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Introduction

Hypertrophic cardiomyopathy (HCM) is an autosomal dominant heart disease characterized by asymmetric hypertrophy of the left ventricle, primarily caused by enlarged myocytes, in the absence of other diseases (1,2). The etiology of HCM is mainly associated with genetic factors, endocrine disorders or autoimmune diseases (3). The estimated prevalence of HCM in the general population is ~0.6%, and varies among children, adolescents and adults (4). With advancements in diagnostic techniques, the prevalence of HCM has shown an increasing trend (5). Clinical manifestations of HCM include chest tightness, angina, dyspnea and syncope. These symptoms are progressive and may lead to serious complications such as heart failure and sudden cardiac death (6-8). Despite substantial progress in the treatment of HCM using personalized strategies (9,10), a definitive cure for this condition remains unknown.

Autophagy (AT) is a biochemical process that involves the degradation and recycling of damaged or discarded intracellular components by lysosomes to protect cells against external environmental conditions, such as hypoxia and oxidative stress (11). Administration of AT inducers, such as rapamycin, in animal models, inhibits mTOR and promotes AT, effectively protecting cardiomyocytes and improving cardiomyopathy phenotypes (12,13). In addition, studies have demonstrated a close relationship between AT and HCM, suggesting that targeting AT is a promising therapeutic strategy for HCM (14-16). However, the precise roles of AT-related genes (ARGs) in HCM remain unclear, necessitating further investigation of the relationship between ARGs and HCM.

In this study, key ARGs related to the development of HCM were identified using bioinformatics analysis. The association between these genes and HCM was determined through functional annotation, pathway enrichment, protein-protein interaction (PPI) and immune infiltration analyses. In addition, potential drugs for the treatment of HCM were predicted, and the results of bioinformatics analysis were validated through reverse transcription-quantitative PCR (RT-qPCR). The findings provide a valuable theoretical foundation for the development of novel diagnostic and therapeutic strategies for HCM.

Materials and methods

Data collection and processing

The HCM dataset GSE180313(17) was obtained from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/). This dataset contains heart tissues from 7 healthy individuals and 13 patients with HCM. Preprocessed and merged data were integrated into a unified dataset. The ‘limma’ package (18) in R software (v.3.6.3) (19) was used to identify differentially expressed genes (DEGs) between the HCM and control groups, with the screening criteria being set as a |log2FC| value of <0.5 and a P-value of <0.05. The ‘ggplot2’ package (20) in R was used to generate heat maps and volcano plots. Information regarding ARGs was obtained from the Human Autophagy Database (http://www.autophagy.lu/index.html) and the Gene Set Enrichment Analysis (GSEA) website (http://software.broadinstitute.org/gsea/index.jsp). The extracted ARGs were processed and integrated into a gene set known as ARGs. GSEA was used to investigate the overall association between AT and HCM and identify potential biological processes involving ARGs that were associated with the pathogenesis of HCM. Genes in the GSE180313 dataset were scored using the AT dataset from GSEA, resulting in the calculation of normalized enrichment scores (NESs). P<0.05 was considered to indicate significant enrichment. The workflow of the present study is shown in Fig. S1.

Identification and functional enrichment analysis of differentially expressed ARGs (DEARGs)

DEARGs were obtained by intersecting the DEGs identified in the GSE180313 dataset with the integrated ARG set. The overlapping genes (DEARGs) were visualized on a Venn diagram. Subsequently, the cluster profiler package (v4.8.3) (21) in R and DAVID (david.ncifcrf.gov/) were used to implement Gene Ontology (GO; https://geneontology.org/) and Kyoto Encyclopedia of Genes and Genomes (KEGG; https://www.genome.jp/kegg/) enrichment analyses of DEARGs. P<0.05 was considered to indicate significant enrichment.

PPI network and identification of hub genes and key modules

DEARGs were imported into the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database (v11.09) (https://string-db.org/) for PPI analysis with default settings. The resulting PPI network was visualized using Cytoscape software (v.3.7.2) (22). Subsequently, the MCODE plug-in (V 3.7.1) (20) was used to filter and visualize key PPI networks, resulting in the identification of key modules containing hub genes.

Receiver operating characteristic (ROC) analysis of hub genes

The ROC curves of hub genes were constructed using data from the GSE180313 and GSE36961(23) datasets. The area under the curve (AUC) was quantified for comparison, and only genes with AUC values of >0.6 were considered to have statistically significant diagnostic potential.

Immune infiltration analysis

To investigate the immune microenvironment of HCM and key DEARGs, CIBERSORTx (https://cibersortx.stanford.edu/) was employed to analyze the differences in immune infiltration between patients with HCM and healthy controls. Additionally, this algorithm was utilized to determine the proportions of various immune cell types.

Prediction of therapeutic drugs

Key differentially expressed AT-associated genes and compound interaction data from the Drug Signatures Database (DSigDB; http://dsigdb.tanlab.org/) were extracted using the Enrichr online tool (http://amp.pharm.mssm.edu/Enrichr). Drugs for the treatment of HCM were predicted based on hub genes.

Cell culture and model construction

The H9c2 immortalized rat cardiomyocyte-like cell line (The Cell Bank of Type Culture Collection of The Chinese Academy of Sciences) is commonly used in cardiac research in vitro (24). H9c2 cells were cultured in high-glucose DMEM (Hyclone; Cytiva) supplemented with 10% fresh fetal bovine serum (HyClone; Cytiva) and 1% penicillin-streptomycin (Gibco; Thermo Fisher Scientific, Inc.) under standard conditions (37˚C, 5% CO2, 95% humidity and 21% oxygen). Cells from passages 3-10 were used for subsequent experiments. Angiotensin II (AngII; GlpBio Technology, Inc.) was used to induce hypertrophy in H9c2 cells. The cells were incubated with AngII at different concentrations (50, 100, 200 and 400 nM) at 37˚C for 24 h, and the optimal concentration was determined based on the mRNA and protein expression of atrial natriuretic peptide (ANP) and brain natriuretic peptide (BNP). For further experimentation, the cells were incubated with the determined optimal concentration of AngII at 37˚C for 12, 24, 36 and 48 h.

RT-qPCR

Total RNA was extracted from H9c2 cells using TRIzol reagent (Beijing Solarbio Science & Technology Co., Ltd.) and reverse transcribed using the PrimeScript RT reagent kit (Monad Biotech Co., Ltd.) according to the manufacturer's instructions. qPCR was conducted using the SYBR Green qPCR Master Mix (cat. no. B21203; Bimake.com) on a BIO-RAD CFX Connect Real-Time PCR Detection System (Bio-Rad Laboratories, Inc.). The qPCR protocol included an initial denaturation step at 95˚C for 10 min, followed by 40 cycles of thermal cycling with denaturation at 95˚C for 15 sec and annealing at 60˚C for 1 min. The primer sequences used for qPCR are shown in Table SI. ACTB served as the internal reference, and the relative mRNA expression of target genes was calculated using the Cq (2-ΔΔCq) method (25).

Western blotting

To extract total proteins, H9c2 cells were lysed in RIPA buffer supplemented with protease inhibitors (Beijing Solarbio Science & Technology Co., Ltd.) on ice. The extracted proteins were quantified using a BCA assay kit (GlpBio Technology, Inc.) and denatured by boiling for 5 min, and subsequently separated by on 12% gels by sodium dodecyl sulfate-polyacrylamide gel electrophoresis, with 40 µg of protein loaded per lane. The separated proteins were transferred to a PVDF membrane (MilliporeSigma), which was incubated with 5% skimmed milk powder at room temperature for 2 h on a shaker. Subsequently, the membrane was incubated with primary antibodies against ANP (1:500; cat. no. 27426-1-AP; Proteintech Group, Inc.), BNP (1:500; cat. no. A2179; ABclonal, Inc.), LC3 (1:1,000; cat.no. 381544; Chengdu Zen-Bioscience Co., Ltd.), P62 (1:1,000; cat. no. 380612; Chengdu Zen-Bioscience Co., Ltd.) and β-actin (1:1,000; cat. no. 66009-1-Ig; Proteintech Group, Inc.) at 4˚C overnight. The following day, the membrane was incubated with horseradish peroxidase-conjugated mouse and rabbit secondary antibodies (1:5,000; cat. nos. 511103 and 511203, respectively; Chengdu Zen-Bioscience, Co., Ltd.) at room temperature for 2 h. Protein bands were visualized using the Ultra High Sensitivity ECL kit (catalog no. GK10008; GlpBio Technology, Inc.) and captured using the FluorChem FC3 System (ProteinSimple). ImageJ software (v1.8.0.345; National Institutes of Health) was used to semi-quantify the optical density of protein bands.

Detection of autolysosome acidification

To assess the level of AT in cells, LysoTracker Red was used to label intracellular lysosomes, as autophagosomes can bind to lysosomes. Briefly, H9c2 cells were incubated with 50 nM LysoTracker Red working solution (Beyotime Institute of Biotechnology) at 37˚C for 30 min in the dark and subsequently examined using a fluorescence microscope.

Immunofluorescence analysis

LC3 expression in H9c2 cells was detected through immunofluorescence staining. Briefly, the cells were washed twice with PBS, fixed with 4% paraformaldehyde (biosharp life sciences) at room temperature for 10 min, blocked with 2% BSA (cat. no. CAS#9048-46-8; Shanghai Yuanye Bio-Technology Co., Ltd.) at room temperature for 30 min and incubated with anti-LC3 antibody (1:200; cat. no. 381544; Chengdu Zen-Bioscience Co., Ltd.) at 4˚C overnight. The following day, the cells were incubated with a fluorescently labeled secondary antibody (1:200; cat. no. A32732; Thermo Fisher Scientific, Inc.) at room temperature for 1 h. Thereafter, nuclei were stained with DAPI at room temperature for 5 min and the cells were examined using a fluorescence microscope.

Statistical analysis

Statistical analysis was performed using the GraphPad Prism 8 software (Dotmatics). The unpaired two-tailed Student's t-test was used to compare the differences between two groups. One-way analysis of variance and Dunnett's multiple comparison test were employed to assess the differences among multiple groups. Data are expressed as the mean ± standard deviation of 3 repeats. P<0.05 was considered to indicate a statistically significant difference.

Results

Study protocol

The overall protocol of the present study is shown in Fig. S1. The patient data used in the present study were derived from the GSE180313 dataset in the GEO database.

Identification of DEGs between the HCM and control groups

After pre-processing and normalization of the GSE180313 dataset, a total of 966 DEGs were identified between the HCM and control groups. Of these 966 DEGs, 510 genes were upregulated and 456 genes were downregulated. A volcano plot and a heat map were generated to visualize the DEGs (Fig. 1A and B).

Figure 1 Identification of DEGs in hypertrophic cardiomyopathy. (A) Volcano plot of DEGs in the GSE180313 dataset (log2FC >0.5 and adjusted P<0.05). (B) Heatmap clustering of genes with markedly different expression in HCM compared with normal control samples in GSE180313 (log2FC >0.5 and adjusted P<0.05). (C) Gene Set Enrichment Analysis of ARGs in the GSE180313 dataset. (D) Venn diagram showing common genes between the GSE180313 dataset and ARGs. (E) Clustered heatmap of differentially expressed ARGs in the GSE180313 dataset. Correlation coefficients are plotted with negative correlation shown in purple and positive correlation shown in orange. ARG, autophagy-related gene; CON, control; DEG, differentially expressed gene; FC, fold change; HCM, hypertrophic cardiomyopathy.

Identification of DEARGs and enrichment analysis

GSEA was used to compare ARGs between the HCM and control groups. Fig. 1C demonstrates the significant differences in ARG expression between the two groups (|NES|=1.521; P<0.05). This indicates that ARGs could be a crucial characteristic of HCM and that their dysregulation supports a link between HCM and autophagy. Furthermore, a total of 1,167 ARGs were identified after pre-processing of the integrated gene set. These ARGs were intersected with DEGs to obtain 58 DEARGs (Fig. 1D). A heat map was generated to visualize the expression patterns of these DEARGs in the HCM and control groups (Fig. 1E).

Functional and mechanistic analyses of DEARGs

To investigate the functions and pathways of DEARGs, GO and KEGG enrichment analyses were performed using DAVID. The results demonstrated that DEARGs were significantly enriched in biological processes such as ‘peptidyl-serine phosphorylation’, ‘cellular response to oxidative stress’, ‘regulation of autophagy’ and ‘response to UV’; molecular functions such as ‘protein serine kinase activity’, ‘tau protein binding’ and ‘death domain binding’; and cellular components such as the ‘mitochondrial outer membrane’ and ‘organelle outer membrane’ (Fig. 2A-C). KEGG analysis demonstrated that the DEARGs were notably enriched in the ‘AGE-RAGE signaling pathway in diabetic complications’, ‘PI3K-Akt signaling pathway’, ‘measles’, ‘hepatitis C’, ‘AMPK signaling pathway’ and ‘EGFR tyrosine kinase inhibitor resistance’ (Fig. 2D). Interactions were identified between DEARGs and the aforementioned functions and pathways through gene and pathway cross-talk mapping, suggesting that multiple genes and pathways may be involved in the regulation of DEARGs in HCM (Fig. 2E-H). Overall, these findings suggested that ARGs regulate the progression of HCM through intricate interplay among multiple gene functions and pathways.

Figure 2 GO and KEGG enrichment analyses of DEARGs. GO enrichment analysis of DEARGs in the (A) BP, (B) CC and (C) MF categories. (D) KEGG enrichment analysis of DEARGs. Crosstalk analysis between DEARGs and gene functions in (E) BP, (F) CC and (G) MF categories, and (H) KEGG pathways. AGE-RAGE, advanced glycation end product-receptor for advanced glycation endproducts; AMPK, AMP-activated protein kinase; BP, biological process; CC, cellular component; DEARG, differentially expressed autophagy-related gene; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; MF, molecular function; p.adjust, adjusted P-value; PML, promyelocytic leukemia.

PPI network analysis, functional module construction and hub gene identification

A PPI network of 58 DEARGs was constructed using the STRING database (Fig. 3A). After the network was imported into the Cytoscape software, the Maximal Clique Centrality algorithm was used to identify a sub-network comprising 32 hub genes. To filter these hub genes, the MCODE plug-in was used to identify important functional modules within the PPI network. Notably, a key cluster in the network consisted of a functional module with 10 nodes and 14 edges, including EIF4EBP1, MCL1, PIK3R1, CCND1, PPARG, SMPD1, RICTOR, NOS3, SNCA and UBB. Fig. 3B and C illustrate the interactions between DEARGs and hub genes.

Figure 3 PPI network and identification of hub genes. (A) PPI network of DEARGs constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins database. (B) Crosstalk between the top 10 hub genes based on the Maximal Clique Centrality algorithm and other DEARGs. (C) Crosstalk among the 10 hub genes, where the intensity of the dot color represents the higher rank of the hub gene. DEARG, differentially expressed autophagy-related gene; PPI, protein-protein interaction.

Diagnostic value of the hub genes

ROC curves were generated to evaluate the diagnostic efficacy of the 10 hub genes (Fig. 4A). In the GSE180313 dataset, all hub genes exhibited AUC values of >0.8, indicating a significant association with HCM and promising diagnostic potential. An external dataset (GSE36961) was used to validate these findings (Fig. 4B). Notably, discrepancies were observed in the results of hub gene analysis between the two datasets. During the validation of external datasets, it was observed that the AUC values for EIF4EBP1, MCL1, and SMPD1 were <0.7, indicating that these three hub genes exhibit limited diagnostic performance for HCM. By contrast, most other hub genes demonstrated robust diagnostic capabilities (AUC >0.7), suggesting a potential association between autophagy and HCM. Nonetheless, the significance of these hub genes warrants further investigation in future studies

Figure 4 Assessing the diagnostic value of hub genes. (A) ROC curves showing the diagnostic capability of hub genes for HCM in the GSE180313 dataset. (B) ROC curves showing the diagnostic capability of hub genes for HCM in the GSE36961 dataset. AUC, area under the curve; HCM, hypertrophic cardiomyopathy; ROC, receiver operating characteristic.

Prediction of drugs and molecular docking simulations

The DSigDB in the Enrichr platform was used to identify small-molecule drugs targeting hub genes for the treatment of HCM. A total of 1,332 drugs with potential therapeutic value were identified based on the degree of gene-compound match and median number, with the screening criteria being set as a false discovery rate of <0.05 and composite scores of >5,000. The top 10 small-molecule drugs with the most significant impact on the expression of hub genes are shown in Fig. 5A. Among these, rapamycin and melatonin are the top two candidates. Fig. 5B and C show the molecular structure of rapamycin and melatonin (Mel; N-acetyl-5-methoxytryptamine). Research has demonstrated that rapamycin exerts a notable effect on the treatment and prevention of HCM (26,27). Mel possesses antioxidant properties and exerts protective effects against various cardiovascular diseases, including diabetic cardiomyopathy and myocardial hypertrophy (28,29). To gain insights into the binding between hub genes and predicted drugs, molecular docking simulations were performed using rapamycin and Mel as examples (Fig. 5D and E).

Figure 5 Potential target drug prediction and molecular docking simulation. (A) Top 10 targeted drugs predicted and ranked based on their combined score in the Drug Signatures Database. (B) Chemical structure of rapamycin. (C) Chemical structure of melatonin. The molecular docking simulation revealed the formation of a stable complex between (D) rapamycin and (E) melatonin and hub genes.

Relationship between ARGs and immune cell infiltration in HCM

Cellular and humoral immune functions serve a crucial role in the development of HCM (30). The relative proportions of infiltrating immune cells in heart samples from the GSE180313 dataset were assessed and quantified using the CIBERSORT algorithm (Fig. 6A). The resulting heatmap illustrates the relationships between various infiltrating immune cells (Fig. 6B). In addition, violin plots were generated to visualize the expression profiles of the 20 immune cell subtypes in the control and HCM groups (Fig. 6C). Only the infiltration levels of T follicular helper (Tfh) cells were significantly different between the HCM and control groups, with lower levels being observed in the HCM group. Therefore, Tfh cells were identified as differentially infiltrating immune cells. Furthermore, the correlation between immune cells and the 10 hub DEARGs was examined (Fig. 7). Nine hub genes, except for CCND1, exhibited varying correlations with six types of immune cells, namely Tfh cells, monocytes, neutrophils, regulatory T cells, resting natural killer cells and T cells CD4 memory resting. These findings suggested that the hub genes serve an important role in the immune response to HCM.

Figure 6 Association between SRGs and immune cell infiltration in HCM. (A) Proportion of infiltrating immune cells in the samples from the GSE180313 dataset based on the CIBERSORT algorithm. (B) Correlation heatmap of DIICs in the GSE180313 dataset displaying the correlation coefficients, with negative correlations represented by blue and positive correlations represented by red. (C) Violin plots showing the comparison of infiltrating immune cells between normal and hypertrophic cardiomyopathy samples in the GSE180313 dataset. DIICs, differentially infiltrating immune cells; NK, natural killer.

Figure 7 Correlation between key genes and infiltrating immune cells. The association between hub genes and immune cell infiltration is presented, only including immune cells with a P-value <0.05. abs(cor), absolute value of correlation; NK, natural killer.

Validation of hub genes in an in vitro model of HCM

To validate the expression of the hub genes EIF4EBP1, MCL1, PIK3R1, CCND1, PPARG, SMPD1, RICTOR, NOS3, SNCA and UBB in vitro, H9c2 cells were stimulated with AngII to induce HCM. Western blotting and RT-qPCR were used to determine the optimal concentration and duration of AngII treatment. The results demonstrated that when H9C2 cells were pretreated with AngII for the same duration, the highest protein expression levels of ANP and BNP were observed in the 100 nM AngII group (Fig. 8A-C). Additionally, when H9C2 cells were pretreated with 100 nM AngII for different time periods, the highest ANP and BNP protein expression levels were found at 24 h of pretreatment (Fig. 8D-F). Consistent with these changes in protein expression, the mRNA levels also corroborated this finding (Fig. 8G-J). Therefore, a pretreatment of H9C2 cells with 100 nM AngII for 24 h was selected to induce hypertrophy. Western blotting was used to evaluate the expression levels of the AT-associated proteins LC3 and P62 in the AngII and control groups (Fig. 9A). As shown in Fig. 9B and C, the LC3/β-actin ratio was significantly lower and the protein expression of P62 was higher in the AngII group. Furthermore, autolysosome acidification was detected, and immunofluorescence analysis was performed to assess the level of AT. H9c2 cells stained with LysoTracker Red showed reduced fluorescence intensity in the AngII group compared with that in the control group (Fig. 9D). Immunofluorescence analysis revealed a decrease in LC3 fluorescence intensity in the AngII group compared with that in the control group (Fig. 9F). The aforementioned results indicate that autophagy is reduced in the AngII-induced H9C2 cell hypertrophy model. To ensure the reliability of the results, RT-qPCR was performed to evaluate the expression levels of hub genes in both groups. Based on PPI network analysis, EIF4EBP1, MCL1, PIK3R1, CCND1 and PPARG were identified as the most significant hub ARGs associated with HCM. RT-qPCR revealed that EIF4EBP1 and PPARG were downregulated, while MCLl, PIK3R1 and CCND1 were upregulated after treatment with AngII (Fig. 10). Therefore, we propose that EIF4EBP1, MCL1, PIK3R1, CCND1 and PPARG play roles in the regulation of autophagy in HCM.

Figure 8 AngII-induced H9c2 cell hypertrophy model. (A-C) Immunoblotting analysis of ANP and BNP protein expression levels in treatment with different concentrations of AngII, along with quantification of the immunoblotting results. β-actin was used as an internal control. (D-F) Immunoblotting analysis of ANP and BNP protein expression levels in treatment with the same concentrations of AngII for different durations, along with quantification of the immunoblotting results. β-actin was used as an internal control. (G and H) Relative mRNA expression levels of (G) ANP and (H) BNP after treatment with different concentrations of AngII. (I and J) Relative mRNA expression levels of (I) ANP and (J) BNP after treatment with the same concentration of AngII for different durations. Error bars represent the SD. Data are presented as the mean ± SD (n=3). *P<0.05, **P<0.01 and ***P<0.001. ns, not significant. AngII, angiotensin II; ANP, atrial natriuretic peptide; BNP, brain natriuretic peptide; ns, not significant.

Figure 9 AngII induces changes in the level of autophagy in H9c2 cell hypertrophy. (A) Representative western blotting bands of LC3 and P62. Relative protein expression levels of (B) LC3 and (C) P62, estimated using ImageJ software. (D,E) Lysotracker red fluorescence intensity represented the number of autolysosomes (magnification, x400; scale bar, 100 µm). (F) Representative images of LC3 immunofluorescence in the AngII-induced H9c2 cell hypertrophy model (magnification, x200; scale bar, 50 µm). Error bars represent the SD. Data are presented as the mean ± SD (n=3). *P<0.05. Con, control; AngII, angiotensin II.

Figure 10 Revalidation of hub genes in AngII-induced H9c2 cell hypertrophy. (A-J) mRNA expression levels of hub genes were detected using reverse transcription-quantitative PCR. Error bars represent the SD. Data are presented as the mean ± SD (n=3). *P<0.05 and **P<0.01. ns, not significant; AngII, angiotensin II.

Discussion

HCM is a prevalent hereditary cardiac disease that predisposes individuals, especially young athletes, to sudden death, also known as exercise-induced sudden cardiac death (31,32). Although HCM does not progress rapidly, its complications such as sudden arrhythmogenic death, heart failure and atrial fibrillation can occur abruptly or worsen under any circumstances, posing a severe threat to the life of patients (33). According to the 2018 Epidemiological Survey statistics, HCM remains a major health concern worldwide, affecting ~88% of the global population and imposing a long-lasting socioeconomic burden (34). However, no precise and efficient therapeutic strategy has been developed to date. AT serves an essential role in the development and progression of cardiovascular diseases such as myocardial infarction, aortic coarctation, atherosclerosis and ischemic cardiomyopathy (35-37). Therefore, targeting AT represents a promising strategy for the treatment of HCM. However, the precise role of AT in the pathogenesis of HCM warrants further investigation. In the present study, bioinformatics analysis was used to examine the roles and mechanisms of ARGs in the development of HCM to explore novel avenues for effective treatment.

A total of 58 DEARGs associated with HCM were identified through comprehensive analysis of a GEO dataset and an ARG set. GSEA revealed a significant association between ARGs and HCM, suggesting that AT serves a crucial role in the development of HCM. Furthermore, GO functional annotation and KEGG pathway enrichment analysis demonstrated that the DEARGs were closely associated with various biological processes, cellular components and molecular functions related to AT (Fig. 2). Notably, the findings indicated that the pathogenesis of HCM involves not only AT but also other classical pathways such as the ‘AGE-RAGE signaling pathway in diabetic complications’ and the ‘PI3K-Akt signaling pathway’. AT serves as a cytoprotective mechanism that maintains cellular homeostasis by regulating intracellular and extracellular catabolic and anabolic processes through the lysosomal degradation pathway (38,39). Alterations in the levels of cardiomyocyte AT have been reported to induce functional or morphological changes, including apoptosis, atrophy, fibrosis or hypertrophy (40-42). A recent study revealed that excessive cardiomyocyte AT can result in lysosomal storage disorders that lead to cellular damage and cardiac dysfunction (16). These findings highlight the extensive investigation of the role of AT in HCM, while emphasizing the need for further exploration of other molecules and pathways. In the present study, PPI network analysis revealed 10 hub genes (EIF4EBP1, MCL1, PIK3R1, CCND1, PPARG, SMPD1, RICTOR, NOS3, SNCA and UBB) associated with the development of HCM. The diagnostic value of these hub genes was assessed through ROC analysis and validated through cellular experiments. All hub genes except for SMPD1 exhibited significant diagnostic potential. Notably, EIF4EBP1, MCL1, PIK3R1, CCND1 and PPARG were differentially expressed between control and AngII-treated H9c2 cells. In particular, PIK3R1, MCL-1 and CCND1 were significantly upregulated in AngII-treated cells. These results suggested that the aforementioned five ARGs serve as promising therapeutic targets for HCM.

EIF4EBP1 functions as a regulatory protein in cell signaling pathways and is involved in the initiation and progression of various diseases (43-46). Upregulation of EIF4EBP1 has been shown to delay the progression of systemic lupus erythematosus through B-cell AT (43). Additionally, EIF4EBP1 acts as a tumor suppressor gene. Upregulation of EIF4EBP1 promotes the development and metastasis of breast cancer, whereas downregulation of EIF4EBP1 in breast cancer impedes the proliferation of pituitary tumor cells (45,46). Several studies have indicated that EIF4EBP1 serves as a biomarker for evaluating the prognosis of tumors (44). MCL1, an anti-apoptotic gene, serves a crucial role in cell survival, metabolism, apoptosis, immunity and tumor formation (47-49). It has attracted attention in research on hematological malignancies (48). Inhibition of MCL1 can promote tumor cell apoptosis and enhance the cytotoxicity or antitumor immune efficacy of drugs in acute myeloid leukemia (AML) (50). Dysregulation or inhibition of MCL1 is an essential factor contributing to drug resistance in various cancer types (51-54), as MCL1 is a major regulatory protein of the intrinsic apoptosis pathway. Given that the PIK3R1/Akt/mTOR signaling pathway is regulated by AT, targeting PIK3R1 can reduce cellular oxidative stress and apoptosis, thereby regulating cardiomyocyte apoptosis in the treatment of heart diseases (55). In addition, PIK3R1 is positively associated with immune activation and serves an essential role in regulating the tumor microenvironment, inflammation and drug sensitivity or resistance (56-59).

CCND1, located on chromosome 11q, is a member of the cell cycle protein D family and serves a crucial role in anti-aging signaling pathways (60). Liu et al (61) found that downregulation of CCND1 activated anti-aging signaling pathways, enhanced the expression of antioxidant genes, suppressed the production of reactive oxygen species and prevented the osteogenic differentiation of valve interstitial cells in heart valve disease. CCND1 has been revealed to regulate the viability, proliferation and cell cycle of oral squamous cell carcinoma cells through microRNA-519d-3p (62). Furthermore, detection of CCND1 rearrangements holds diagnostic value for blood disorders (63). PPARG, a member of the peroxisome proliferator-activated receptor subfamily, serves a crucial role in regulating various signaling pathways involved in the pathophysiological mechanisms of various diseases and states, including inflammation, lipid metabolism, AT, apoptosis and cell cycle progression (64,65). Notably, PPARG has been closely associated with chemosensitivity in gastric cancer, AML, colorectal cancer and breast cancer (66), highlighting its important role in modulating the response of tumor cells to chemotherapeutic agents. Mechanistically, PPARG influences chemosensitivity by regulating cell cycle progression and AT, and participating in inflammatory responses (67). In the present study, DSigDB was utilized to predict potential drugs targeting the identified hub genes. The results indicated that rapamycin and Mel are promising drugs targeting ARGs and pathways in HCM. Previous studies have demonstrated the therapeutic efficacy of rapamycin and Melatonin in HCM, which is consistent with the findings of the present study (28,68). However, the therapeutic efficacy of other drugs warrants further investigation and validation.

The precise role of the immune system in the development of HCM remains elusive. Previous studies have demonstrated that AT serves an essential role in immunity, primarily through its involvement in pathogen clearance and inflammation regulation (69-71). Therefore, in the current study, the relationship between ARGs and immune cell infiltration in HCM was investigated. Only the infiltration levels of Tfh cells were significantly different between the HCM and control groups. Tfh cells represent an independent subset of CD4(+) T effector cells involved in humoral immunity and activation of other immune cells (72). The infiltration levels of Tfh cells were positively correlated with EIF4EBP1 but negatively correlated with MCL1 and PIK3R1. Furthermore, the infiltration levels of monocytes and neutrophils were positively correlated with MCL1, and those of regulatory T cells were negatively correlated with PPARG. However, CCND1 did not exhibit a significant correlation with the 22 immune cell types examined in the present study. These results provided valuable insights into how ARGs influence the development of HCM by regulating immunity. However, the present study did not validate the relationship between immune infiltrating cells and core ARGs through specific experiments. Further research and evidence in this area are required in the future.

The level of autophagic activity reported in studies on HCM is inconsistent possibly due to differences in study participants, experimental design and sample size (73,74). Upregulation of certain ARGs is also observed in HCM, which may be attributed to the following reasons: Firstly, it can be considered as a compensatory mechanism wherein cells attempt to restore autophagic function by increasing the expression of specific ARGs. This upregulation aims to compensate for the reduced autophagic activity either by enhancing particular steps of AT or by augmenting the number of autophagosomes (73,75,76). Secondly, due to the complex nature of HCM as a disease, involving alterations in multiple genes and signaling pathways during its pathological progression, certain factors within this process might contribute to the upregulation of select ARGs (77-79). The objective of the present study was to investigate the function and expression of ARGs in HCM through bioinformatics analysis and cellular experiments in order to predict potential drugs for HCM. A total of 10 drugs targeting hub genes were identified, including rapamycin and Mel. Studies have validated the therapeutic or preventive effects of rapamycin in HCM (26,27,80). Activation of the mTOR signaling pathway serves a crucial role in regulating cell proliferation and protein activation. Rapamycin inhibits mTOR, directly influencing metabolic disorders, fibrosis and myocardial hypertrophy. It attenuates myocardial hypertrophy and fibrosis, while reversing ventricular remodeling and restoring cardiac function (68,73,81,82). Notably, mTOR signaling has been investigated in studies on heart diseases (83). Furthermore, Mel possesses antioxidant properties and exerts protective effects against various cardiovascular diseases, including diabetic cardiomyopathy and myocardial hypertrophy (28,29). Additionally, studies have demonstrated that activation of macrophage stimulating 1/nuclear factor erythroid 2-related factor 2 signaling and MICU1 could effectively reduce oxidative stress, alleviating myocardial hypertrophy (84,85). In addition to rapamycin and Mel, other ARG-targeted drugs predicted in the present study include wortmannin, deguelin, imatinib, everolimus and rosiglitazone. However, the mechanisms of action of these drugs warrant further investigation.

The present study emphasized the important role of AT in HCM. ARGs associated with the development of HCM were analyzed using bioinformatics tools, and the findings were validated using an external dataset and a cell model of HCM. The present study provides novel insights into the pathological mechanisms of HCM and offers promising avenues for developing therapeutic strategies targeting ARGs.

Despite its important findings, the current study had some limitations that should be acknowledged. First, the sample size should be increased to enhance the reliability of the results, and a more comprehensive prospective study is warranted to validate the results of the present study. Second, clinical samples and animal models are required to verify the functional roles of the identified hub ARGs. Third, validation of the protein expression levels of core ARGs should be added. Lastly, further investigation is required to verify the therapeutic efficacy of the predicted drugs and elucidate the specific mechanisms through which the hub ARGs regulate the development of HCM.

In conclusion, the present study demonstrated that the expression of ARGs was significantly altered in HCM. In particular, EIF4EBP1, MCL1, PIK3R1, CCND1 and PPARG were identified as key ARGs that serve as potential diagnostic markers and therapeutic targets for HCM. Additionally, 10 drugs targeting the key ARGs were identified, which may be used in the ARG-targeted treatment of HCM. In conclusion, the current study improved the understanding of the pathogenesis of HCM and highlighted the potential diagnostic and therapeutic value of ARGs in HCM, providing a crucial theoretical foundation for the development of personalized therapies.

Supplementary Material

Overall protocol of the present study. ARG, autophagy-related gene; DEARG, differentially expressed ARG; DEGs, differentially expressed genes; DIIC, differentially infiltrating immune cell; DSigDB, Drug Signatures Database; GO, Gene Ontology; GSEA, Gene Set Enrichment Analysis; HADb, Human Autophagy Database; KEGG, Kyoto Encyclopedia of Genes and Genomes; PPI, protein-protein interaction.

Sequences of primers used for reverse transcription-quantitative PCR.

Acknowledgements

Not applicable.

Funding

Funding: The present study was supported by the Natural Science Foundation of Jiangxi (grant nos. 20212ACB206011, 20224ACB206002 and 20232BAB206009) and the National Natural Science Foundation of China (grant nos. 82160073 and 81860082).

Availability of data and materials

The data generated in the present study may be requested from the corresponding author.

Authors' contributions

RBQ conducted the cell experiments, and analyzed and mapped the experimental data. STZ contributed to the experimental design and performed data analysis for the bioinformatics analysis. ZWL drafted the article, provided software support and analyzed the data. RYZ, ZCQ, and HZP confirm the authenticity of all the raw data and contributed to data interpretation. LFZ and ZQX contributed to the cell experiments. SQL and LW designed the experiments and provided financial support. All authors read and approved the final version of the manuscript.

Ethics approval and consent to participate

Not applicable.

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

References