The mitochondrial hub gene UCHL1 May serve as a potential biomarker for diagnosing diabetic cardiomyopathy: a comprehensive integration of biological pathways

Abstract

Background

Diabetic cardiomyopathy (DCM) is a complex clinical syndrome characterized by cardiac systolic and diastolic dysfunction. Research on the underlying mechanism of mitochondrial dysfunction and the involved genes in patients with DCM is limited.

Objective

We aimed to explore the hub genes and pathways related to mitochondrial dysfunction that affect the progression of DCM.

Methods

DCM patient datasets (GSE161052, GSE210611 (test sets) and GSE26887 (validation set) were downloaded from the Gene Expression Omnibus (GEO) database. The identification of the differentially expressed genes (DEGs) was performed using the “limma” R package. Mitochondrial dysfunction-related genes (MDRGs) associated with DCM were obtained from the Molecular Signatures Database (MSigDB). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were carried out to analyse the biological function of mitochondrial dysfunction-related differentially expressed genes (MDRDEGs) via the “ClusterProfiler”, “DOSE”, “org.Hs.eg.db” and “circlize” R packages. The diagnostic value of the hub genes for DCM was confirmed using receiver operating characteristic (ROC) curves in the test and validation groups. Moreover, the functions of the hub genes in the context of DCM were explored via gene set enrichment analysis (GSEA). A protein‒protein interaction (PPI) network of the hub genes was constructed using the GeneMANIA database. Finally, real-time reverse transcription polymerase chain reaction (real-time RT PCR) analysis and western blot analysis were performed to detect the expression levels of UCHL1.

Results

A total of 705 DEGs and 122 MDRGs closely related to DCM were identified, and 6 MDRDEGs (AGT, KIT, SLC2A1, SLC2A4, TK2, and UCHL1) were obtained and subjected to GO and KEGG enrichment analyses. ROC curve analysis was performed for the test and validation groups. Only the AUC of UCHL1 reached 1.0 in both the test and validation groups, and UCHL1 was identified as a hub gene in DCM. GSEA revealed that multiple biological pathways were activated or inhibited along with alterations in the expression of UCHL1. PPI network analysis revealed that the hub genes interacted with mainly the ASPSCR1, PTPRU, STXBP3, SOCS6 and UCHL5 proteins. There was a reciprocal regulatory relationship between UCHL1 expression and hsa-miR-181a-5p, hsa-miR-193b-3p, hsa-miR-877-5p and hsa-miR-218-5p levels. Finally, real-time RT PCR and western blot analysis revealed that UCHL1 may be used as a potential diagnostic biomarker of DCM.

Conclusions

In this study, 6 mitochondrial dysfunction-related hub genes related to DCM were identified. The mitochondrial hub gene UCHL1 was demonstrated to be a potential diagnostic biomarker for DCM.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-025-02199-0.

Introduction

Globally, there were 463 million diabetic patients in 2019, and this number is predicted to increase to 578 million by 2030 and 700 million by 2045; the number of diabetic patients has doubled over the past few decades [1]. Diabetic cardiomyopathy (DCM) refers to cardiac dysfunction in diabetic individuals in the absence of coronary artery disease (CAD), hypertension, valvular disease, congenital heart disease, or any other known cardiac disorders [2]. Heart failure with preserved ejection (HFpEF) is a category of heart failure characterized by a normal ejection fraction with a systemic pathophysiological change. While heart failure with reduced ejection fraction (HFrEF) is a phenotype of heart failure with a significant reduction in ejection fraction having a worse clinical outcome. Some specialists consider DCM to progress from HFpEF with cardiac hypertrophy and diastolic dysfunction to HFrEF with further structural damage and systolic impairment, which substantially decreases patient quality of life [3]. DCM includes complex mechanisms covering but not limited to glucose toxicity, lipotoxicity, Ca2 + imbalance, oxidative stress, advanced glycation end products (AGEs), inflammation, cell apoptosis and necrosis, mitochondrial damage, etc [4]. Although researchers have recently proposed that sodium‒glucose cotransporter 2 inhibitors (SGLT2is) are highly beneficial for DCM patients [5, 6], the prognosis of DCM patients remains poor, and the mechanisms of DCM are still being investigated.

Mitochondria are vital for diverse cellular processes. Mitochondrial dysfunction refers to structural and functional abnormalities in mitochondria caused by the accumulation of reactive oxygen species (ROS) in cells frequently generated under ischaemic and hypoxic conditions [7]. Damaged mitochondria or mitochondrial dynamics often occurs in cardiomyocytes because they are hypermetabolic and have a long lifespan [8]. Mitochondrial dysfunction can aggravate DCM by accelerating oxidative stress, dysregulating calcium homeostasis, metabolic reprogramming, abnormal intracellular signalling and mitochondrial apoptosis in cardiomyocytes [9]. Several interventions modulating mitochondrial physiology have reached the goal of abrogating the DCM phenotype, and targeting optic atrophy 1 (OPA1) and mitofusin 2 (Mfn2) could be considered pharmacological strategies [10, 11]. Some researchers have reported that exercise can induce the upregulation of cardiac β-klotho and render cardiomyocytes sensitive to fibroblast growth factor 21 (FGF21), which ameliorates mitochondrial dysfunction, oxidative stress and fibrosis to protect against DCM [12]. Xie et al. [13] reported that ubiquitin-specific protease 28 (USP28) regulated mitochondrial homeostasis through the peroxisome proliferator-activated receptor α-mitofusin 2 (PPARα-Mfn2) to improve cardiac function and alleviate cardiac hypertrophy and fibrosis in diabetic hearts.

At present, the conventional treatment for DCM mainly focuses on optimizing blood glucose control, reducing blood lipid levels and reducing oxidative stress, but effective targeted treatments for the key pathological link of mitochondrial damage are lacking. Identifying relevant genes could help to bridge this therapeutic gap by developing drugs or therapies that can directly improve mitochondrial function and repair mitochondrial damage.This study aimed to identify potentially differentially expressed genes associated with mitochondrial dysfunction in DCM patients. These genes may be involved in the regulation of DCM and mitochondrial dysfunction, with high diagnostic value, and could be new targets for subsequent treatment.

Materials and methods

Data source

Three DCM RNA-sequencing datasets, GSE161052, GSE210611 and GSE26887, were downloaded from the Gene Expression Omnibus (GEO) database of NCBI (http://www.ncbi.nlm.nih.gov/) using the R package GEOquery. The GSE161052 dataset contains data from 3 heart biopsy samples from streptozotocin (STZ)- treated mice and 3 perfectly matched heart samples from normal mice. The GSE210611 dataset also contains data from 3 heart biopsy samples from STZ - treated mice and 3 perfectly matched heart samples from normal mouse heart samples. The GSE26887 dataset contains data from 7 heart biopsy samples from patients with type 2 diabetes affected by postischemic heart failure and 5 partially matched heart samples from normal heart samples. Although the GSE26887 dataset presents data mainly pertaining to postischemic heart failure, the gene expression data provide important information for understanding mitochondrial dysfunction. By combining data from both mice and clinical patients, we were able to explore the mechanisms of mitochondrial dysfunction in DCM fully. The GSE161052 and GSE210611 datasets served as test sets, whereas the GSE26887 dataset served as the validation set.

Three reference gene datasets related to mitochondrial dysfunction were obtained from the Molecular Signatures Database (MSigDB), among which 113 genes were obtained by homologous conversion and deduplication in mice and humans (Supplemental Material 1). In addition, 11 genes related to mitochondrial dysfunction were obtained from the relevant literature (Supplemental Material2). Mitochondrial dysfunction-related genes (MDRGs) from the 2 sources were merged and deduplicated to obtain 122 genes for analysis in this study.

Identification of differentially expressed genes

The expression matrices were divided into disease and control groups and screened for differentially expressed genes (DEGs). The identification of DEGs between DCM tissue samples and normal tissue samples was performed by using the “limma” R package [14]. The cut-off criteria were as follows:|logFC| >0 and adjusted p value < 0.05 [15]. DEGs meeting the criteria were selected for further analysis.

Gene ontology and kyoto encyclopedia of genes and genomes analyses of the hub genes

The DEGs and MDRGs were compared and the shared genes were obtained. To elucidate the role of these shared hub genes in DCM, the specific mechanisms, functions and pathways these genes are involved in were explored and annotated. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of the DEGs and MDRGs were implemented using the R packages “ClusterProfiler”, “DOSE”, “org.Hs.eg.db” and “circlize” [16–19]. An adjusted p value < 0.05 was considered statistically significant.

Receiver operating characteristic curve analysis

A receiver operating characteristic (ROC) curve was subsequently constructed to assess the sensitivity and specificity of the hub genes for diagnosing DCM. We calculated the area under the curve (AUC) of each hub gene in the training group and validation group using the R packages “glmnet” and “pROC” [20, 21]. A gene with an AUC above 0.95 was considered a hub gene with the potential capacity for the diagnosis of DCM.

Gene set enrichment analysis

Gene set enrichment analysis (GSEA) was implemented to further explore the hub genes that play a vital role in the pathogenesis of DCM. We systematically annotated the functions and pathways enriched with these hub genes on the basis of DCM gene expression profile data using the R packages “ClusterProfiler”, “DOSE” and “org.Hs.eg.db” [16–18]. An adjusted p value < 0.05 was considered statistically significant. We identified the biological pathways closely related to the pathogenesis of DCM and further elucidated the important roles of these pathways in the pathophysiological process of DCM, providing important clues for revealing the pathogenesis of DCM.

Construction of the protein‒protein interaction regulatory network

To further explore the coexpression relationships between the hub genes and proteins, we conducted a comprehensive protein‒protein interaction (PPI) network analysis. First, we constructed and analysed the PPI network of the hub genes using GeneMANIA (http://genemania.org/), thereby revealing the potential interactions and functional associations of these genes at the protein level. Next, to predict the miRNAs associated with these hub gene regulatory effects, we used the MIRTARBASE database (https://mirtarbase.cuhk.edu.cn/) to identify possible miRNA regulatory relationships. In addition, we used KnockTF2.0 (https://bio.liclab.net/KnockTFv2/index.php) to predict further relationships between the hub genes and transcription factors (TFs). This analysis revealed the important role of these genes in transcriptional regulatory networks and their complex interactions with TFs.

Diabetic cardiomyopathy models and echocardiography

Eight-week-old male C57BL/6J mice (n = 16; STZ mice, n = 8; control mice, n = 8) and db/db mice (n = 8) were procured from Shanghai Jihui Biotechnology Co., Ltd. and Hangzhou Ziyuan Biotechnology Co., Ltd., respectively. All the mice were housed under specific pathogen-free (SPF) conditions in an accredited animal facility. To establish a diabetes mellitus model, C57BL/6J mice (n = 8) were subjected to five consecutive daily intraperitoneal injections of streptozotocin (STZ; 50 mg/kg body weight; Sigma–Aldrich, St. Louis, MO, USA) dissolved in 0.1 M cold sodium citrate buffer (pH 4.5). Following STZ treatment, the mice were fed a high-fat diet. Hyperglycaemia was confirmed by measuring blood glucose levels in the tail vein, and mice with fasting blood glucose levels exceeding 11.1 mmol/L were considered to have successfully developed diabetes. The protocol for this research was approved by the Animal Exprimental Ethical Inspection Committee of Shanghai Chedun Experimental Animal Ethics Committee (approval number: AD20220311). All animal experiments were performed in accordance with the guidelines of the National Institute of Health Guide for the Care and Use of Laboratory Animals (NIH publication 8023, revised 1996) under the approval of the Shanghai Chedun Experimental Animal Ethics Committee.

All surviving mice (db/db mice, n = 8; STZ mice, n = 8; control mice, n = 8) were subjected to follow-up echocardiography. The animals were lightly anaesthetized with isoflurane (1.5%). Cardiac function testing was performed on C57BL/6J mice after 8 weeks of treatment and on 24-week-old db/db mice that were fed normally via the Vevo 2100 ultrasound system (VisualSonics Inc.). Left ventricular end-diastolic dimension (LVEDD) and left ventricular end-systolic dimension (LVESD) were measured. The left ventricular ejection fraction (LVEF) and left ventricular fraction shortening (FS) were calculated. FS was calculated using the following formula: FS=[(LVEDD-LVESD)/(LVEDD)] %. After the measurements, the mice were euthanized via cervical dislocation after isoflurane inhalation (concentration of 3%) in a closed chamber, and heart samples were collected for follow-up studies.

Histological analysis, immunohistochemistry analysis, real-time reverse transcription polymerase chain reaction and western blot analysis of the hub genes

The heart was fixed in 4% paraformaldehyde solution for 24 hours and then embedded in paraffin. The Sect. (5 µm-thick) were subjected to haematoxylin and eosin (HE) and Masson’s trichrome staining using the standard procedure [22, 23]. Immunohistochemical staining was performed as described previously [23]. The monoclonal antibody UCHL1 (1:100 dilution) was used as the primary antibody. Positive staining was detected using 3,3’ -diaminobenzidine (DAB), and the sections were counterstained with haematoxylin before mounting with a coverslip for microscopic analysis. Quantitative analysis of the positive staining was performed using imageJ software to determine the percentage of positive cells in the tissue sections.

Real-time reverse transcription polymerase chain reaction (real-time RT PCR) was conducted with RealStar Fast SYBR Qpcr Mix (GenStar, #A304). Primers for this study are as follows:

Gene	Sequence (5’-3’)
Mouse-β-actin-Forward	GTGACGTTGACATCCGTAAAGA
Mouse-β-actin-Reverse	GCCGGACTCATCGTACTCC
Mouse-UCHL1-Forward	AGGGACAGGAAGTTAGCCCTA
Mouse-UCHL1-Reverse	AGCTTCTCCGTTTCAGACAGA
Mouse-TK-2-Forward	AGCAGTGGTTTGTATTGAGGG
Mouse-TK-2-Reverse	ACATGAGGCTCAGAGGGTTATG
Mouse-SLC2A4-Forward	ACACTGGTCCTAGCTGTATTCT
Mouse-SLC2A4-Reverse	CCAGCCACGTTGCATTGTA
Mouse-SLC2A1-Forward	TCAAACATGGAACCACCGCTA
Mouse-SLC2A1-Reverse	AAGAGGCCGACAGAGAAGGAA
Mouse-KIT-Forward	GGCCTCACGAGTTCTATTTACG
Mouse-KIT-Reverse	GGGGAGAGATTTCCCATCACAC
Mouse-AGT-Forward	CAGGTCGCAATGATCGCCA
Mouse-AGT-Reverse	GTGTCCATCTAGTCGGGAGGT

The reactions were heated to 95℃ for 2 min, and the reaction was repeated for 3 times, with 40 cycles of 95℃ for 15 s, 60℃ for 30 s, and 95℃ for 1 s. Heart tissues were lysed with radioimmunoprecipitation assay lysis buffer (Solarbio, Beijing, China). Equal amounts of protein (50 to 60 µg) were separated via SDS‒polyacrylamide gel electrophoresis (SDS‒PAGE), transferred to polyvinylidene difluoride membranes, and incubated with primary antibodies as indicated for each experiment, followed by incubation with horseradish peroxidase-conjugated secondary antibodies (1:5000) for 2 h at 37 °C. All the blots were developed using a chemiluminescent system, and the signal intensities were analysed with a Gel-Pro 4.5 Analyser (Media Cybernetics, Rockville, MD, USA).

Statistical analysis

The data are presented as the means ± standard deviations. The data were analysed by ANOVA with a post hoc Tukey test for comparisons of multiple groups and were considered significant if P < 0.05. The statistical analyses of the bioinformatics data were performed using R (4.1.3). The statistical analyses of the animal studies were performed using SPSS version 23.0 software and GraphPad Prism 9.0 software.

Results

Collection and correction of the datasets

To obtain the combined GEO dataset, the R package sva was used to remove batch effects on the combined datasets GSE161052 and GSE210611 [24]. The datasets before and after batch effect removal were compared by distribution box plots and principal component analysis (PCA) plots (Fig. 1). The results of the distribution box plots revealed that the batch effect of samples in the combined dataset was eliminated after batch removal.

Fig. 1.

The collection and correction of the datasets. A Boxplot for the combined dataset before batch removal. B The PCA of the dataset before batch removal. C The box plot for combined dataset after batch removal. D The PCA of the combined dataset after batch removal. DCM diabetic cardiomyopathy, PCA principle component analysis

Analysis of genes related to mitochondrial dysfunction

To obtain mitochondrial dysfunction-related differentially expressed genes (MDRDEGs), the combined datasets were divided into a DCM group and a normal group. The differentially expressed genes in the 2 groups were analysed using the “limma” R package [14]. The data were filtered using|logFC| >0 and adjusted P value < 0.05. A total of 705 DEGs (DCM vs. normal) were identified, including 372 upregulated genes and 333 downregulated genes. A volcano plot was drawn according to the difference analysis results of the combined datasets (Fig. 2A). Six MDRDEGs were obtained by intersections of all the obtained DEGs and MDRGs. A total of 6 MDRDEGs were obtained, and a Venn diagram was drawn (Fig. 2B). According to the intersection results, the differences in the expression of MDRDEGs between the DCM and normal groups in the combined datasets were analysed, and heatmaps were drawn (Fig. 2C). Spearman correlation analysis was used to calculate the correlation coefficients between different molecules, and correlation heatmaps were drawn (Fig. 2D).

Fig. 2.

The analysis of differentially expressed genes associated with mitochondrial dysfunction. A A volcano plot was drawn according to the difference analysis result of the combined dataset. B A Venn diagram of DEGs and MDRGs. C The differences in the expression of MDRDEGs between the DCM and normal groups in the combined dataset were analysed, and heatmap was drawn. D MDRDEGs correlation heatmap. DEGs differentially expressed gene, MDRGs mitochondrial dysfunction-related genes, MDRDEGs mitochondrial dysfuction-related differentially expressed genes, DCM diabetic cardiomyopathy

The expression, function and enrichment pathway analyses of the hub genes in DCM

The expression levels of these 6 MDRDEGs were divided into DCM and normal groups (Fig. 3A) (a p value < 0.05 was considered statistically significant). The results revealed significant differences among the 6 MDRDEGs (AGT, KIT, SLC2A1, SLC2A4, TK2, and UCHL1) in the combined datasets (P value < 0.05). Moreover, the 6 MDRDEGs were converted into human‒mouse homology, and a comparison diagram of the GSE26887 dataset was drawn (Fig. 3B). The results revealed that UCHL1 expression was significantly different between the combined dataset and the GSE26887 dataset and that the expression trend was consistent (P value < 0.05).

Fig. 3.

The analysis of hub gene expressed and functional enrichment in DCM. A There were significant differnces among the 6 MDRDEs (AGT, KIT, SLC2A1, SLC2A4, TK2, and UCHL1) in the combined dataset (P value < 0.05). B The 6 MDRDEGs were converted into human-mouse homology, and a comparison diagram of the GSE26887 dataset was drawn. C GO enrichment analysis of hub gene in diabetic cardiomyopathy. Blue biological process. Red cellular component. Green molecular function. D KEEG enrichment analysis of the hub gene in DCM. DCM diabetic cardiomyopathy, GO Gene ontology, KEGG Kyoto encyclopedia of genes and genomes

In addition, GO enrichment analysis revealed that the hub genes were significantly involved in the biological process (BP) terms regulation of mitogen-activated protein kinase (MAPK), serine/threonine kinase activity and vascular processes in the circulatory system. In cell components (CCs) category, the hub genes were closely related to the structures of the myolemma, blood particles and membrane microdomains. In the molecular function (MF) category, these genes were significantly involved in D-glucose transmembrane transport activity, glucose transmembrane transport activity and hexose transmembrane transport activity (Fig. 3C). KEGG pathway analysis revealed that the significant expression of Hub genes in dilated cardiomyopathy was related to insulin resistance, DCM and the adipocytokine signalling pathway (Fig. 3D). These findings provide important clues for further understanding the roles of hub genes in the development of diseases.

Diagnostic ability of the hub genes in the training and validation datasets

To assess the potential capacity of each hub gene in DCM diagnosis, ROC curve analysis was performed using the training and validation datasets. In the training dataset, the AUC values of UCHL1, KIT, TK2, SLC2A4 and SLC2A1 was 1.0 (Fig. 4A–E), whereas the AUC value of AGT was 0.972 (Fig. 4F). The results revealed that UCHL1, KIT, TK2, SLC2A4 and SLC2A1 had excellent performance in the diagnosis of DCM. In the validation dataset, the AUC value of UCHL1 also reached 1.0 (Fig. 5A), while the AUC values of the other 5 genes were lower than 0.95 (Fig. 5B–F). These findings suggested that although these genes performed exceptionally well in the training group, their reliability declined in the validation group, except for UCHL1. We finally identified UCHL1 as the hub gene of DCM. This study not only provides a new biomarker for the early diagnosis of DCM but also lays the foundation for subsequent clinical studies and the development of potential therapeutic strategies.

Fig. 4.

The RCO curve analysis of the 6 hub genes in the test group. A The AUC of UCHL1 reached 1. B The AUC of TK2 reached 1. C The AUC of KIT reached 1. D The AUC of SLC2A1 reached 1. E The AUC of SLC2A4 reached 1. F The AUC of AGT reached 0.972. ROC receiver operating characteristic, AUC area under the curve, UCHL1 ubiquitin c-terminal hydrolase 1, TK2 thymidine kinase, SLC2A1 solute carrier family 2 member 1, SLC2A4 solute carrier family 2 member 4, AGT angiotensinogen

Fig. 5.

The ROC curve analysis of 6 hub genes in the validation group. A The AUC of UCHL1 reached 1. B The AUC of TK2 reached 0.714. C The AUC of KIT reached 0.914. D The AUC of SLC2A1 reached 0.80. E The AUC of SLC2A4 reached 0.686. F The AUC of AGT reached 0714. ROC receiver operating characteristic, AUC area under the curve, UCHL1 ubiquitin c-terminal hydrolase 1, TK2 thymidine kinase, SLC2A1 solute carrier family 2 member 1, SLC2A4 solute carrier family 2 member 4, AGT angiotensinogen

Gene set enrichment analysis

To further explore the potential regulatory pathways of the hub gene UCHL1, we performed GSEA for UCHL1 in the training dataset. The results revealed that in the UCHL1 high-expression group, multiple biological pathways, including extracellular matrix (ECM) receptor interactions, melanoma and neuroactive ligand‒receptor interactions, were activated (Fig. 6A). The activity of these pathways indicated that UCHL1 possibly plays a key role in cell migration, proliferation and information transduction, suggesting that UCHL1 may promote the progression of DCM through these pathways.

Fig. 6.

GSEA of the hub gene. A GSEA in the UCHL1 high expression group showed significant correlation with extracellular matrix receptor interactions, melanoma and neuroactive ligand-receptor interactions and other pathways. B GSEA in the UCHL1 low expression group showed significant correlation with B cell receptor signaling pathways, cytokine-cytokine receptor interactions and primary immunodeficiency pathways. GSEA Gene set enrichment analysis. UCHL1 ubiquitin c-terminal c-hydrolase 1

In contrast, in the UCHL1 low-expression group, we observed significant inhibition of B-cell receptor signalling pathways, cytokine‒cytokine receptor interactions, and primary immunodeficiency pathways (Fig. 6B). This phenomenon suggested that low expression of UCHL1 may lead to weakening of the immune response and inhibit signalling between cells, thus affecting immune surveillance and the response ability of the body. These findings provide important clues for further understanding the biological function of UCHL1 in DCM, especially its mechanism in cardiac pathophysiological changes. Future studies could focus on these activated and inhibited pathways to explore how to improve outcomes in DCM patients by modulating the expression of UCHL1 and its associated pathways.

Protein‒protein interaction network and transcription factor regulation of the hub genes

We conducted a protein–protein interaction (PPI) network analysis of the 6 hub genes, and the results revealed that these genes interacted with mainly the ASPSCR1, PTPRU, STXBP3, SOCS6 and UCHL5 proteins (Fig. 7A). We used the MIRTARBASE database and found that there was a reciprocal regulatory relationship between UCHL1 and hsa-miR-181a-5p, hsa-miR-193b-3p, hsa-miR-877-5p and hsa-miR-218-5p (Fig. 7B). In addition, to further explore the regulatory network between UCHL1 and TFs, we obtained 126 TFs that have regulatory relationships with UCHL1 through the KnockTF2.0 database, including 74 upregulated and 52 downregulated TFs (Supplemental Material 3) (Fig. 7C). Figure 7D shows the regulatory relationships of the PPIs between UCHL1 and these TFs. These analyses revealed the important impact of UCHL1 on the protein interaction network and the complex regulatory relationships among UCHL1, miRNAs and TFs. These findings also provide vital clues for further study of the function and related regulatory mechanisms of UCHL1.

Fig. 7.

Hub gene protein interaction regulatory network. A A PPI network among the hub gene and protein based on the GeneMANIA dataset (ASPSCR1, PTPRU, STXBP3, SOCS6 and UCHL5). B UCHL1 and microRNAs regulatory network. Yellow microRNAs. Blue gene. C UCHL1 and TFs network. Red TF. D Regulatory relationship of the PPIs between UCHL1 and TFs. PPI protein-protein interactions, ASPSCR1 alveolar soft part sarcoma chromosomal region candidate 1, PTPRU protein tyrosine phosphatase receptor U, STXBP3 syntaxin binding protein 3, SOCS6 suppressor of cytokine signaling 6, UCHL5 ubiquitin c-terminal hydrolase 5, UCHL1 ubiquitin c-terminal hydrolase 1, TF transcription factor

Validation of the protein levels of the ultimately hub genes between DCM group hearts and control group hearts via real-time RT PCR and western blotting

We constructed DCM models using STZ and db/db mice. The echocardiogram revealed greater LVESD and lower LVEF and FS in the STZ-treated and db/db mice than in the control mice (Table 1). HE and Masson staining revealed myocardial fibrosis in STZ and db/db mouse heart tissue compared with control mouse heart tissue (Fig. 8A). The immunohistochemistry (IHC) results revealed that the expression levels of UCHL1 was significantly greater in the hearts of the STZ, db/db than that in the control mice (Fig. 8A, E). Further real-time RT PCR showed 6 MDRDEGs (AGT, KIT, SLC2A1, SLC2A4, TK-2 and UCHL1) mRNA levels in control, STZ-treated and db/db mice, among which UCHL1 mRNA was significantly greater in the hearts of the STZ, db/db than that in the control mice (Fig. 8B).Finally, we compared the expression levels of UCHL1 between DCM and control samples by western blotting. Western blot analysis revealed that the expression level of UCHL1 was significantly greater in the myocardia of the STZ-treated and db/db mice than in that of the control mice (P < 0.01) (Fig. 8C andD). Overall, these results suggest that increased UCHL1 expression may play a critical role in regulating cardiac systolic and diastolic functions.

Table 1.

Echocardiographic parameters of Ctrl group, STZ group and db/db group

Parameters	Ctrl group (n = 8)	STZ group (n = 8)	db/db group (n = 8)
LVESD, mm	1.9 ± 0.2	2.3 ± 0.2*	2.6 ± 0.2*#
LVEDD, mm	3.4 ± 0.3	3.6 ± 0.2	3.6 ± 0.2
LVEF, %	77 ± 3	69 ± 2*	55 ± 2*#
FS, %	45 ± 3	38 ± 1*	28 ± 2*#

*P < 0.05 compared with Ctrl goup. #P < 0.05 compared with STZ group

Ctrl contro, STZ Streptozocin, db/db Leptin receptor gene mutation mice, LVESD left ventricular end systolic diameter, LVEDD left ventricular end diastolic diameter. LVEF left ventricular ejection fraction. FS fraction shortening

Fig. 8.

UCHL1 is upregulated in STZ and db/db hearts. A Representative hematein eosin (HE) staining and immunohistochemical (IHC) staining of UCHL1 proteins in the heart tissues from Ctrl, STZ and db/db mice. B Real-time RT-PCR analysis of AGT, KIT, SLC2A1, SLC2A4, TK-2 and UCHL1 mRNA levels in Ctrl (n = 6), STZ (n = 6) and db/db (n = 6) mice. C and D representative western blotting analysis of UCHL1 in the heart tissues from Ctrl, STZ and db/db mice. The data were normalized to the GAPDH expression. E Quantitative analysis of the positive staining in the heart tissues from Ctrl, STZ and db/db mice. *P < 0.05, **P < 0.01, ***P < 0.001. Ctrl control, STZ streptozocin, db/db Leptin receptor gene mutation mice

Discussion

DCM is a complex clinical syndrome with high morbidity and mortality rates worldwide and requires sustained treatment, which imposes considerable burdens on patient quality of life. Mitochondrial dysfunction is a common pathogenesis of DCM. Owing to its sophisticated mechanism, early recognition, timely inhibition and treatment of mitochondrial dysfunction remain to be studied. Effective therapeutic drugs and their corresponding targets need to be focused on and discovered. Hence, we explored mitochondrial dysfunction-related biomarkers, which may be therapeutic targets for DCM, in DCM to investigate their role in disease pathogenesis and clinical diagnosis.

In this study, 6 MDRDEGs (AGT, KIT, SLC2A1, SLC2A4, TK2, and UCHL1) between the DCM group and the normal group were identified. Angiotensinogen (Agt) is a unique precursor of all angiotensin peptides. Myocardium overexpression of Agt can mediate cardiac remodelling, leading to age-dependent myocardial dysfunction and failure [25]. Recent studies have focused more on the impact of Agt on insulin and lipid metabolism. It has been reported that loss of Agt in hepatocytes or adipocytes can ameliorate insulin tolerance [26, 27]. Kit, a type III tyrosine kinase receptor, after binding to its ligand, activates signalling cascades associated with processes such as proliferation, differentiation, migration and cell survival. Kit plays an important role in the management of haematopoiesis, gametogenesis and melanogenesis [28]. Solute carrier family 2 member 1 (Slc2a1) is known as glucose transporter 1 (GLUT1) [29]. Therefore, the overexpression of Slc2a1 promotes glycolysis and cell proliferation in various types of cancer [30–32]. Solute carrier family 2 member 4 (Slc2a4), known as glucose transporter 4 (GLUT4), reportedly regulates the progression of obesity and insulin resistance in humans [33]. Thymidine kinase (TK2) is reported to have a significant effect on mitochondrial DNA synthesis and maintenance [34]. The pathways and biological functions of the hub genes identified in the above studies were consistent with the results of the GO and KEGG analyses. The diagnostic ability of the hub genes in the training and validation groups was evaluated via ROC curve analysis. Although these genes performed exceptionally well in the training group, their reliability decreased in the validation group, except for ubiquitin C-terminal hydrolase 1 (UCHL1). We finally identified UCHL1 as the hub gene of DCM.

We explored the expression levels of UCHL1 in DCM model mice and control mice. The real-time RT PCR analysis results, western blot analysis results and the quantitative analysis of the positive staining results were consistent with the intersection results, which indicated that the expression of UCHL1 was significantly greater in the myocardia of the STZ-treated and db/db mice than in that of the control mice. Ubiquitination and deubiquitination are representative posttranslational modifications of cell proteins involving the regulation of both membrane trafficking and protein degradation [35]. UCHL1 is a deubiquitinating enzyme that removes ubiquitin or polyubiquitin from target proteins [36, 37]. At present, UCHL1 plays critical roles in cancer, neurodegenerative diseases and cell senescence [38–40]. In addition, UCHL1 expression was found to be highly upregulated in cardiomyocytes after myocardial infarction and was associated with increased ubiquitin expression [41]. UCHL1 was verified to play a novel protective role against myocardial infarction by stabilizing hypoxia-inducible factor 1 (HIF-1α) and promoting HIF-1α signalling [42]. In DCM, HIF-1α may affect energy metabolism of caardiomyocytes by regulating the expression of genes related to glycolysis and oxidative stress. UCHl1 stabilized HIF-1α through deubiquitination and may enhances its activity in DCM, thereby regulating metabolic adaption and gypoxia response of cardiomyocytes and influencing disease progression. It has also been suggested that UCHL1 promoted angiotensin II-induced fibrotic responses by activating nuclear factor kappa B (NF-κB). Moreover, inhibition of the NF-κB pathway interfered with UCHL1 overexpression-mediated fibrotic responses [43]. In DCM, hyperglycemia and lipid toxicity can activate NF-κB, leading to cardiomyocyte inflammation and fibrosis. UCHL1 may influence the intensity of inflammatory response by regulating the NF-κB signaling pathway. Recently, UCHL1 was revealed to alleviate cardiac hypertrophy by attenuating epidermal growth factor receptor (EGFR) ubiquitination and degradation [44]. These findings provide important clues for further understanding the biological role of UCHL1 in DCM and the mechanism by which it induces cardiac pathophysiological changes. Interestingly, our GSEA results revealed that alterations in UCHL1 expression may be associated with cell migration, proliferation, signal transduction and the immune response. Future study can be conducted in vitro and in vivo in accordance with these fields. This study has several limitations. First, the datasets downloaded from GEO was not abundant. Second, although we verified the difference in UCHL1 expression between DCM and control samples by western blot analysis and the results were consistent with the results of the bioinformatics analysis, the hub genes screened through bioinformatics were only briefly verified via mouse experiments. Owing to time and resource constraints, more efforts should be invested in our future studies focusing on activated and inhibited pathways to explore how to improve outcomes in DCM patients by modulating the expression of UCHL1 and its associated pathways. Third, the sample size of this study was relatively small (n = 8 per group), which affected the statistical significance of the results and increased the risk of false-positive rates. The effect of a small sample size was particularly significant when the broad threshold of|logFC|>0 was used. However, selecting a lower LogFC threshold can provide us with a broader perspective, especially when complex disease mechanisms are involved. In our future studies, we will consider adjusting the threshold range on the basis of a larger sample size to improve the robustness of the results. Fourth, although we have controlled some confounding factors, the characteristics of the GSE26887 dataset may differ from the pathological mechanisms of DCM, which may introduce other confounding factors that were not considered and affect the results. Therefore, these potential confounding factors need to be carefully considered when interpreting our findings. However, we believe that the GSE26887 dataset provides some important biological information for exploring genes associated with mitochondrial dysfunction. Future studies can validate and extend our findings by including more sample datasets specific to DCM, thereby enhancing the reliability and generalisability of the results.

Conclusions

We conducted an integrated analysis using bioinformatics data to explore the hub genes associated with DCM and mitochondrial dysfunction. Six hub genes (AGT, KIT, SLC2A1, SLC2A4, TK2, and UCHL1) related to DCM and mitochondrial dysfunction were identified. We further explored whether UCHL1 may serve as a new diagnostic and therapeutic target for DCM patients and provide new insights into the pathogenesis of mitochondrial dysfunction in DCM patients. In the future, we will further explore the potential mechanisms of UCHL1 through in vitro and in vivo experiments.

Supplementary Information

Below is the link to the electronic supplementary material.

Abbreviations

DCM

Diabetic cardiomyopathy

GEO

Gene expression omnibus

DEGs

Differentially expressed genes

MDRGs

Mitochondrial dysfunction-related genes

MSigDB

Molecular Signatures database

GO

Gene Ontology

KEGG

Kyoto Encyclopedia of Genes and Genomes

MDRDEGs

Mitochondrial dysfunction-related differentially expressed genes

ROC

Receiver operating characteristic

GSEA

Gene set enrichment analysis

PPI

Protein‒protein interaction

CAD

Coronary artery disease

HFpEF

Heart failure with preserved ejection fraction

HFrEF

Heart failure with reduced ejection fraction

AGEs

Advanced glycation end products

SGLT2i

Sodium‒glucose cotransporter 2 inhibitor

ROS

Reactive oxygen species

AUC

Area under the curve

UCHL1

Ubiquitin c-terminal hydrolase 1

TK2

Thymidine kinase

SLC2A1

Solute carrier family 2 member 1

SLC2A4

Solute carrier family 2 member 4

AGT

Angiotensinogen

ASPSCR1

Alveolar soft part sarcoma chromosomal region candidate 1

PTPRU

Protein tyrosine phosphatase receptor U

STXBP3

Syntaxin binding protein 3

SOCS6

Suppressor of cytokine signaling 6

UCHL5

Ubiquitin c-terminal hydrolase 5

TF

Transcription factor

SPF

Specific pathogen-free

LVEDD

Left ventricular end-diastolic dimensions

LVESD

Left ventricular end-systolic dimensions

LVEF

Left ventricular ejection fraction

FS

Fraction shortening

Real-time RT PCR

Real-time reverse transcription polymerase chain reaction

PCA

Principal component analysis

Author contributions

GCJ: Funding acquisition, Conceptualization, Validation, and Writing—original draft; TYJ: Funding acquisition, Conceptualization, Validation, and Writing—original draft; QD: Data curation, Formal analysis, Methodology, Editing; ZYF: Funding acquisition, Supervision, review & editing. All authors read and approved the final version of the manuscript.

Funding

This study was supported by grants from the Hospital-level exploratory clinical research projects in Shanghai Sixth People’s Hospital (ynts 202107), Changshu Health Commission Science and Technology Program of 2022 (CSWSQ202203), Changshu Science and Technology Program of 2023 (CY202301), National Natural Science Foundation of China (81873486), Science and Technology Development Program of Jiangsu Province-Clinical Frontier Technology (BE2022754), Clinical Medicine Expert Team (Class A) of Jinji Lake Health Talents Program of Suzhou Industrial Park (SZYQTD202102), Suzhou Key Discipline for Medicine (SZXK202129), Demonstration of Scientific and Technological Innovation Project (SKY2021002), Suzhou Dedicated Project on Diagnosis and Treatment Technology of Major Diseases (LCZX202132), Research on Collaborative Innovation of Medical Engineering Combinations (SZM2021014), Research on Collaborative Innovation of Medical Engineering Combinations (SZM2022003), Suzhou Key Laboratory of Diagnosis and Treatment of Panvascular Diseases (SZS2023021).

Data availability

Publicly available datasets were analysed in this study. These data can be found in the GEO database (http://www.ncbi.nlm.nih.gov/).

Declarations

Ethical approval and consent to participate

The protocol for this research was approved by the Shanghai Chedun Experimental Animal Ethics Committee (approval number: AD20220311). All animal experiments were performed in accordance with the guidelines of the National Institute of Health Guide for the Care and Use of Laboratory Animals (NIH publication 8023, revised 1996) under the approval of the Shanghai Chedun Experimental Animal Ethics Committee.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.