunit, Ribosome, Ribonucleoprotein complex, etc. CC terms (Figure 7B).

Regarding MF, the identified hub genes were mainly involved in Arylestrease activity, Ubiquinol-cytochrome-c reductase activity, Oxidoreductase activity, acting on diphenois and related substances as donors, Protei glutamine gamma-glutamyltransferase activity, Carbonate dehydratase activity, Structural constituent of ribosome, etc. MF terms (Figure 7C).

KEGG pathway analysis

To further clarify the roles of hub genes (EPB42, RPS24, UQCRH, TNS1, RPL26, CA1, PFDN5, and LSM5) in the development of HCM, the KEGG pathway analysis was also performed by DAVID. The selected hub genes were mainly involved in Nitrogen metabolism, Ribosome, RNA degradation, Cardiac muscle contraction, and Coronavirus disease, etc. KEGG terms (Figure 8A-C).

Figure 8.

Figure 8

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KEGG enrichment analysis of the hub genes via the DAVID. (A) KEGG terms, (B) KEGG terms phylogram, and (C) Most significant KEGG term. A P < 0.01 was regarded as the selection criteria. KEGG = Kyoto Encyclopedia of Genes and Genomes.

Drug prediction analysis

Medical treatment is the preliminary choice to handle disease for patients who are suffering from HCM. Therefore, a selection of suitable candidate potential drugs is necessary. In the current study, with respect to identified hub genes, we explored some suitable therapeutic drugs for the treatment of HCM via the DrugBank database. For example, Rofecoxib drug was identified as the negative expression regulator of EPB42 mRNA expression (Table 2) while Acetaminophen was identified as the negative expression regulator of RPS24 mRNA expression (Table 2).

Table 2.

Hub genes associated drugs from DrugBank database

Sr. No Hub gene Drug name Effect Reference Group
1 EPB42 Rofecoxib Increased expression of EPB42 mRNA A22107 Approved
2 RPS24 Acetaminophen Decreased expression of RPS24 mRNA A20420 Approved
Vincristine A22706
3 UQCRH Disulfiram Decreased expression of UQCRH mRNA A21750 Approved
4 TNS1 Ciglitazone Increased expression of TNS1 mRNA A21647 Approved
Resveratrol A24017
5 RPL26 Bortezomib Increased expression of RPL26 mRNA A21434 Approved
6 CA1 Rifampicin Decreased expression of RPS24 mRNA A24040 Approved
7 PFDN5 Estradiol Decreased expression of PFDN5 mRNA A21098 Approved
Valproic acid A24690
8 LSM5 Tretinoin Decreased expression of LSM5 mRNA A24454 Approved
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Hub genes expression and promoter methylation analysis using clinical HCM and control samples

In the current study, using RNA-seq and targeted bisulfite-seq data of 25 HCM and control samples, the expression and promoter methylation levels of identified hub genes were validated. The expression and promoter methylation levels of these genes were validated using FPKM and beta values, which are quantitative values with widespread use in the RNA-seq and targeted bisulfite-seq analyses.

As shown in Figure 8A, it was noticed that EPB42, RPS24, UQCRH, TNS1, RPL26, CA1, PFDN5, and LSM5 hub genes were expressed in both HCM and normal control samples and FPKM values of EPB42, UQCRH, CA1, PFDN5, and LSM5 were higher while FPKM values of RPS24, TNS1, and RPL26 were higher in HCM samples as compared to normal controls (Figure 9A). Moreover, the beta values of the EPB42, RPS24, UQCRH, TNS1, RPL26, CA1, and PFDN5 were lower, while beta values of RPS24, TNS1, and RPL26 were higher in HCM samples as compared to normal controls (Figure 9B).

Figure 9.

Figure 9

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Validating hub gene expression and promoter methylation using clinical HCM and control samples via RNA sequencing and targeted bisulfite sequencing analysis. (A) FPKM values based expression validation of the hub genes, and (B) Beta values based promoter methylation validation of the hub genes. HCM = Hypertrophic cardiomyopathy, FPKM = Fragments Per Kilobase of transcript per Million mapped reads.

Discussion

Exploring the exact underlying molecular mechanisms of HCM can help to improve the clinical management of this disease. The current study was based on DEGs in HCM patients relative to healthy individuals, combined with protein-protein interaction analysis to discover a few key hub genes associated with HCM. We analyzed HCM GSE68316 and GSE36961 datasets from the GEO database to identify the top 20 DEGs, hub genes, and explore the molecular mechanisms of this disease.

By constructing a PPI network of the identified DEGs, we found 8 hub genes in HCM patients, namely EPB42, RPS24, UQCRH, TNS1, RPL26, CA1, PFDN5, and LSM5. The expression verification of these hub genes via the HCM GSE36961 dataset revealed that EPB42, UQCRH, CA1, PFDN5, and LSM5 hub genes were significantly up-regulated while RPS24, TNS1, and RPL26 hub genes were significantly down-regulated in HCM samples relative to healthy controls.

EPB42 is an ATP-binding protein, which is reported to regulate the association between protein 3 and ankyrin [43]. This protein may also have a key role in maintaining erythrocyte shape and providing mechanical properties [43]. Mutations in this gene or its dysregulation are linked to the recessive spherocytic elliptocytosis [44]. However, we are the first to report the dysregulation of this gene in HCM. RPS24 and RPL26 genes encode ribosomal proteins, which are part of the ribosome machinery [45]. Ribosomes are the organelles in the cells which carry out the synthesis of proteins [46]. Mutations in these genes are known to be associated with Diamond Blackfan Anemia and HCM [47]. UQCRH protein plays an important role to affect the mitochondrial function [48]. Recent data suggested that this protein also plays an important role in cardioprotection and its dysregulation may disturb normal heart functioning [49]. The TNS1 gene encodes for the TNS1 protein, which plays a key role in attaching the membrane of a cell to the extracellular matrix [50]. In recent research, the dysregulation of TNS1 resulted in myocardial infraction in zebra fish model [50]. CA1 gene in humans encodes for carbonic anhydrase 1 enzyme, which catalyzes the conversion of carbon dioxide to carbonic acid [51]. The dysregulation of this gene was previously correlated with heart failure and other diseases [52]. PFDN5 gene codes for a prefoldin alpha subunit family member involved in the stabilization of newly synthesized polypeptide [53]. Dysregulation of the PFDN5 gene was earlier linked to the development and progression of cancer [54]. However, we are the first to report the dysregulation of this gene in HCM. LSM5 encodes a protein that plays a key role in the assembly of spliceosome [55]. Abnormal expression of this gene was associated with circadian rhythms and HCM [56].

In recent times, the participation of miRNAs, and lncRNAs in HCM development is a hot topic for researchers. In this regard, different studies have discovered the involvement of a variety of miRNAs and lncRNAs in HCM. For example, the lncRNA Rp5-833A20.1 was involved in the prevention of miR-382-5p to target NFIA, which is associated with HCM [57]. The lncRNA MIAT was verified to promote myocardial fibrosis after acute ischemia by targeting miRNA24 to target two important mRNAs including Furin and TGF-β1 [58]. The lncRNA ROR is also reported to sponge miR-133 to dysregulate two important mRNAs (ANP and BNP), associated with HCM [59]. In the current manuscript, concerning lncRNA-miRNA-mRNA regulatory network, we explored 6 miRNAs (has-mir-1-3p, has-mir-129-5p, has-mir-16-5p, has-mir-23b-3p, has-mir-27-3p, and has-mir-182-5p) targeting all the shortlisted hub genes and getting targeted by one only 4 lncRNAs including NUTMB2-AS1, NEAT1, XIST, and GABPB1-AS1. These critical data may expand our knowledge about the regulatory mechanisms of HCM. In addition, functional analysis revealed that HCM-associated dysregulated hub genes were majorly involved in a variety of GO and KEGG terms (Figures 6 and 7). Lastly, we have also suggested a few hub gene-related therapeutic drugs from the DrugBank database whose use might help to treat HCM patients.

Previously, it was reported in the medical literature that HCM is the outcome of genetic mutations across the oncoprotein coding gene in Mendel’s autosomal dominant genetic pattern [60]. The discovery of novel dysregulated hub genes in the current study may be helpful for clinically determining the diagnosis of HCM patients. The outcomes of the present study may also guide clinicians to design and use novel treatment therapies for HCM patients.

Conclusion

In the present study, 8 key dysfunctional hub genes (EPB42, RPS24, UQCRH, TNS1, RPL26, CA1, PFDN5, and LSM5) were discovered via detailed analysis between HCM and control samples. Via further enrichment analysis, we revealed that underlying molecular mechanisms behind HCM were associated with Nitrogen metabolism, Ribosome, RNA degradation, Cardiac muscle contraction, and Coronavirus disease signaling pathways. However, this study requires further experimentation to validate the analysis results. One limitation of this study is the focus on developmental diseases as the research subject. As a result, there is no availability of clinical data to accurately determine the patients’ origins, ages, and genders. This restricts the generalizability and applicability of the findings to specific groups. Additionally, there is a scarcity of clinical samples to validate crucial analysis results.

Disclosure of conflict of interest

None.

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