Down-regulation of HSPB1 and MGST1 promote ferroptosis and impact immune infiltration in diabetic cardiomyopathy

Down-regulation of HSPB1 and MGST1 promote ferroptosis and impact immune infiltration in diabetic cardiomyopathy | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Down-regulation of HSPB1 and MGST1 promote ferroptosis and impact immune infiltration in diabetic cardiomyopathy Yaoli Xie, Bin Liang, Zhijun Meng, Rui Guo, Caihong Liu, Yi Yuan, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5153598/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Mar, 2025 Read the published version in Cardiovascular Toxicology → Version 1 posted 4 You are reading this latest preprint version Abstract Diabetic cardiomyopathy (DCM) is a leading cause of death in diabetic patients. Current therapies do not adequately resolve this problem and focus only on the optimal level of blood glucose for patients. Ferroptosis plays an important role in diabetes mellitus and cardiovascular diseases. However, the role of ferroptosis in DCM remains unclear. Differentially expressed ferroptosis-related genes (DE-FRGs) were identified by intersecting GSE26887 dataset and the Ferroptosis Database (FerrDb). The associations between the DE-FRGs and immune cells in DCM, estimated by CIBERSORTx algorithm, were analyzed. Using flow cytometry (FCM) to evaluated the infiltration of immune cells of myocardial tissues. The expression of DE-FRGs, Glutathione peroxidase 4 (GPX4) and Solute carrier family 7 member 11 (SLC7A11) were examined by real-time quantitative PCR and western blotting. 3 DE-FRGs were identified, which are Heat shock protein family B (small) member 1 (HSPB1), Microsomal glutathione S-transferase 1 (MGST1) and solute carrier family 40 member 1 (SLC40A1) respectively, and they were closely linked to immune cells in DCM. In vivo, the levels of CD8 + T cells, B cells and Treg cells were significantly decreased in the DCM group, while the levels of CD4 + T cells, M1 cells, M2 cells and monocytes were increased. Diabetes significantly decreased HSPB1 and MGST1 levels and increased ferroptosis compared to normal group. Furthermore, ferroptosis inhibitor ferrostatin-1 (Fer-1) alleviated high-fat diet (HFD)-induced cadiomyocyte injury and rescued the ferroptosis. This study suggests that ferroptosis related gene HSPB1 and MGST1 are closely related to immune cell infiltration, which may become therapeutic targets for DCM. HSPB1 MGST1 Diabetic cardiomyopathy ferroptosis immune infiltration Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Diabetic cardiomyopathy (DCM) is a specific form of cardiomyopathy, promoted by type 2 diabetes mellitus, insulin resistance and associated hyperinsulinaemia, which occurs independently of other heart disease risk factors including hypertension, coronary artery disease (CAD), valvular disease, and so on[ 1 , 2 ]. The higher risk of cardiovascular disease in people with diabetes is attributed to persistent high glucose status, insulin resistance and long-term metabolic disorders [ 1 , 3 ]. This specific form of cardiomyopathy has a high rise of morbidity and mortality gains considerable attention in the world. DCM is characterized by abnormal myocardial structure and function[ 3 ]. Studies indicated that the main impacts of diabetes on the heart are left ventricular diastolic dysfunction, reduced left ventricular ejection fraction, reduced left ventricular compliance, left ventricular hypertrophy and interstitial fibrosis [ 4 – 6 ]. Many forms of cell death, including necroptosis, apoptosis and pyroptosis, have been verified involved in cardiovascular diseases (CVD) [ 7 ]. Ferroptosis is a new type of iron-dependent regulatory cell death, and this type of cell death is non-apoptotic and associates with the accumulation of lipid hydroperoxides [ 8 ]. In recent 10 years, a growing number of studies support the opinion that ferroptosis involves in the development of cardiovascular diseases, including myocardial ischemia–reperfusion injury, doxorubicin-induced cardiomyopathy, heart failure and myocardial infarction [ 7 , 8 ]. Both primary and secondary iron overload can lead to heart disease via oxidative damage [ 9 ]. Related studies have shown that additional iron in cardiomyocytes can directly induce ferroptosis through accumulating phospholipid hydroperoxide on the cell membrane [ 10 ]. Indeed, oxidative stress has been related to the cardiovascular disease, particularly the antioxidant system of glutathione (GSH)-dependent has been shown to protect against cardiomyopathy [ 11 , 12 ]. Here, we identified the differentially expressed genes (DEGs) in the hearts of DCM patients and non-DCM patients, and then matched these DEGs to the ferroptosis dataset to acquire differently expressed ferroptosis-related genes (DE-FRGs) to determine the mechanism of DCM development. A protein-protein interaction (PPI) network was constructed to identify key functional modules and hub genes. Furthermore, we performed immune infiltration correlation analyses of DE-FRGs in DCM to obtain the tight associations among DCM, ferroptosis and immune infiltration. We also constructed a transcription factor (TF)-miRNA-DE-FRGs network to predict that DE-FRGs are regulated by microRNA. Finally, we established a mouse model of DCM to detect the immune infiltration in the myocardium by FCM and to verify the expression of key ferroptosis-related genes (FRGs) in DCM of mice by PCR and western blotting. We further confirmed that ferroptosis was involved in the process of DCM through in vivo and in vitro experiments. These findings reveal the role of ferroptosis in DCM and suggest that ferroptosis may become a target of DCM therapy in the future. Materials and methods Data collection The GEO ( http://www.ncbi.nlm.nih.gov/geo ) is a public database, which contains a variety of diseases of microarray and high-throughput sequencing datasets. We recruited 7 patients with DCM and 5 normal control patients from GSE26887 dataset. In addition, the FRGs used in this study were derived from the Ferroptosis Database (FerrDb, http://www.zhounan.org/ferrdb ), which is the world's first database that dedicates to ferroptosis regulators and ferroptosis-disease associations. Identification of differently expressed ferroptosis-related genes The GEO2R ( https://www.ncbi.nlm.nih.gov/geo/geo2r/ ) was used to screen DEGs between the DCM group and the normal group. The screening criterion was adjusted to P < 0.05 and |log2FC|≥1. Heatmap and volcano plots of DEGs from the databases were constructed using R packages and GEO2R. We selected the overlapping DEGs of FRGs as DE-FRGs. Analysis of receiver operating characteristic curve The DE-FRGs in the validation dataset GSE26887 were considered as diagnostic biomarkers for DCM. Based on the expression of DE-FRGs in GSE26887, Graphpad Prism was used to compare the normal group and DCM group. Area under the curve (AUC) of ROC curve analysis was used to evaluate the diagnostic value of DE-FRGs for DCM. Construction of PPI Network We constructed PPI network diagrams by uploads DE-FRGs to GENEMANIA ( http://genemania.org/ ), which can find other genes that are related to a set of input genes, using a very large set of functional association data [ 13 ]. Analysis of immune cell infiltration We uploaded the expression profiles of GSE26887 to CIBERSORTx ( https://cibersortx.stanford.edu/ ) to analyze immune cell infiltration. The software tool provided an estimation of the abundances of member cell types in a mixed cell population, using gene expression data [ 14 ]. First, we used CIBERSORTx to assess the proportion of immune cell infiltration in each sample. Then, we performed Spearman's rank correlation analysis between immune cells and DE-FRGs. Animal model All animal experiments were performed in adherence to the National Institutes of Health Guidelines on the Use of Laboratory Animals and were approved by the Ethics Committee of Shanxi Provincial People Hospital. Wild-type (WT) mice (Male C57BL/6J, 8 weeks old) were utilized in this study. Mice were randomly assigned to two groups: normal diet (ND)-treated group; high-fat diet (HFD) (60% kcal fat, 20% kcal protein, 20% kcal carbohydrate, Catalogue No. D12492; Research Diets)-treated group. These two groups were fed with ND or HFD for 24 weeks. Fasting blood glucose level and glucose tolerance were examined at 12 weeks of ND or HFD. Type 2 diabetes are characterized by rising glucose (fasting blood glucose exceeded 12 mmol/L) and insulin resistance. Glucose tolerance: At the end of 12-hour fasting, blood glucose was measured via glucometer (Roche Accu-Chek™). Mice were then administrated with 1.5g/kg D-glucose via intraperitoneal injection, and blood glucose were measured at 30, 60, 120 and 180 minutes after glucose administration. Insulin tolerance: Mice were administrated with 0.5 units/kg Novolin R regular insulin via intraperitoneal injection after fasting for 4 h, and blood glucose were measured at 0, 30, 60, 90 and 120 minutes after glucose administration. Echocardiography At 24 weeks of ND or HFD feeding, mice were anaesthetized by continuous inhalation of 1−2% isoflurane. The anesthesia was determined sufficient if the mice did not respond to pinch with toothed pliers. After removing chest hairs using acoustic-coupled gel, serial M-mode echocardiographic images were obtained by GE Vivid 7 Pro System equipped with a 15-MHz linear transducer (General Electric Company, USA). Body temperature of mice was maintained between 36.5 ℃ − 37.2 ℃ during the entire experiments. Cardiac function of mice was assessed by left ventricular ejection fraction (LVEF) and the left ventricular fractional shortening (LVFS). We examined 6 mice in each group and measured each row for 3 times, and then took the average value. Histopathology After performing the echocardiography at 24 weeks, the hearts were harvested, fixed with formalin and embedded in paraffin wax. Then, 5 µm serial sections were cut and placed on plus slides. The tissue sections were stained hematoxylin and eosin (H&E) to assess for the pathological changes in the cardiac tissues of each group. cardiac tissue sections were also stained using Masson trichrome kit (BA4079A, BASO, China). Staining images were captured with a Nikon Ni-SS microscope. Real-time quantitative PCR Total RNA of cardiac tissue was extracted by Trizol kits (9108, TaKaRa, Japan), and then the cDNA was reversely transcribed by PrimeScriptTM RT Master Mix (Takara, Japan). Real-time PCR reactions were performed via the PrimeScript™ RT reagent Kit with gDNA Eraser (Takara, Japan). Primers were purchased from Sangon Biotech (Table 1 ). Relative quantities were assessed by the 2 −ΔΔCt method. Table 1 Primer sequence (5′−3′) Gene name Primer sequence (5 ′- 3 ′) HSPB1 Forward: GCTCACAGTGAAGACCAAGGAAGG Reverse: GCACCGAGAGATGTAGCCATGTTC MGST1 Forward: CGCACTGACGAGAAGGTGGAAC Reverse: GCCGATGCCGAGAAAGGGAAC SLC40A1 Forward: TTGGTGACTGGGTGGATAAGAATGC Reverse: CGCAGAGGATGACGGACACATTC Western blotting Total proteins of cells or tissues were extracted by lysis buffer. BCA Protein Assay kit (AR0197, BOSTER, China) was used to measure protein concentration. Fifty µg total proteins per sample were subjected to gel electrophoresis (Bio-Rad, Shanghai, China), and transferred to polyvinylidene fluoride (PVDF) membranes. After blocking with 5% skimmed milk (room temperature, 1 h), the membranes were incubated with anti-mouse HSPB1 (1:200 dilution, #SC-13123, Santa Cruz Biotechnology, USA), anti-rabbit SLC40A1 (1:1000 dilution, A11156, ABclonal, China), anti-MGST1(1:500 dilution, ab131059, Abcam, UK), anti-rabbit GPX4 (1:1000 dilution, #52455, Cell Signaling Technology, USA), anti-rabbit SLC7A11 (1:1000 dilution, #98051, Cell Signaling Technology, USA) at 4°C overnight. Then the membranes were incubated with the secondary HRP-conjugated antibody (anti-rabbit antibody, anti-mouse antibody, 1:5000 dilution, Boster, China) at room temperature for 2h. Ultra-sensitive enhanced chemiluminescence detection kit (AR1191, BOSTER, China) was used to detect the bands of membrane, and the image was obtained via ChemiDoc MP Imaging System (Bio-Rad, USA). The bands of density were quantified by densitometry (Image Lab). Preparation of cardiac immune cell suspension and FCM At 24 weeks, ND-fed and HFD-fed mice were both anesthetized, chest hairs were removed, open-chest surgery was performed to expose the heart, and heart was harvested for isolating single immne cells. Hearts were washed 3 times with PBS to remove blood and connective tissues. Heart tissues were digested with a digestive solution to obtain single immune cells. The composition of cardiac digestive solution: collagenase I (1.6 mg/mL, Gibco, USA) and DNase (0.2 mg/ml, MBI, USA) dissolved in PBS. The isolated immune cells were filtered using a 70-micron cell strainer. Cell suspension was stained with an antibody (BioLegend, USA) cocktail at 37 ℃ for 15 min and FCM was performed using a FCM system (BD Biosciences, USA). Immune cells were sorted as follows: CD4 + T cells, CD8 + T cells, Treg cells, B cells, NK cells, monocytes, macrophages, M1 cells, and M2 cells. Isolation of cardiomyocytes Hearts were harvested as described above. Heart tissues were rinsed 3 times with PBS to remove blood and connective tissues. Ventricles was gently cut into 1-mm pieces and then were digested using collagenase II (Worthington, Lakewood, NJ). After achieving cellular dissociation, culture medium was added to terminate digestion. Cell suspension was filtered with a 100-µm filter, then the resuspended cells were preplated for 2 h to remove fibroblasts and to obtain isolated cardiomyocytes for culture. Cell culture and treatment Cardiomyocytes isolated from the ND-fed and HFD-fed mice were cultured till 80% confluence. These cultured cells were cultured in normal DMEM medium (BOSTER, Shanghai, 25 mM D-glucose) or high glucose/high lipids (HGHL) DMEM medium (33 mM D-glucose and 500 µM palmitates) containing 10% fetal bovine serum (FBS) (BOSTER, Shanghai) and 1% penicillin/streptomycin/amphotericin B solution (BOSTER, Shanghai) in an incubator with 5% CO 2 at 37 ℃ for 24 h. To inhibit ferroptosis, the cardiomyocytes from ND-fed mice and HFD-fed mice were treated with 1 µM Fer-1 (Cat# A4371, APExBIO, USA) for 5 h. According to experimental design, the cells were divided into four groups (ND, HFD, ND + Fer-1, HFD + Fer-1), and ferroptosis was observed for all the cell groups. Cell counting kit-8 (CCK8) cell viability assay Cardiomyocytes of the above two cell groups were both cultured in 96-well plates with normal or HGHL DMEM medium for 24 h and then were exposed to vehicle or Fer-1 for 5 h. Subsequently, 10 µL fresh CCK8 solution (C0038, Beyotime, China) was added to each well and incubated for 1 h at 5% CO 2 and 37 ℃. The absorbance values at 450 nm were detected using a microplate reader (Molecular Devices, USA). Statistical analysis All statistical analysis were performed using the R version 4.0.3 and GraphPad Prism 8 software. Student’s t -test was used for comparison between two groups, and One-way ANOVA was used for multiple groups comparison. P < 0.05 was considered statistically significant. Each in vitro experiment was repeated at least three times. Results Bioinformatics reveals important role of FRGs in human DCM A total of 133 DEGs were identified in GSE26887 database using the following criteria: |log (FC)| > 0.5 and adjusted p -value > 0.05. These DEGs included 33 upregulated and 100 downregulated genes (Fig. 1 A, B). We also acquired a dataset that included 933 genes from the FerrDb. Venn diagram analysis of these two gene sets identified 3 DE-FRGs (Fig. 1 C), including 2 downregulated genes (HSPB1, MGST1) and 1 upregulated gene (SLC40A1). The PPI network analysis of interacting genes with HSPB1, MGST1 and SLC40A1 were generated by GeneMANIA. The nodes in the network show that genes were related to three DE-FRGs in terms of physical interactions, co-expression, predicted, co-localization, genetic interactions, pathway, and shared protein domains. PPI network showed that three DE-FRGs significantly interacted with ceruloplasmin, hephaestin, cytochrome P450 family 2 subfamily E member 1, heat shock protein family A member 1A, hepcidin antimicrobial peptide, and other essential genes. Biological function analysis of these genes indicated the involvement of eicosanoid metabolic process, peroxidase activity, fatty acid derivative metabolic process, antioxidant activity, iron ion homeostasis etc. (Fig. 1 D). ROC curve analysis was performed on the data of GSE26887 database to evaluate the diagnostic value of the three ferroptosis-related biomarkers in DCM. As a result, the accuracy of HSPB1, SLC40A1 and MGST1 in the diagnosis of DCM was all 100% in the GSE26887 (Fig. 1 D−F). We further used the CIBERSORTx to assess the immune infiltration levels and proportions of 11 types of immune cells between normal patients and DCM patients (Fig. 2 A). As shown in Fig. 2 B, HSPB1 was positively correlated with B cell, macrophage M2, neutrophil, Treg cell and NK cell, but was negatively correlated with monocyte and CD4 + T cell (non-regulatory). SLC40A1 was positively correlated with monocyte and CD4 + T cell (non-regulatory), while was negatively correlated with B cell, macrophage M2, neutrophil and Treg cell. MGST1 was positively correlated with macrophage M2, neutrophil and B cell, but was negatively correlated with monocyte and CD4 + T cell (non-regulatory). In addition, the three DE-FRGs were uploaded to the miRNet online database to analyze their interactions with miRNA and transcription factors (TF) in humans. As shown in Fig. 2 C, HSPB1, SLC40A1 and MGST1 were the targets of hsa-mir-34a-5p. Two TFs, STAT3 and ESR1, were closely related to HSPB1. HFD contributes to cardiac injury in mice To verify the immune infiltration in cardiac tissue and the expression of DE-FRGs in DCM, we prepared a DCM mouse model (HFD-fed for 24 weeks) [ 15 ]. At 12 weeks, compared with the ND-fed mice, the mice in the DCM group showed a significant increase in body weight (Fig. 3 A). Meanwhile, the fasting blood glucose in the DCM group was higher than 12 mmol/L, and there were obvious symptoms of polydipsia, polyphagia and polyuria (Fig. 3 B). The glucose tolerance and insulin resistance assays revealed that mice in the DCM group exhibited abnormal glucose tolerance and insulin resistance at 12 weeks (Fig. 3 C, D). At 24 weeks, the cardiac functions of HFD-fed mice were significantly decreased, as evidenced by reduced LVEF and LVFS (Fig. 3 E, F). Meanwhile, cardiac pathological changes were observed in the DCM group. H&E stains of mouse myocardium showed that, compared with the ND-fed group, the DCM group exhibited cardiac hypertrophy and disordered arrangement of cells in the myocardial tissue (Fig. 3 G). Likewise, increased cardiac fibrosis was observed in the DCM group by Masson staining (Fig. 3 H). Differential immune cell infiltration are found in the myocardium of DCM mice To examine the immune infiltration in the heart of DCM mice, we prepared single immune cell suspension of myocardial tissue in the two groups (ND-fed group and HFD-fed group), and immune cell infiltration was analyzed using FCM (Fig. 4 ). An obvious alteration of immune cell composition was observed in the DCM group compared to the normal group, including CD4 + T cells, CD8 + T cells, Treg cells, B cells, monocytes, M1 and M2 macrophages (Fig. 4 A−F). In the DCM group, B cells, CD8 + T cells and Treg cells showed a marked reduction (Fig. 4 A, C, D). Conversely, the DCM group presented with increased levels of CD4 + T cells, monocytes, M1 cells and M2 cells (Fig. 4 B, E, F). These results suggest a strong association of DCM with alterative infiltration of immune cells such as CD4 + T cells, CD8 + T cells, Treg cells, B cells, monocytes, M1 and M2 macrophages. Ferroptosis is involved in cardiac injury in mice with DCM We examined the DE-FRGs expressions of HSPB1, SLC40A1 and MGST1 in mice cardiac tissues using quantitative PCR and Western blotting (Fig. 5 ). Compared to the normal group, the mRNA expression levels of HSPB1, SLC40A1 and MGST1 was remarkably decreased in the DCM group (Fig. 5 A−C). Same results were obtained from the Western blots, the protein expressions of HSPB1, SLC40A1 and MGST1 in the DCM group was lower than that in normal group (Fig. 5 D−G). Based on RNA and protein expressions, HSPB1 and MGST1 were both coincident with the result of bioinformatics analysis. It has been reported that HSPB1 is not only related to iron death, but also involved in regulating cardiac immune microenvironment in coronary artery disease [ 16 ]. MGST1 is considered as a key ferroptosis-related gene in type 2 diabetic mellitus [ 17 ]. Our finding indicated that the expression of HSPB1 and MGST1 in DCM group were downregulated compared with the normal group. To determine whether ferroptosis was involved in this process, we verified the expressions of ferroptosis related proteins (GPX4 and SLC7A11) by western blotting. Inactivation of GPX4 and SLC7A11 has been shown to promote ferroptosis. We showed that the cardiac expression levels of GPX4 and SLC7A11 in the DCM group was significantly lower than that in the normal group (Fig. 5 H−J). Collectively, these results strongly suggest that ferroptosis is involved in the DCM of mice, and further, HSPB1 and MGST1 were likely linked to this process. Ferroptosis is involved in HFD-induced cardiomyocyte damage The above research showed that the ferroptosis plays a key role in DCM of mice. We next explored whether ferroptosis was induced in diabetic cardiomyocytes. To this end, we used isolated cardiomyocytes of mice to evaluate cell viability using CCK8 assay. Cardiomyocytes from the ND-fed and HFD-fed mice were both exposed Fer-1 or absence of Fer-1 for 5 h. Notably, the cell injury induced by HFD was significantly reversed by specific ferroptosis inhibitor Fer-1 as shown by the CCK8 assay (Fig. 6 A), suggesting that ferroptosis plays a key role in HFD-induced cardiomyocyte injury. Subsequently, we detected GPX4 and SLC7A11 protein expressions in these four groups by western blotting. We found that the protein expression levels of GPX4 and SLC7A11 were reduced in the HFD group, but Fer-1 markedly reversed the low levels of GPX4 and SLC7A11 (Fig. 6 B−D). Cell viability was decreased in cardiomyocytes from the HFD-fed mice, but was rescued by Fer-1. Moreover, the expressions of ferroptosis key suppressors GPX4 and SLC7A11 was significantly increased after Fer-1 treatment in the cardiomyocytes from HFD-fed mice. Collectively, these results strongly suggest that ferroptosis is an important cell death form in diabetic cardiomyocytes. Discussion Diabetes mellitus (DM) often accompanied by severe cardiovascular complications (DCCs) which is the main cause of death in diabetic patients and DCM is the most common DCC in DM patients [ 18 ]. In recent years, many studies have suggested that ferroptosis plays a pathophysiological role in the development of cardiovascular diseases, including diabetic cardiomyopathy, myocardial infarction, heart failure, and myocardial ischemia-reperfusion injury [ 19 , 20 ]. The pathogenesis of DCM includes oxidative stress and impaired antioxidant system under hyperglycemia [ 21 ]. As a result, imbalance of the antioxidant system often leads to excessive generation of reactive oxygen species (ROS), therefore the myocardium might undergo iron prolapse, apoptosis, inflammation, and fibrosis [ 22 ]. Recent studies indicate that nuclear factor erythroid 2-related factor 2 (NRF2) plays a pivotal role in maintaining cellular redox by regulating multiple antioxidants [ 23 ]. These regulatory genes include almost all genes encoding antioxidants involved in ferroptosis, such as glutathione regulatory genes, NADPH regeneration genes, lipid peroxidation genes, and iron regulatory genes [ 24 , 25 ]. The search for new ferroptosis-related genes may reveal the pathogenesis of DCM and provide new ideas for research. In the existing database, there are few DCM data on human specimens, and we screened 133 DEGs from the GSE26887 dataset. Three DE-FRGs were identified by interaction with the ferroptosis database. The diagnostic value of these three DE-FRGs in the GSE26887 dataset was verified by ROC curve, and the results showed that all of them had a large AUC. A larger AUC indicates a higher diagnostic value, but due to the small number of samples in this database, the significance of this result needs to be further determined. PPI network showed that three DE-FRGs significantly interacted with ceruloplasmin, hephaestin, cytochrome P450 family 2 subfamily E member 1, heat shock protein family A member 1A, hepcidin antimicrobial peptide and other essential genes. Biological function analysis showed that these genes were associated with eicosanoid metabolic process, peroxidase activity, fatty acid derivative metabolic process, antioxidant activity, iron ion homeostasis. Several studies have shown that these interacting genes are closely related to ferroptosis. Ceruloplasmin can suppresses ferroptosis by regulating iron homeostasis in hepatocellular carcinoma cells [ 26 ]. High expression of cytochrome P450 family 2 subfamily E member 1 can protect COS-7 cancer cells against ferroptosis. Our present study further demonstrated the role of these genes were associated with ferroptosis in DCM. It has been shown that genes associated with ferroptosis can lead to different immune infiltrates in many diseases [ 27 ]. For the first time, we confirmed that all the three DE-FRGs (HSPB1, SLC40A1 and MGST1) were closely related to multiple immune cell infiltration in DCM. In addition, our analysis also identified HSPB1, SLC40A1 and MGST1 to be regulated by hsa-mir-34a-5p. These results also provide us with new research ideas for the mechanism of DCM, and further provide valuable insights into the intricate interplay between DE-TSRGs and immune cell infiltration in the context of acute myocardial infarction, potentially guiding future research into targeted therapies. These findings underscore the complexity of immune cell dynamics in the context of acute myocardial infarction and may provide valuable insights into the immunopathology of the condition. To further validate the results of bioinformatics analysis, we established an animal model of DCM and confirmed its success through echocardiography and histopathology. Types and quantities of immune cells dramatically depend on different conditions of heart [ 28 , 29 ]. The infiltration or depletion of immune cells are both involved in a variety of cardiovascular diseases [ 30 , 31 ]. To verify the association between immune cells and DCM, FCM was used to detect immune cells in cardiac tissues. The results showed that CD8 + T cells, B cells and Treg cells were significant reduced in the DCM group, while CD4 + T cells, M1 cells, M2 cells and monocytes showed an obvious increase. These findings demonstrate the complexity of immune cells in the context of DCM and may provide potential guide into the immunopathology of this disease. Subsequently, we detected the cardiac expressions of HSPB1, SLC40A1 and MGST1 in mice, and both RT-qPCR and Western blotting results showed that the expressions of HSPB1 and MGST1 were consistent with the results of bioinformatics analysis. Compared with the normal group, the expressions of HSPB1 and MGST1 were significantly decreased in the DCM group. GSH is composed of glycine, amino acid cysteine, and glutamic acid. It is an effective lipid peroxidation scavenger and a key cofactor of GPX4, and the rate limiting precursor of glutathione synthesis is cysteine. Inactivation of the enzyme phospholipid hydroperoxide GPX4 and GSH depletion have been shown to promote ferroptosis [ 32 ]. SLC7A11 and 4F2HC constitute the cysteine-glutamate antiporter system, and most cells obtain cysteine from this system [ 33 ]. Thus, the GPX4-GSH-SLC7A11 axis plays a key role in the process of ferroptosis, and inhibition of GPX4 and SLC7A11 contributes to ferroptosis [ 34 ]. In this study, we detected the protein expressions of GPX4 and SLC7A11 in DCM mice, and the results showed that GPX4 and SLC7A11 proteins were significantly reduced, which is positively correlated with the expressions of HSPB1 and MGST1. Therefore, these results suggest that the reduction of HSPB1 and MGST1 is closely related to ferroptosis in DCM of mice, but further research is still needed. HSPB1 (also called mouse HSP25 or human HSP27), a member of the small heat shock proteins regulate iron homeostasis and prevent ferroptosis caused by high concentration of intracellular iron [ 23 ]. HSPB1 is an independent predictor of prognosis in patients with chronic heart failure, and its activation can also improve myocardial fiber arrangement and restore cardiac diastolic function [ 35 , 36 ]. Here we showed that HSPB1 concentrations were lower in patients with type 1 diabetes than in non-diabetic healthy controls, and animal experiments showed that upregulation of HSPB1 is associated with neuronal protection [ 37 , 38 ]. HSPB1 expression in cardiomyocytes is upregulated after myocardial infarction, and cardiomyocyte-specific HSPB1 deficiency promotes adverse remodeling after myocardial infarction, leading to cardiac dysfunction, cardiac rupture, and increased mortality [ 39 ]. At present, there are few in-depth studies on HSPB1 in diabetic cardiomyopathy and no relevant reports. MGST1 belongs to a superfamily named MAPEG (membrane-associated proteins in eicosanoid and glutathione metabolism), which is localized to the outer mitochondrial membrane and endoplasmic reticulum [ 40 , 41 ]. It is an important mediator of inflammation and the main function is to reduce the lipid peroxide and protect intracellular membranes from oxidative stress [ 42 ]. Multiple studies have shown that MGST1 is closely related to the ferroptosis in cancer disease. In pancreatic cancer, MGST1 binds ALOX5 to inhibit lipid peroxide production during ferroptosis induction [ 43 ]. MGST1 can be used as a potential prognostic indicator and immunotherapy target based on iron ptosis in uterine corpus endometrial carcinoma [ 44 ]. Upregulated expression of MGST1 is associated with poor prognosis, enhancing the Wnt/β-catenin pathway via regulating AKT, and inhibiting ferroptosis in gastric carcinoma cells [ 45 ]. Overexpression of MGST1 induces gastric carcinoma cell proliferation by activating the Akt/GSK-3β signaling pathway. In type 2 diabetes, MGST1 was identified as the potential ferroptosis-related gene in islet beta cells dysfunction (IBCD) of type 2 diabetic mellitus (T2DM) by combing large patient samples of islet issue sequencing datasets and specificity of islet single-cell sequencing dataset [ 17 ]. At present, there are few studies on MGST1 in heart disease, and no study has been reported in diabetic cardiomyopathy. Our present study is also the first report. Iron, an essential element for life, is closely associated with many biological processes, including nucleotide synthesis and repair and energy metabolism [ 46 ]. Iron overload is closely related to heart disease, for example, iron overload and injured function in heart have been observed in mice with deletion of cardiomyocyte-specific ferroportin (an iron exporter) gene [ 47 ]. Of note, the excessive cellular iron can react with oxidants through the Fenton reaction, thus promoting ferroptosis [ 48 , 49 ]. Therefore, we used Fer-1 to explore whether ferrop`tosis was involved in HFD-induced cardiomyocyte damage. Our results showed that Fer-1 could effectively ameliorate the damage of diabetic cardiomyocytes and also increase the low expression of SLC7A11 and GPX4 in diabetic cardiomyocytes. This result suggests that ferroptosis is closely related with the cardiomyocyte injury induced by HFD-fed mice. Based on our bioinformatics analysis, 3 ferroptosis related genes (HSPB1, MGST1 and SLC40A1) were focused, and the expression levels of these genes were detected in the heart tissues of mice. We found that the changes in protein levels of HSPB1 and MGST1 was coincide with the results of bioinformatics analysis, and the expression of GPX4 and SLCA71in DCM group were significantly decreased. Our results indicate that ferroptosis-related proteins (HSPB1 and MGST1) are involved in DCM in mice. In order to confirm whether ferroptosis plays an important role in cells level, we pretreated isolated cardiomyocyte with Fer-1, and found that Fer-1 could protect against cardiomyocyte injury induced by HFD in mice and notably enhance the expressions of GPX4 and SLC7A11. Our research also has several limitations. Firstly, the number of database samples we selected is relatively small, and the results of data analysis may have some randomness. Secondly, our bioinformatics analysis results show that DE-FRGs are closely related to immune infiltration and are regulated by miRNA. Further validation experiments in this area are needed. Thirdly, the role of Fer-1 is unclear in vivo. Hence, researches about improvement of myocardial damage after ferroptosis is blocked in DCM mice are necessary. In addition, we should knock down the levels of HSPB1 and MGST1 gene in DCM mice to study the status of myocardial damage, and further explore the signaling pathway of HSPB1 and MGST1 in DCM. Conclusion We for the first time demonstrated that decreases of ferroptosis-related proteins of HSPB1 and MGST1 are involved in the process of DCM, and further identified that ferroptosis plays an important role in HFD-induced cardiomyocyte injury by bioinformatics analysis of human data, cell and animal experiments. HSPB1 and MGST1 are associated with multiple immune cells infiltration and are regulated by hsa-mir-34a-5p in DCM patients. In addition, immune cells of cardiac tissues changed remarkably in DCM. The study may provide a new insight in exploring the roles of ferroptosis and immunopathology for DCM research. Declarations Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Ethics statement The animal use protocol was approved by the Ethic Committee of Shanxi Provincial People Hospital (approval No. 482). The study was conducted in accordance with the local legislation and institutional requirements. Funding This study was supported by the National Natural Science Foundation of China (82270494, 82300492, 82170523); Basic Research Program of Science and Technology Department of Shanxi Province (Grant No. 202103021223418); Scientific Research Project of Health Commission in Shanxi Province (Grant No. 2023XG009 and 2023RC008); China Postdoctoral Science Foundation (Grant No. 2023M732154); Research Project Supported by Shanxi Scholarship Council of China (Grant No. 2023 − 181) and Fund Program for the Scientific Activities of Selected Returned Overseas Professionals in Shanxi Province (Grant No. 20230054). Author Contribution Y.X., B.L., and Z.M. wrote the manuscript. Y.X., R.G., J.C., and Y.W. conceptualized the project. Y.X., B.L., Z.M., R.G., Y.Y., and W.M. collected data., B.L., C.L., and W.M. data curation, visualization, formal analysis and J.C., B.L., and Z.M. acquired funding. All authors reviewed the manuscript. Acknowledgement We appreciate all authors in this study. Data Availability The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. References Jia, G., Whaley-Connell, A., & Sowers, J. R. (2018). Diabetic cardiomyopathy: a hyperglycaemia- and insulin-resistance-induced heart disease. Diabetologia , 61 (1), 21–28. https://doi.org/10.1007/s00125-017-4390-4 Jia, G., Hill, M. A., & Sowers, J. R. (2018). Diabetic Cardiomyopathy: An Update of Mechanisms Contributing to This Clinical Entity. Circulation research , 122 (4), 624–638. https://doi.org/10.1161/circresaha.117.311586 Tan, Y., Zhang, Z., Zheng, C., Wintergerst, K. A., Keller, B. B., & Cai, L. (2020). Mechanisms of diabetic cardiomyopathy and potential therapeutic strategies: preclinical and clinical evidence. Nature reviews Cardiology , 17 (9), 585–607. https://doi.org/10.1038/s41569-020-0339-2 Travers, J. G., Kamal, F. A., Robbins, J., Yutzey, K. E., & Blaxall, B. C. (2016). Cardiac Fibrosis: The Fibroblast Awakens. Circulation research , 118 (6), 1021–1040. https://doi.org/10.1161/circresaha.115.306565 Smith, C. L., Baek, S. T., Sung, C. Y., & Tallquist, M. D. (2011). Epicardial-derived cell epithelial-to-mesenchymal transition and fate specification require PDGF receptor signaling. Circulation research , 108 (12), e15–26. https://doi.org/10.1161/circresaha.110.235531 Choi, S. Y., Chang, H. J., Choi, S. I., Kim, K. I., Cho, Y. S., Youn, T. J., Chung, W. Y., Chae, I. H., Choi, D. J., Kim, H. S., Kim, C. H., Oh, B. H., & Kim, M. H. (2009). Long-term exercise training attenuates age-related diastolic dysfunction: association of myocardial collagen cross-linking. Journal of Korean medical science , 24 (1), 32–39. https://doi.org/10.3346/jkms.2009.24.1.32 Del Re, D. P., Amgalan, D., Linkermann, A., Liu, Q., & Kitsis, R. N. (2019). Fundamental Mechanisms of Regulated Cell Death and Implications for Heart Disease. Physiological reviews , 99 (4), 1765–1817. https://doi.org/10.1152/physrev.00022.2018 Dixon, S. J., Lemberg, K. M., Lamprecht, M. R., Skouta, R., Zaitsev, E. M., Gleason, C. E., Patel, D. N., Bauer, A. J., Cantley, A. M., Yang, W. S., Morrison, B., & Stockwell, B. R. (2012). Ferroptosis: an iron-dependent form of nonapoptotic cell death. Cell , 149 (5), 1060–1072. https://doi.org/10.1016/j.cell.2012.03.042 Berdoukas, V., Coates, T. D., & Cabantchik, Z. I. (2015). Iron and oxidative stress in cardiomyopathy in thalassemia. Free radical biology & medicine 88(Pt A) , 3–9. https://doi.org/10.1016/j.freeradbiomed.2015.07.019 Fang, X., Wang, H., Han, D., Xie, E., Yang, X., Wei, J., Gu, S., Gao, F., Zhu, N., Yin, X., Cheng, Q., Zhang, P., Dai, W., Chen, J., Yang, F., Yang, H. T., Linkermann, A., Gu, W., Min, J., & Wang, F. (2019). Ferroptosis as a target for protection against cardiomyopathy. Proceedings of the National Academy of Sciences of the United States of America , 116 (7), 2672–2680. https://doi.org/10.1073/pnas.1821022116 Chen, Y. R., & Zweier, J. L. (2014). Cardiac mitochondria and reactive oxygen species generation. Circulation research , 114 (3), 524–537. https://doi.org/10.1161/circresaha.114.300559 Bajic, V. P., Van Neste, C., Obradovic, M., Zafirovic, S., Radak, D., Bajic, V. B., Essack, M., & Isenovic, E. R. (2019). Glutathione Redox Homeostasis and Its Relation to Cardiovascular Disease. Oxidative medicine and cellular longevity . 20195028181.https://doi.org/10.1155/2019/5028181 Warde-Farley, D., Donaldson, S. L., Comes, O., Zuberi, K., Badrawi, R., Chao, P., Franz, M., Grouios, C., Kazi, F., Lopes, C. T., Maitland, A., Mostafavi, S., Montojo, J., Shao, Q., Wright, G., Bader, G. D., & Morris, Q. (2010). The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic acids research , W214–220. https://doi.org/10.1093/nar/gkq537 . 38(Web Server issue). Newman, A. M., Liu, C. L., Green, M. R., Gentles, A. J., Feng, W., Xu, Y., Hoang, C. D., Diehn, M., & Alizadeh, A. A. (2015). Robust enumeration of cell subsets from tissue expression profiles. Nature methods , 12 (5), 453–457. https://doi.org/10.1038/nmeth.3337 Mu, J., Zhang, D., Tian, Y., Xie, Z., & Zou, M. H. (2020). BRD4 inhibition by JQ1 prevents high-fat diet-induced diabetic cardiomyopathy by activating PINK1/Parkin-mediated mitophagy in vivo. Journal of molecular and cellular cardiology , 1491–1414. https://doi.org/10.1016/j.yjmcc.2020.09.003 Wu, X., Qin, K., Iroegbu, C. D., Xiang, K., Peng, J., Guo, J., Yang, J., & Fan, C. (2022). Genetic analysis of potential biomarkers and therapeutic targets in ferroptosis from coronary artery disease. Journal of cellular and molecular medicine , 26 (8), 2177–2190. https://doi.org/10.1111/jcmm.17239 Ye, H., Wang, R., Wei, J., Wang, Y., Zhang, X., & Wang, L. (2022). Bioinformatics Analysis Identifies Potential Ferroptosis Key Gene in Type 2 Diabetic Islet Dysfunction. Frontiers in endocrinology . 13904312.https://doi.org/10.3389/fendo.2022.904312 Wei, J., Zhao, Y., Liang, H., Du, W., & Wang, L. (2022). Preliminary evidence for the presence of multiple forms of cell death in diabetes cardiomyopathy. Acta pharmaceutica Sinica B , 12 (1), 1–17. https://doi.org/10.1016/j.apsb.2021.08.026 Li, W., Li, W., Leng, Y., Xiong, Y., & Xia, Z. (2020). Ferroptosis Is Involved in Diabetes Myocardial Ischemia/Reperfusion Injury Through Endoplasmic Reticulum Stress. DNA and cell biology , 39 (2), 210–225. https://doi.org/10.1089/dna.2019.5097 Fang, X., Ardehali, H., Min, J., & Wang, F. (2023). The molecular and metabolic landscape of iron and ferroptosis in cardiovascular disease. Nature reviews Cardiology , 20 (1), 7–23. https://doi.org/10.1038/s41569-022-00735-4 Marwick, T. H., Ritchie, R., Shaw, J. E., & Kaye, D. (2018). Implications of Underlying Mechanisms for the Recognition and Management of Diabetic Cardiomyopathy. Journal of the American College of Cardiology , 71 (3), 339–351. https://doi.org/10.1016/j.jacc.2017.11.019 Shah, M. S., & Brownlee, M. (2016). Molecular and Cellular Mechanisms of Cardiovascular Disorders in Diabetes. Circulation research , 118 (11), 1808–1829. https://doi.org/10.1161/circresaha.116.306923 Sha, W., Hu, F., Xi, Y., Chu, Y., & Bu, S. (2021). Mechanism of Ferroptosis and Its Role in Type 2 Diabetes Mellitus. Journal of diabetes research . https://doi.org/10.1155/2021/9999612 . 20219999612. Fan, Z., Wirth, A. K., Chen, D., Wruck, C. J., Rauh, M., Buchfelder, M., & Savaskan, N. (2017). Nrf2-Keap1 pathway promotes cell proliferation and diminishes ferroptosis. Oncogenesis , 6 (8), e371. .https://doi.org/10.1038/oncsis.2017.65 Abdalkader, M., Lampinen, R., Kanninen, K. M., Malm, T. M., & Liddell, J. R. (2018). Targeting Nrf2 to Suppress Ferroptosis and Mitochondrial Dysfunction in Neurodegeneration. Frontiers in neuroscience . 12466.https://doi.org/10.3389/fnins.2018.00466 Shang, Y., Luo, M., Yao, F., Wang, S., Yuan, Z., & Yang, Y. (2020). Ceruloplasmin suppresses ferroptosis by regulating iron homeostasis in hepatocellular carcinoma cells. Cellular signalling . 72109633.https://doi.org/10.1016/j.cellsig.2020.109633 Lu, H., Xie, Y., Zhou, Z., Hong, P., & Chen, J. (2023). Identification of Novel Targets for Treatment of Dilated Cardiomyopathy Based on the Ferroptosis and Immune Heterogeneity. Journal of inflammation research , 162461–162476. https://doi.org/10.2147/jir.s407588 Ren, Z., Yu, P., Li, D., Li, Z., Liao, Y., Wang, Y., Zhou, B., & Wang, L. (2020). Single-Cell Reconstruction of Progression Trajectory Reveals Intervention Principles in Pathological Cardiac Hypertrophy. Circulation , 141 (21), 1704–1719. https://doi.org/10.1161/circulationaha.119.043053 Blanton, R. M., Carrillo-Salinas, F. J., & Alcaide, P. (2019). T-cell recruitment to the heart: friendly guests or unwelcome visitors? American journal of physiology Heart and circulatory physiology , 317 (1), H124–h140. https://doi.org/10.1152/ajpheart.00028.2019 Dick, S. A., Macklin, J. A., Nejat, S., Momen, A., Clemente-Casares, X., Althagafi, M. G., Chen, J., Kantores, C., Hosseinzadeh, S., Aronoff, L., Wong, A., Zaman, R., Barbu, I., Besla, R., Lavine, K. J., Razani, B., Ginhoux, F., Husain, M., Cybulsky, M. I., Robbins, C. S., & Epelman, S. (2019). Self-renewing resident cardiac macrophages limit adverse remodeling following myocardial infarction. Nature immunology , 20 (1), 29–39. https://doi.org/10.1038/s41590-018-0272-2 Wong, A., Hamidzada, H., & Epelman, S. (2022). A cardioimmunologist's toolkit: genetic tools to dissect immune cells in cardiac disease. Nature reviews Cardiology , 19 (6), 395–413. https://doi.org/10.1038/s41569-022-00701-0 Stockwell, B. R., Friedmann Angeli, J. P., Bayir, H., Bush, A. I., Conrad, M., Dixon, S. J., Fulda, S., Gascón, S., Hatzios, S. K., Kagan, V. E., Noel, K., Jiang, X., Linkermann, A., Murphy, M. E., Overholtzer, M., Oyagi, A., Pagnussat, G. C., Park, J., Ran, Q., Rosenfeld, C. S., Salnikow, K., Tang, D., Torti, F. M., Torti, S. V., Toyokuni, S., Woerpel, K. A., & Zhang, D. D. (2017). Ferroptosis: A Regulated Cell Death Nexus Linking Metabolism, Redox Biology, and Disease. Cell , 171 (2), 273–285. https://doi.org/10.1016/j.cell.2017.09.021 Koppula, P., Zhuang, L., & Gan, B. (2021). Cystine transporter SLC7A11/xCT in cancer: ferroptosis, nutrient dependency, and cancer therapy. Protein & cell , 12 (8), 599–620. https://doi.org/10.1007/s13238-020-00789-5 Konstorum, A., Tesfay, L., Paul, B. T., Torti, F. M., Laubenbacher, R. C., & Torti, S. V. (2020). Systems biology of ferroptosis: A modeling approach. Journal of theoretical biology . 493110222.https://doi.org/10.1016/j.jtbi.2020.110222 Traxler, D., Lainscak, M., Simader, E., Ankersmit, H. J., & Jug, B. (2017). Heat shock protein 27 acts as a predictor of prognosis in chronic heart failure patients. Clinica chimica acta; international journal of clinical chemistry , 473127–473132. https://doi.org/10.1016/j.cca.2017.08.028 Li, S., Liang, M., Pan, Y., Wang, M., Gao, D., Shang, H., Su, Q., & Laher, I. (2020). Exercise modulates heat shock protein 27 activity in diabetic cardiomyopathy. Life sciences . 243117251.https://doi.org/10.1016/j.lfs.2019.117251 Pourhamidi, K., Skärstrand, H., Dahlin, L. B., & Rolandsson, O. (2014). HSP27 concentrations are lower in patients with type 1 diabetes and correlate with large nerve fiber dysfunction. Diabetes care , 37 (3), e49–50. https://doi.org/10.2337/dc13-1780 Dodge, M. E., Wang, J., Guy, C., Rankin, S., Rahimtula, M., & Mearow, K. M. (2006). Stress-induced heat shock protein 27 expression and its role in dorsal root ganglion neuronal survival. Brain research , 1068 (1), 34–48. https://doi.org/10.1016/j.brainres.2005.11.008 Wang, Y., Liu, J., Kong, Q., Cheng, H., Tu, F., Yu, P., Liu, Y., Zhang, X., Li, C., Li, Y., Min, X., Du, S., Ding, Z., & Liu, L. (2019). Cardiomyocyte-specific deficiency of HSPB1 worsens cardiac dysfunction by activating NFκB-mediated leucocyte recruitment after myocardial infarction. Cardiovascular research , 115 (1), 154–167. https://doi.org/10.1093/cvr/cvy163 Morgenstern, R., Zhang, J., & Johansson, K. (2011). Microsomal glutathione transferase 1: mechanism and functional roles. Drug metabolism reviews , 43 (2), 300–306. https://doi.org/10.3109/03602532.2011.558511 Zeng, B., Ge, C., Li, R., Zhang, Z., Fu, Q., Li, Z., Lin, Z., Liu, L., Xue, Y., Xu, Y., He, J., Guo, H., Li, C., Huang, W., Song, X., & Huang, Y. (2020). Knockdown of microsomal glutathione S-transferase 1 inhibits lung adenocarcinoma cell proliferation and induces apoptosis. Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie . 121109562.https://doi.org/10.1016/j.biopha.2019.109562 Bräutigam, L., Zhang, J., Dreij, K., Spahiu, L., Holmgren, A., Abe, H., Tew, K. D., Townsend, D. M., Kelner, M. J., Morgenstern, R., & Johansson, K. (2018). MGST1, a GSH transferase/peroxidase essential for development and hematopoietic stem cell differentiation. Redox biology , 17171–17179. https://doi.org/10.1016/j.redox.2018.04.013 Kuang, F., Liu, J., Xie, Y., Tang, D., & Kang, R. (2021). MGST1 is a redox-sensitive repressor of ferroptosis in pancreatic cancer cells. Cell chemical biology , 28 (6), 765–775. e765.https://doi.org/10.1016/j.chembiol.2021.01.006 Yan, J., Ye, G., & Shao, Y. (2022). High expression of the ferroptosis-associated MGST1 gene in relation to poor outcome and maladjusted immune cell infiltration in uterine corpus endometrial carcinoma. Journal of clinical laboratory analysis , 36 (4), e24317. .https://doi.org/10.1002/jcla.24317 Li, Y., Xu, X., Wang, X., Zhang, C., Hu, A., & Li, Y. (2023). MGST1 Expression Is Associated with Poor Prognosis, Enhancing the Wnt/β-Catenin Pathway via Regulating AKT and Inhibiting Ferroptosis in Gastric Cancer. ACS omega , 8 (26), 23683–23694. https://doi.org/10.1021/acsomega.3c01782 Ganz, T. (2013). Systemic iron homeostasis. Physiological reviews , 93 (4), 1721–1741. https://doi.org/10.1152/physrev.00008.2013 Lakhal-Littleton, S., Wolna, M., Carr, C. A., Miller, J. J., Christian, H. C., Ball, V., Santos, A., Diaz, R., Biggs, D., Stillion, R., Holdship, P., Larner, F., Tyler, D. J., Clarke, K., Davies, B., & Robbins, P. A. (2015). Cardiac ferroportin regulates cellular iron homeostasis and is important for cardiac function. Proceedings of the National Academy of Sciences of the United States of America , 112 (10), 3164–3169. https://doi.org/10.1073/pnas.1422373112 Fang, X., Cai, Z., Wang, H., Han, D., Cheng, Q., Zhang, P., Gao, F., Yu, Y., Song, Z., Wu, Q., An, P., Huang, S., Pan, J., Chen, H. Z., Chen, J., Linkermann, A., Min, J., & Wang, F. (2020). Loss of Cardiac Ferritin H Facilitates Cardiomyopathy via Slc7a11-Mediated Ferroptosis. Circulation research , 127 (4), 486–501. https://doi.org/10.1161/circresaha.120.316509 Yu, Y., Jiang, L., Wang, H., Shen, Z., Cheng, Q., Zhang, P., Wang, J., Wu, Q., Fang, X., Duan, L., Wang, S., Wang, K., An, P., Shao, T., Chung, R. T., Zheng, S., Min, J., & Wang, F. (2020). Hepatic transferrin plays a role in systemic iron homeostasis and liver ferroptosis. Blood , 136 (6), 726–739. https://doi.org/10.1182/blood.2019002907 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 07 Mar, 2025 Read the published version in Cardiovascular Toxicology → Version 1 posted Editorial decision: Revision requested 06 Oct, 2024 Editor assigned by journal 06 Oct, 2024 Submission checks completed at journal 26 Sep, 2024 First submitted to journal 25 Sep, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5153598","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":362827434,"identity":"028d9739-9854-4151-858d-f52f1df6ca75","order_by":0,"name":"Yaoli Xie","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yaoli","middleName":"","lastName":"Xie","suffix":""},{"id":362827436,"identity":"a93638d0-cb81-4fd3-abcc-5b59031db0fe","order_by":1,"name":"Bin Liang","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Bin","middleName":"","lastName":"Liang","suffix":""},{"id":362827437,"identity":"a258c82f-a6e5-42dd-b419-841971f4fca4","order_by":2,"name":"Zhijun Meng","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhijun","middleName":"","lastName":"Meng","suffix":""},{"id":362827439,"identity":"c24209c2-e8dc-4b98-a669-41261ba2bdbd","order_by":3,"name":"Rui Guo","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Guo","suffix":""},{"id":362827440,"identity":"dd1101b5-8d4b-4e16-b671-63b66a27cc6b","order_by":4,"name":"Caihong Liu","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Caihong","middleName":"","lastName":"Liu","suffix":""},{"id":362827441,"identity":"94f2c1c1-7cc5-41d6-9e88-81353376865a","order_by":5,"name":"Yi Yuan","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Yuan","suffix":""},{"id":362827442,"identity":"a49baba6-5e5e-45ed-a991-f18fcb990cd5","order_by":6,"name":"Wei Mu","email":"","orcid":"","institution":"The Fifth Hospital of Shanxi Medical University (Shanxi Provincial People's Hospital)","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Mu","suffix":""},{"id":362827443,"identity":"7ba169cb-5fd6-4a9d-a2ef-2575db27db67","order_by":7,"name":"Yajing Wang","email":"","orcid":"","institution":"University of Alabama at Birmingham","correspondingAuthor":false,"prefix":"","firstName":"Yajing","middleName":"","lastName":"Wang","suffix":""},{"id":362827444,"identity":"5a821e89-6f60-49a9-a445-4e1bd587e4f1","order_by":8,"name":"Jimin Cao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIiWNgGAWjYJACgwQGCQZ+KIexgWgtkg2kaIHoO0CsFv72wwcKHuZY5BmfP2O6mYfBRnbDAeZnD/BpkTiTlmCQuE2i2OxGWtptHoY04w0H2MwN8LqHIccApCVx2w3mY0AthxM3HOBhk8Crhf8NRMvm/oNtQC3/idAiAbVlA0MyyJYDhLVI3HgG9kviDKBfbs4xSDaeeZjNDK8W/v7kY4Y/t9Ul9vefMbvxpsJOtu948zO8WoCADSl8QExmAupBSh4QVjMKRsEoGAUjGgAA0iRJl4sQrhMAAAAASUVORK5CYII=","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":true,"prefix":"","firstName":"Jimin","middleName":"","lastName":"Cao","suffix":""}],"badges":[],"createdAt":"2024-09-25 16:53:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5153598/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5153598/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12012-025-09982-6","type":"published","date":"2025-03-07T15:57:07+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":70908808,"identity":"96fae26a-bf10-436f-86ca-f8db71590906","added_by":"auto","created_at":"2024-12-09 07:03:37","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3401975,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of DE-FRGs and their role in DCM. (A) Volcano plot of DEGs. (B) Heatmaps of DEGs. (C) Venn diagram of DE-FRGs from GSE26887 and FerrDb. (D) PPI network of genes associated with three DE-FRGs. (E, F) ROC curve analysis revealing the diagnostic values of the three DE-FRGs in DCM.\u003c/p\u003e","description":"","filename":"fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5153598/v1/aab1dd1caa6f25e1fbd770dc.jpg"},{"id":70910526,"identity":"8abea3d5-6d29-4196-bb8b-41c361fda59b","added_by":"auto","created_at":"2024-12-09 07:19:38","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2956825,"visible":true,"origin":"","legend":"\u003cp\u003eImmune infiltration analysis and miRNA regulatory network of DE-FRGs. (A) The enrichment fraction of 11 types of immune infiltrating cells in the DCM and normal samples. (B) Correlation heat map between 11 types of immune cells and DE-FRGs (blue indicates a positive correlation, red indicates a negative correlation, and the depth of the color indicates the degree of correlation). (C) Construction of the TF-miRNA-DE-FRGs network based on miRNet (blue indicates miRNA, green indicates TF, and yellow indicates DE-FRGs).\u003c/p\u003e","description":"","filename":"fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5153598/v1/db56997882c33ca19cf04671.jpg"},{"id":70908813,"identity":"68c81517-ff0b-41fd-9d0d-f02380f61bfe","added_by":"auto","created_at":"2024-12-09 07:03:38","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":17956948,"visible":true,"origin":"","legend":"\u003cp\u003eManifestations of DCM and cardiac injury of mice in normal group and DCM group. (A, B) Body weight and fasting blood glucose of mice in both groups. n = 10 for each group. * \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05, ** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01 vs. Normal. (C, D) Glucose tolerance and insulin resistance of mice at 12 weeks in both groups. Data are expressed as mean ± SD, n = 10 for each group. * \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003e p\u003c/em\u003e \u0026lt; 0.01 vs. Normal. (E) Cardiac function of mice in different groups accessed by echocardiography. (F, H) left ventricle ejection fraction (LVEF) and left ventricle fraction shortening (LVFS) measured by echocardiography. Data are expressed as mean ± SD, n = 10 for each group. ** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01 vs. Normal. (G) Pathological changes of myocardium in mice (H\u0026amp;E, 20×). (H) Changes of cardiac fibrosis in mice (Masson, 20×). DCM, diabetic cardiomyopathy. LVEF, left ventricular ejection fraction. LVFS, left ventricular fraction shortening. H\u0026amp;E, hematoxylin and eosin.\u003c/p\u003e","description":"","filename":"fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5153598/v1/4aca91d6d2a1b1ea6c4afd80.jpg"},{"id":70910397,"identity":"6b0ab23d-32fc-4168-a77c-f4785d055ae8","added_by":"auto","created_at":"2024-12-09 07:11:37","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2333540,"visible":true,"origin":"","legend":"\u003cp\u003eFCM results on the status of immune infiltration in Normal group and DCM group. (A-F) Count of each immune cell type per gram of the myocardial tissue (count/g) in Normal group and DCM group. Data are expressed as mean ± SD, n = 10 for each group. * \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 vs. Normal group.\u003c/p\u003e","description":"","filename":"FIg4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5153598/v1/3f23c4a875fa8d117748546f.jpg"},{"id":70910398,"identity":"d45a201f-791f-46ba-929b-09897cbbc073","added_by":"auto","created_at":"2024-12-09 07:11:38","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":9224525,"visible":true,"origin":"","legend":"\u003cp\u003eFerroptosis in the progression of cardiac injury in DCM. (A-C) Transcription levels of HSPB1, SLC40A1 and MGST1 genes in Normal group and DCM group by accessed by quantitative real-time PCR. Data are expressed as means ± SD, n = 10 for each group. *** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001 vs. Normal. (D) Western blots showing the protein expression levels of HSPB1, SLC40A and MGST1 in both groups. (E-G) Quantification of the Western blot results. Data are expressed as mean ± SD, n = 10 for each group. ** \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.01, *** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001 vs. Normal. (H) Western blots of GPX4 and SLC7A11 in Normal group and DCM group. (I, J) Quantification of the Western blot results. Data are expressed as mean ± SD, n = 10 for each group. ** \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.01 vs. Normal. HSPB1, heat shock protein family B (small) member 1. SLC40A1, solute carrier family 40 member 1. MGST1, microsomal glutathione S-transferase 1. GPX4, glutathione peroxidase. SLC7A11, solute carrier family 7 member 11.\u003c/p\u003e","description":"","filename":"FIg5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5153598/v1/cf9ff30213592aeeec0b2ad4.jpg"},{"id":70908810,"identity":"3fed8a13-7b27-42fd-9c29-4d368fb00cbb","added_by":"auto","created_at":"2024-12-09 07:03:38","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":6055144,"visible":true,"origin":"","legend":"\u003cp\u003eFerroptosis involved in HGHL-induced injury in AC16 cells. (A) Viability of AC16 cells evaluated by CCK8 assay. AC16 cells were divided into four groups (Normal, HGHL, Normal+Fer-1, HGHL+Fer-1). Data are expressed as mean ± SD, n = 8 for each group. ** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01 vs. Normal; # \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 vs. HGHL; ## \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.01 vs. HGHL. (B) Western blots of GPX4 and SLC7A11 in the four groups (Normal, HGHL, Normal+Fer-1, HGHL+Fer-1). (C, D) Quantification of the Western blot results. Data are expressed as mean ± SD, n = 8 for each group. ** \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.01, vs. Normal; ## \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01 vs. AC16, human cardiomyocyte cell line. HGHL, high glucose/high lipids. Fer-1, ferrostatin-1.\u003c/p\u003e","description":"","filename":"fig6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5153598/v1/b3b196291db5e876850511c0.jpg"},{"id":78191289,"identity":"d902a5f5-d59e-4d96-8b81-2380e9030b03","added_by":"auto","created_at":"2025-03-10 19:55:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":42816489,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5153598/v1/87400855-42d8-4f60-adc3-46a4837e47bc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Down-regulation of HSPB1 and MGST1 promote ferroptosis and impact immune infiltration in diabetic cardiomyopathy","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDiabetic cardiomyopathy (DCM) is a specific form of cardiomyopathy, promoted by type 2 diabetes mellitus, insulin resistance and associated hyperinsulinaemia, which occurs independently of other heart disease risk factors including hypertension, coronary artery disease (CAD), valvular disease, and so on[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The higher risk of cardiovascular disease in people with diabetes is attributed to persistent high glucose status, insulin resistance and long-term metabolic disorders [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This specific form of cardiomyopathy has a high rise of morbidity and mortality gains considerable attention in the world. DCM is characterized by abnormal myocardial structure and function[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Studies indicated that the main impacts of diabetes on the heart are left ventricular diastolic dysfunction, reduced left ventricular ejection fraction, reduced left ventricular compliance, left ventricular hypertrophy and interstitial fibrosis [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMany forms of cell death, including necroptosis, apoptosis and pyroptosis, have been verified involved in cardiovascular diseases (CVD) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Ferroptosis is a new type of iron-dependent regulatory cell death, and this type of cell death is non-apoptotic and associates with the accumulation of lipid hydroperoxides [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In recent 10 years, a growing number of studies support the opinion that ferroptosis involves in the development of cardiovascular diseases, including myocardial ischemia\u0026ndash;reperfusion injury, doxorubicin-induced cardiomyopathy, heart failure and myocardial infarction [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Both primary and secondary iron overload can lead to heart disease via oxidative damage [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Related studies have shown that additional iron in cardiomyocytes can directly induce ferroptosis through accumulating phospholipid hydroperoxide on the cell membrane [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Indeed, oxidative stress has been related to the cardiovascular disease, particularly the antioxidant system of glutathione (GSH)-dependent has been shown to protect against cardiomyopathy [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHere, we identified the differentially expressed genes (DEGs) in the hearts of DCM patients and non-DCM patients, and then matched these DEGs to the ferroptosis dataset to acquire differently expressed ferroptosis-related genes (DE-FRGs) to determine the mechanism of DCM development. A protein-protein interaction (PPI) network was constructed to identify key functional modules and hub genes. Furthermore, we performed immune infiltration correlation analyses of DE-FRGs in DCM to obtain the tight associations among DCM, ferroptosis and immune infiltration. We also constructed a transcription factor (TF)-miRNA-DE-FRGs network to predict that DE-FRGs are regulated by microRNA. Finally, we established a mouse model of DCM to detect the immune infiltration in the myocardium by FCM and to verify the expression of key ferroptosis-related genes (FRGs) in DCM of mice by PCR and western blotting. We further confirmed that ferroptosis was involved in the process of DCM through in vivo and in vitro experiments. These findings reveal the role of ferroptosis in DCM and suggest that ferroptosis may become a target of DCM therapy in the future.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eThe GEO (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/geo\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/geo\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is a public database, which contains a variety of diseases of microarray and high-throughput sequencing datasets. We recruited 7 patients with DCM and 5 normal control patients from GSE26887 dataset. In addition, the FRGs used in this study were derived from the Ferroptosis Database (FerrDb, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.zhounan.org/ferrdb\u003c/span\u003e\u003cspan address=\"http://www.zhounan.org/ferrdb\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which is the world's first database that dedicates to ferroptosis regulators and ferroptosis-disease associations.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eIdentification of differently expressed ferroptosis-related genes\u003c/h3\u003e\n\u003cp\u003eThe GEO2R (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/geo2r/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/geo2r/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to screen DEGs between the DCM group and the normal group. The screening criterion was adjusted to \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log2FC|\u0026ge;1. Heatmap and volcano plots of DEGs from the databases were constructed using R packages and GEO2R. We selected the overlapping DEGs of FRGs as DE-FRGs.\u003c/p\u003e\n\u003ch3\u003eAnalysis of receiver operating characteristic curve\u003c/h3\u003e\n\u003cp\u003eThe DE-FRGs in the validation dataset GSE26887 were considered as diagnostic biomarkers for DCM. Based on the expression of DE-FRGs in GSE26887, Graphpad Prism was used to compare the normal group and DCM group. Area under the curve (AUC) of ROC curve analysis was used to evaluate the diagnostic value of DE-FRGs for DCM.\u003c/p\u003e\n\u003ch3\u003eConstruction of PPI Network\u003c/h3\u003e\n\u003cp\u003eWe constructed PPI network diagrams by uploads DE-FRGs to GENEMANIA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://genemania.org/\u003c/span\u003e\u003cspan address=\"http://genemania.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which can find other genes that are related to a set of input genes, using a very large set of functional association data [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eAnalysis of immune cell infiltration\u003c/h3\u003e\n\u003cp\u003eWe uploaded the expression profiles of GSE26887 to CIBERSORTx (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cibersortx.stanford.edu/\u003c/span\u003e\u003cspan address=\"https://cibersortx.stanford.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to analyze immune cell infiltration. The software tool provided an estimation of the abundances of member cell types in a mixed cell population, using gene expression data [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. First, we used CIBERSORTx to assess the proportion of immune cell infiltration in each sample. Then, we performed Spearman's rank correlation analysis between immune cells and DE-FRGs.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAnimal model\u003c/h2\u003e \u003cp\u003e All animal experiments were performed in adherence to the National Institutes of Health Guidelines on the Use of Laboratory Animals and were approved by the Ethics Committee of Shanxi Provincial People Hospital. Wild-type (WT) mice (Male C57BL/6J, 8 weeks old) were utilized in this study. Mice were randomly assigned to two groups: normal diet (ND)-treated group; high-fat diet (HFD) (60% kcal fat, 20% kcal protein, 20% kcal carbohydrate, Catalogue No. D12492; Research Diets)-treated group. These two groups were fed with ND or HFD for 24 weeks. Fasting blood glucose level and glucose tolerance were examined at 12 weeks of ND or HFD. Type 2 diabetes are characterized by rising glucose (fasting blood glucose exceeded 12 mmol/L) and insulin resistance. Glucose tolerance: At the end of 12-hour fasting, blood glucose was measured via glucometer (Roche Accu-Chek\u0026trade;). Mice were then administrated with 1.5g/kg D-glucose via intraperitoneal injection, and blood glucose were measured at 30, 60, 120 and 180 minutes after glucose administration. Insulin tolerance: Mice were administrated with 0.5 units/kg Novolin R regular insulin via intraperitoneal injection after fasting for 4 h, and blood glucose were measured at 0, 30, 60, 90 and 120 minutes after glucose administration.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEchocardiography\u003c/h3\u003e\n\u003cp\u003eAt 24 weeks of ND or HFD feeding, mice were anaesthetized by continuous inhalation of 1\u0026minus;2% isoflurane. The anesthesia was determined sufficient if the mice did not respond to pinch with toothed pliers. After removing chest hairs using acoustic-coupled gel, serial M-mode echocardiographic images were obtained by GE Vivid 7 Pro System equipped with a 15-MHz linear transducer (General Electric Company, USA). Body temperature of mice was maintained between 36.5 ℃ \u0026minus; 37.2 ℃ during the entire experiments. Cardiac function of mice was assessed by left ventricular ejection fraction (LVEF) and the left ventricular fractional shortening (LVFS). We examined 6 mice in each group and measured each row for 3 times, and then took the average value.\u003c/p\u003e\n\u003ch3\u003eHistopathology\u003c/h3\u003e\n\u003cp\u003eAfter performing the echocardiography at 24 weeks, the hearts were harvested, fixed with formalin and embedded in paraffin wax. Then, 5 \u0026micro;m serial sections were cut and placed on plus slides. The tissue sections were stained hematoxylin and eosin (H\u0026amp;E) to assess for the pathological changes in the cardiac tissues of each group. cardiac tissue sections were also stained using Masson trichrome kit (BA4079A, BASO, China). Staining images were captured with a Nikon Ni-SS microscope.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eReal-time quantitative PCR\u003c/h2\u003e \u003cp\u003eTotal RNA of cardiac tissue was extracted by Trizol kits (9108, TaKaRa, Japan), and then the cDNA was reversely transcribed by PrimeScriptTM RT Master Mix (Takara, Japan). Real-time PCR reactions were performed via the PrimeScript\u0026trade; RT reagent Kit with gDNA Eraser (Takara, Japan). Primers were purchased from Sangon Biotech (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Relative quantities were assessed by the 2\u003csup\u003e\u0026minus;ΔΔCt\u003c/sup\u003e method.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrimer sequence (5\u0026prime;\u0026minus;3\u0026prime;)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimer sequence (5 \u0026prime;- 3 \u0026prime;)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHSPB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward: GCTCACAGTGAAGACCAAGGAAGG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReverse: GCACCGAGAGATGTAGCCATGTTC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMGST1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward: CGCACTGACGAGAAGGTGGAAC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReverse: GCCGATGCCGAGAAAGGGAAC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSLC40A1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward: TTGGTGACTGGGTGGATAAGAATGC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReverse: CGCAGAGGATGACGGACACATTC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eWestern blotting\u003c/h2\u003e \u003cp\u003eTotal proteins of cells or tissues were extracted by lysis buffer. BCA Protein Assay kit (AR0197, BOSTER, China) was used to measure protein concentration. Fifty \u0026micro;g total proteins per sample were subjected to gel electrophoresis (Bio-Rad, Shanghai, China), and transferred to polyvinylidene fluoride (PVDF) membranes. After blocking with 5% skimmed milk (room temperature, 1 h), the membranes were incubated with anti-mouse HSPB1 (1:200 dilution, #SC-13123, Santa Cruz Biotechnology, USA), anti-rabbit SLC40A1 (1:1000 dilution, A11156, ABclonal, China), anti-MGST1(1:500 dilution, ab131059, Abcam, UK), anti-rabbit GPX4 (1:1000 dilution, #52455, Cell Signaling Technology, USA), anti-rabbit SLC7A11 (1:1000 dilution, #98051, Cell Signaling Technology, USA) at 4\u0026deg;C overnight. Then the membranes were incubated with the secondary HRP-conjugated antibody (anti-rabbit antibody, anti-mouse antibody, 1:5000 dilution, Boster, China) at room temperature for 2h. Ultra-sensitive enhanced chemiluminescence detection kit (AR1191, BOSTER, China) was used to detect the bands of membrane, and the image was obtained via ChemiDoc MP Imaging System (Bio-Rad, USA). The bands of density were quantified by densitometry (Image Lab).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePreparation of cardiac immune cell suspension and FCM\u003c/h2\u003e \u003cp\u003eAt 24 weeks, ND-fed and HFD-fed mice were both anesthetized, chest hairs were removed, open-chest surgery was performed to expose the heart, and heart was harvested for isolating single immne cells. Hearts were washed 3 times with PBS to remove blood and connective tissues. Heart tissues were digested with a digestive solution to obtain single immune cells. The composition of cardiac digestive solution: collagenase I (1.6 mg/mL, Gibco, USA) and DNase (0.2 mg/ml, MBI, USA) dissolved in PBS. The isolated immune cells were filtered using a 70-micron cell strainer. Cell suspension was stained with an antibody (BioLegend, USA) cocktail at 37 ℃ for 15 min and FCM was performed using a FCM system (BD Biosciences, USA). Immune cells were sorted as follows: CD4\u0026thinsp;+\u0026thinsp;T cells, CD8\u0026thinsp;+\u0026thinsp;T cells, Treg cells, B cells, NK cells, monocytes, macrophages, M1 cells, and M2 cells.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eIsolation of cardiomyocytes\u003c/h2\u003e \u003cp\u003eHearts were harvested as described above. Heart tissues were rinsed 3 times with PBS to remove blood and connective tissues. Ventricles was gently cut into 1-mm pieces and then were digested using collagenase II (Worthington, Lakewood, NJ). After achieving cellular dissociation, culture medium was added to terminate digestion. Cell suspension was filtered with a 100-\u0026micro;m filter, then the resuspended cells were preplated for 2 h to remove fibroblasts and to obtain isolated cardiomyocytes for culture.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCell culture and treatment\u003c/h2\u003e \u003cp\u003eCardiomyocytes isolated from the ND-fed and HFD-fed mice were cultured till 80% confluence. These cultured cells were cultured in normal DMEM medium (BOSTER, Shanghai, 25 mM D-glucose) or high glucose/high lipids (HGHL) DMEM medium (33 mM D-glucose and 500 \u0026micro;M palmitates) containing 10% fetal bovine serum (FBS) (BOSTER, Shanghai) and 1% penicillin/streptomycin/amphotericin B solution (BOSTER, Shanghai) in an incubator with 5% CO\u003csub\u003e2\u003c/sub\u003e at 37 ℃ for 24 h.\u003c/p\u003e \u003cp\u003eTo inhibit ferroptosis, the cardiomyocytes from ND-fed mice and HFD-fed mice were treated with 1 \u0026micro;M Fer-1 (Cat# A4371, APExBIO, USA) for 5 h. According to experimental design, the cells were divided into four groups (ND, HFD, ND\u0026thinsp;+\u0026thinsp;Fer-1, HFD\u0026thinsp;+\u0026thinsp;Fer-1), and ferroptosis was observed for all the cell groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eCell counting kit-8 (CCK8) cell viability assay\u003c/h2\u003e \u003cp\u003eCardiomyocytes of the above two cell groups were both cultured in 96-well plates with normal or HGHL DMEM medium for 24 h and then were exposed to vehicle or Fer-1 for 5 h. Subsequently, 10 \u0026micro;L fresh CCK8 solution (C0038, Beyotime, China) was added to each well and incubated for 1 h at 5% CO\u003csub\u003e2\u003c/sub\u003e and 37 ℃. The absorbance values at 450 nm were detected using a microplate reader (Molecular Devices, USA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analysis were performed using the R version 4.0.3 and GraphPad Prism 8 software. Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test was used for comparison between two groups, and One-way ANOVA was used for multiple groups comparison. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. Each in vitro experiment was repeated at least three times.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eBioinformatics reveals important role of FRGs in human DCM\u003c/h2\u003e \u003cp\u003eA total of 133 DEGs were identified in GSE26887 database using the following criteria: |log (FC)| \u0026gt; 0.5 and adjusted \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05. These DEGs included 33 upregulated and 100 downregulated genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, B). We also acquired a dataset that included 933 genes from the FerrDb. Venn diagram analysis of these two gene sets identified 3 DE-FRGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC), including 2 downregulated genes (HSPB1, MGST1) and 1 upregulated gene (SLC40A1). The PPI network analysis of interacting genes with HSPB1, MGST1 and SLC40A1 were generated by GeneMANIA. The nodes in the network show that genes were related to three DE-FRGs in terms of physical interactions, co-expression, predicted, co-localization, genetic interactions, pathway, and shared protein domains. PPI network showed that three DE-FRGs significantly interacted with ceruloplasmin, hephaestin, cytochrome P450 family 2 subfamily E member 1, heat shock protein family A member 1A, hepcidin antimicrobial peptide, and other essential genes. Biological function analysis of these genes indicated the involvement of eicosanoid metabolic process, peroxidase activity, fatty acid derivative metabolic process, antioxidant activity, iron ion homeostasis etc. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). ROC curve analysis was performed on the data of GSE26887 database to evaluate the diagnostic value of the three ferroptosis-related biomarkers in DCM. As a result, the accuracy of HSPB1, SLC40A1 and MGST1 in the diagnosis of DCM was all 100% in the GSE26887 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD\u0026minus;F).\u003c/p\u003e \u003cp\u003eWe further used the CIBERSORTx to assess the immune infiltration levels and proportions of 11 types of immune cells between normal patients and DCM patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, HSPB1 was positively correlated with B cell, macrophage M2, neutrophil, Treg cell and NK cell, but was negatively correlated with monocyte and CD4\u0026thinsp;+\u0026thinsp;T cell (non-regulatory). SLC40A1 was positively correlated with monocyte and CD4\u0026thinsp;+\u0026thinsp;T cell (non-regulatory), while was negatively correlated with B cell, macrophage M2, neutrophil and Treg cell. MGST1 was positively correlated with macrophage M2, neutrophil and B cell, but was negatively correlated with monocyte and CD4\u0026thinsp;+\u0026thinsp;T cell (non-regulatory). In addition, the three DE-FRGs were uploaded to the miRNet online database to analyze their interactions with miRNA and transcription factors (TF) in humans. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, HSPB1, SLC40A1 and MGST1 were the targets of hsa-mir-34a-5p. Two TFs, STAT3 and ESR1, were closely related to HSPB1.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eHFD contributes to cardiac injury in mice\u003c/h2\u003e \u003cp\u003eTo verify the immune infiltration in cardiac tissue and the expression of DE-FRGs in DCM, we prepared a DCM mouse model (HFD-fed for 24 weeks) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. At 12 weeks, compared with the ND-fed mice, the mice in the DCM group showed a significant increase in body weight (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Meanwhile, the fasting blood glucose in the DCM group was higher than 12 mmol/L, and there were obvious symptoms of polydipsia, polyphagia and polyuria (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The glucose tolerance and insulin resistance assays revealed that mice in the DCM group exhibited abnormal glucose tolerance and insulin resistance at 12 weeks (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, D). At 24 weeks, the cardiac functions of HFD-fed mice were significantly decreased, as evidenced by reduced LVEF and LVFS (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE, F). Meanwhile, cardiac pathological changes were observed in the DCM group. H\u0026amp;E stains of mouse myocardium showed that, compared with the ND-fed group, the DCM group exhibited cardiac hypertrophy and disordered arrangement of cells in the myocardial tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG). Likewise, increased cardiac fibrosis was observed in the DCM group by Masson staining (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eDifferential immune cell infiltration are found in the myocardium of DCM mice\u003c/h2\u003e \u003cp\u003eTo examine the immune infiltration in the heart of DCM mice, we prepared single immune cell suspension of myocardial tissue in the two groups (ND-fed group and HFD-fed group), and immune cell infiltration was analyzed using FCM (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). An obvious alteration of immune cell composition was observed in the DCM group compared to the normal group, including CD4\u0026thinsp;+\u0026thinsp;T cells, CD8\u0026thinsp;+\u0026thinsp;T cells, Treg cells, B cells, monocytes, M1 and M2 macrophages (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA\u0026minus;F). In the DCM group, B cells, CD8\u0026thinsp;+\u0026thinsp;T cells and Treg cells showed a marked reduction (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, C, D). Conversely, the DCM group presented with increased levels of CD4\u0026thinsp;+\u0026thinsp;T cells, monocytes, M1 cells and M2 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, E, F). These results suggest a strong association of DCM with alterative infiltration of immune cells such as CD4\u0026thinsp;+\u0026thinsp;T cells, CD8\u0026thinsp;+\u0026thinsp;T cells, Treg cells, B cells, monocytes, M1 and M2 macrophages.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eFerroptosis is involved in cardiac injury in mice with DCM\u003c/h2\u003e \u003cp\u003eWe examined the DE-FRGs expressions of HSPB1, SLC40A1 and MGST1 in mice cardiac tissues using quantitative PCR and Western blotting (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Compared to the normal group, the mRNA expression levels of HSPB1, SLC40A1 and MGST1 was remarkably decreased in the DCM group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA\u0026minus;C). Same results were obtained from the Western blots, the protein expressions of HSPB1, SLC40A1 and MGST1 in the DCM group was lower than that in normal group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD\u0026minus;G). Based on RNA and protein expressions, HSPB1 and MGST1 were both coincident with the result of bioinformatics analysis. It has been reported that HSPB1 is not only related to iron death, but also involved in regulating cardiac immune microenvironment in coronary artery disease [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. MGST1 is considered as a key ferroptosis-related gene in type 2 diabetic mellitus [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Our finding indicated that the expression of HSPB1 and MGST1 in DCM group were downregulated compared with the normal group. To determine whether ferroptosis was involved in this process, we verified the expressions of ferroptosis related proteins (GPX4 and SLC7A11) by western blotting. Inactivation of GPX4 and SLC7A11 has been shown to promote ferroptosis. We showed that the cardiac expression levels of GPX4 and SLC7A11 in the DCM group was significantly lower than that in the normal group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eH\u0026minus;J). Collectively, these results strongly suggest that ferroptosis is involved in the DCM of mice, and further, HSPB1 and MGST1 were likely linked to this process.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eFerroptosis is involved in HFD-induced cardiomyocyte damage\u003c/h2\u003e \u003cp\u003eThe above research showed that the ferroptosis plays a key role in DCM of mice. We next explored whether ferroptosis was induced in diabetic cardiomyocytes. To this end, we used isolated cardiomyocytes of mice to evaluate cell viability using CCK8 assay. Cardiomyocytes from the ND-fed and HFD-fed mice were both exposed Fer-1 or absence of Fer-1 for 5 h. Notably, the cell injury induced by HFD was significantly reversed by specific ferroptosis inhibitor Fer-1 as shown by the CCK8 assay (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA), suggesting that ferroptosis plays a key role in HFD-induced cardiomyocyte injury. Subsequently, we detected GPX4 and SLC7A11 protein expressions in these four groups by western blotting. We found that the protein expression levels of GPX4 and SLC7A11 were reduced in the HFD group, but Fer-1 markedly reversed the low levels of GPX4 and SLC7A11 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB\u0026minus;D). Cell viability was decreased in cardiomyocytes from the HFD-fed mice, but was rescued by Fer-1. Moreover, the expressions of ferroptosis key suppressors GPX4 and SLC7A11 was significantly increased after Fer-1 treatment in the cardiomyocytes from HFD-fed mice. Collectively, these results strongly suggest that ferroptosis is an important cell death form in diabetic cardiomyocytes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eDiabetes mellitus (DM) often accompanied by severe cardiovascular complications (DCCs) which is the main cause of death in diabetic patients and DCM is the most common DCC in DM patients [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In recent years, many studies have suggested that ferroptosis plays a pathophysiological role in the development of cardiovascular diseases, including diabetic cardiomyopathy, myocardial infarction, heart failure, and myocardial ischemia-reperfusion injury [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The pathogenesis of DCM includes oxidative stress and impaired antioxidant system under hyperglycemia [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. As a result, imbalance of the antioxidant system often leads to excessive generation of reactive oxygen species (ROS), therefore the myocardium might undergo iron prolapse, apoptosis, inflammation, and fibrosis [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Recent studies indicate that nuclear factor erythroid 2-related factor 2 (NRF2) plays a pivotal role in maintaining cellular redox by regulating multiple antioxidants [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. These regulatory genes include almost all genes encoding antioxidants involved in ferroptosis, such as glutathione regulatory genes, NADPH regeneration genes, lipid peroxidation genes, and iron regulatory genes [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The search for new ferroptosis-related genes may reveal the pathogenesis of DCM and provide new ideas for research.\u003c/p\u003e \u003cp\u003eIn the existing database, there are few DCM data on human specimens, and we screened 133 DEGs from the GSE26887 dataset. Three DE-FRGs were identified by interaction with the ferroptosis database. The diagnostic value of these three DE-FRGs in the GSE26887 dataset was verified by ROC curve, and the results showed that all of them had a large AUC. A larger AUC indicates a higher diagnostic value, but due to the small number of samples in this database, the significance of this result needs to be further determined. PPI network showed that three DE-FRGs significantly interacted with ceruloplasmin, hephaestin, cytochrome P450 family 2 subfamily E member 1, heat shock protein family A member 1A, hepcidin antimicrobial peptide and other essential genes. Biological function analysis showed that these genes were associated with eicosanoid metabolic process, peroxidase activity, fatty acid derivative metabolic process, antioxidant activity, iron ion homeostasis. Several studies have shown that these interacting genes are closely related to ferroptosis. Ceruloplasmin can suppresses ferroptosis by regulating iron homeostasis in hepatocellular carcinoma cells [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. High expression of cytochrome P450 family 2 subfamily E member 1 can protect COS-7 cancer cells against ferroptosis. Our present study further demonstrated the role of these genes were associated with ferroptosis in DCM.\u003c/p\u003e \u003cp\u003eIt has been shown that genes associated with ferroptosis can lead to different immune infiltrates in many diseases [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. For the first time, we confirmed that all the three DE-FRGs (HSPB1, SLC40A1 and MGST1) were closely related to multiple immune cell infiltration in DCM. In addition, our analysis also identified HSPB1, SLC40A1 and MGST1 to be regulated by hsa-mir-34a-5p. These results also provide us with new research ideas for the mechanism of DCM, and further provide valuable insights into the intricate interplay between DE-TSRGs and immune cell infiltration in the context of acute myocardial infarction, potentially guiding future research into targeted therapies. These findings underscore the complexity of immune cell dynamics in the context of acute myocardial infarction and may provide valuable insights into the immunopathology of the condition.\u003c/p\u003e \u003cp\u003eTo further validate the results of bioinformatics analysis, we established an animal model of DCM and confirmed its success through echocardiography and histopathology. Types and quantities of immune cells dramatically depend on different conditions of heart [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The infiltration or depletion of immune cells are both involved in a variety of cardiovascular diseases [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. To verify the association between immune cells and DCM, FCM was used to detect immune cells in cardiac tissues. The results showed that CD8\u0026thinsp;+\u0026thinsp;T cells, B cells and Treg cells were significant reduced in the DCM group, while CD4\u0026thinsp;+\u0026thinsp;T cells, M1 cells, M2 cells and monocytes showed an obvious increase. These findings demonstrate the complexity of immune cells in the context of DCM and may provide potential guide into the immunopathology of this disease. Subsequently, we detected the cardiac expressions of HSPB1, SLC40A1 and MGST1 in mice, and both RT-qPCR and Western blotting results showed that the expressions of HSPB1 and MGST1 were consistent with the results of bioinformatics analysis. Compared with the normal group, the expressions of HSPB1 and MGST1 were significantly decreased in the DCM group. GSH is composed of glycine, amino acid cysteine, and glutamic acid. It is an effective lipid peroxidation scavenger and a key cofactor of GPX4, and the rate limiting precursor of glutathione synthesis is cysteine. Inactivation of the enzyme phospholipid hydroperoxide GPX4 and GSH depletion have been shown to promote ferroptosis [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. SLC7A11 and 4F2HC constitute the cysteine-glutamate antiporter system, and most cells obtain cysteine from this system [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Thus, the GPX4-GSH-SLC7A11 axis plays a key role in the process of ferroptosis, and inhibition of GPX4 and SLC7A11 contributes to ferroptosis [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, we detected the protein expressions of GPX4 and SLC7A11 in DCM mice, and the results showed that GPX4 and SLC7A11 proteins were significantly reduced, which is positively correlated with the expressions of HSPB1 and MGST1. Therefore, these results suggest that the reduction of HSPB1 and MGST1 is closely related to ferroptosis in DCM of mice, but further research is still needed. HSPB1 (also called mouse HSP25 or human HSP27), a member of the small heat shock proteins regulate iron homeostasis and prevent ferroptosis caused by high concentration of intracellular iron [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. HSPB1 is an independent predictor of prognosis in patients with chronic heart failure, and its activation can also improve myocardial fiber arrangement and restore cardiac diastolic function [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Here we showed that HSPB1 concentrations were lower in patients with type 1 diabetes than in non-diabetic healthy controls, and animal experiments showed that upregulation of HSPB1 is associated with neuronal protection [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. HSPB1 expression in cardiomyocytes is upregulated after myocardial infarction, and cardiomyocyte-specific HSPB1 deficiency promotes adverse remodeling after myocardial infarction, leading to cardiac dysfunction, cardiac rupture, and increased mortality [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. At present, there are few in-depth studies on HSPB1 in diabetic cardiomyopathy and no relevant reports. MGST1 belongs to a superfamily named MAPEG (membrane-associated proteins in eicosanoid and glutathione metabolism), which is localized to the outer mitochondrial membrane and endoplasmic reticulum [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. It is an important mediator of inflammation and the main function is to reduce the lipid peroxide and protect intracellular membranes from oxidative stress [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Multiple studies have shown that MGST1 is closely related to the ferroptosis in cancer disease. In pancreatic cancer, MGST1 binds ALOX5 to inhibit lipid peroxide production during ferroptosis induction [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. MGST1 can be used as a potential prognostic indicator and immunotherapy target based on iron ptosis in uterine corpus endometrial carcinoma [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Upregulated expression of MGST1 is associated with poor prognosis, enhancing the Wnt/β-catenin pathway via regulating AKT, and inhibiting ferroptosis in gastric carcinoma cells [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Overexpression of MGST1 induces gastric carcinoma cell proliferation by activating the Akt/GSK-3β signaling pathway. In type 2 diabetes, MGST1 was identified as the potential ferroptosis-related gene in islet beta cells dysfunction (IBCD) of type 2 diabetic mellitus (T2DM) by combing large patient samples of islet issue sequencing datasets and specificity of islet single-cell sequencing dataset [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. At present, there are few studies on MGST1 in heart disease, and no study has been reported in diabetic cardiomyopathy. Our present study is also the first report.\u003c/p\u003e \u003cp\u003eIron, an essential element for life, is closely associated with many biological processes, including nucleotide synthesis and repair and energy metabolism [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Iron overload is closely related to heart disease, for example, iron overload and injured function in heart have been observed in mice with deletion of cardiomyocyte-specific ferroportin (an iron exporter) gene [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Of note, the excessive cellular iron can react with oxidants through the Fenton reaction, thus promoting ferroptosis [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Therefore, we used Fer-1 to explore whether ferrop`tosis was involved in HFD-induced cardiomyocyte damage. Our results showed that Fer-1 could effectively ameliorate the damage of diabetic cardiomyocytes and also increase the low expression of SLC7A11 and GPX4 in diabetic cardiomyocytes. This result suggests that ferroptosis is closely related with the cardiomyocyte injury induced by HFD-fed mice. Based on our bioinformatics analysis, 3 ferroptosis related genes (HSPB1, MGST1 and SLC40A1) were focused, and the expression levels of these genes were detected in the heart tissues of mice. We found that the changes in protein levels of HSPB1 and MGST1 was coincide with the results of bioinformatics analysis, and the expression of GPX4 and SLCA71in DCM group were significantly decreased. Our results indicate that ferroptosis-related proteins (HSPB1 and MGST1) are involved in DCM in mice. In order to confirm whether ferroptosis plays an important role in cells level, we pretreated isolated cardiomyocyte with Fer-1, and found that Fer-1 could protect against cardiomyocyte injury induced by HFD in mice and notably enhance the expressions of GPX4 and SLC7A11.\u003c/p\u003e \u003cp\u003eOur research also has several limitations. Firstly, the number of database samples we selected is relatively small, and the results of data analysis may have some randomness. Secondly, our bioinformatics analysis results show that DE-FRGs are closely related to immune infiltration and are regulated by miRNA. Further validation experiments in this area are needed. Thirdly, the role of Fer-1 is unclear in vivo. Hence, researches about improvement of myocardial damage after ferroptosis is blocked in DCM mice are necessary. In addition, we should knock down the levels of HSPB1 and MGST1 gene in DCM mice to study the status of myocardial damage, and further explore the signaling pathway of HSPB1 and MGST1 in DCM.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe for the first time demonstrated that decreases of ferroptosis-related proteins of HSPB1 and MGST1 are involved in the process of DCM, and further identified that ferroptosis plays an important role in HFD-induced cardiomyocyte injury by bioinformatics analysis of human data, cell and animal experiments. HSPB1 and MGST1 are associated with multiple immune cells infiltration and are regulated by hsa-mir-34a-5p in DCM patients. In addition, immune cells of cardiac tissues changed remarkably in DCM. The study may provide a new insight in exploring the roles of ferroptosis and immunopathology for DCM research.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eConflict of interest\u003c/strong\u003e \u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthics statement\u003c/h2\u003e \u003cp\u003e The animal use protocol was approved by the Ethic Committee of Shanxi Provincial People Hospital (approval No. 482). The study was conducted in accordance with the local legislation and institutional requirements.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was supported by the National Natural Science Foundation of China (82270494, 82300492, 82170523); Basic Research Program of Science and Technology Department of Shanxi Province (Grant No. 202103021223418); Scientific Research Project of Health Commission in Shanxi Province (Grant No. 2023XG009 and 2023RC008); China Postdoctoral Science Foundation (Grant No. 2023M732154); Research Project Supported by Shanxi Scholarship Council of China (Grant No. 2023\u0026thinsp;\u0026minus;\u0026thinsp;181) and Fund Program for the Scientific Activities of Selected Returned Overseas Professionals in Shanxi Province (Grant No. 20230054).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eY.X., B.L., and Z.M. wrote the manuscript. Y.X., R.G., J.C., and Y.W. conceptualized the project. Y.X., B.L., Z.M., R.G., Y.Y., and W.M. collected data., B.L., C.L., and W.M. data curation, visualization, formal analysis and J.C., B.L., and Z.M. acquired funding. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe appreciate all authors in this study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJia, G., Whaley-Connell, A., \u0026amp; Sowers, J. R. (2018). Diabetic cardiomyopathy: a hyperglycaemia- and insulin-resistance-induced heart disease. \u003cem\u003eDiabetologia\u003c/em\u003e, \u003cem\u003e61\u003c/em\u003e(1), 21\u0026ndash;28. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00125-017-4390-4\u003c/span\u003e\u003cspan address=\"10.1007/s00125-017-4390-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJia, G., Hill, M. A., \u0026amp; Sowers, J. R. (2018). Diabetic Cardiomyopathy: An Update of Mechanisms Contributing to This Clinical Entity. \u003cem\u003eCirculation research\u003c/em\u003e, \u003cem\u003e122\u003c/em\u003e(4), 624\u0026ndash;638. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/circresaha.117.311586\u003c/span\u003e\u003cspan address=\"10.1161/circresaha.117.311586\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTan, Y., Zhang, Z., Zheng, C., Wintergerst, K. A., Keller, B. B., \u0026amp; Cai, L. (2020). Mechanisms of diabetic cardiomyopathy and potential therapeutic strategies: preclinical and clinical evidence. \u003cem\u003eNature reviews Cardiology\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(9), 585\u0026ndash;607. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41569-020-0339-2\u003c/span\u003e\u003cspan address=\"10.1038/s41569-020-0339-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTravers, J. G., Kamal, F. A., Robbins, J., Yutzey, K. E., \u0026amp; Blaxall, B. C. (2016). Cardiac Fibrosis: The Fibroblast Awakens. \u003cem\u003eCirculation research\u003c/em\u003e, \u003cem\u003e118\u003c/em\u003e(6), 1021\u0026ndash;1040. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/circresaha.115.306565\u003c/span\u003e\u003cspan address=\"10.1161/circresaha.115.306565\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmith, C. L., Baek, S. T., Sung, C. Y., \u0026amp; Tallquist, M. D. (2011). Epicardial-derived cell epithelial-to-mesenchymal transition and fate specification require PDGF receptor signaling. \u003cem\u003eCirculation research\u003c/em\u003e, \u003cem\u003e108\u003c/em\u003e(12), e15\u0026ndash;26. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/circresaha.110.235531\u003c/span\u003e\u003cspan address=\"10.1161/circresaha.110.235531\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChoi, S. Y., Chang, H. J., Choi, S. I., Kim, K. I., Cho, Y. S., Youn, T. J., Chung, W. Y., Chae, I. H., Choi, D. J., Kim, H. S., Kim, C. H., Oh, B. H., \u0026amp; Kim, M. H. (2009). Long-term exercise training attenuates age-related diastolic dysfunction: association of myocardial collagen cross-linking. \u003cem\u003eJournal of Korean medical science\u003c/em\u003e, \u003cem\u003e24\u003c/em\u003e(1), 32\u0026ndash;39. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3346/jkms.2009.24.1.32\u003c/span\u003e\u003cspan address=\"10.3346/jkms.2009.24.1.32\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDel Re, D. P., Amgalan, D., Linkermann, A., Liu, Q., \u0026amp; Kitsis, R. N. (2019). Fundamental Mechanisms of Regulated Cell Death and Implications for Heart Disease. \u003cem\u003ePhysiological reviews\u003c/em\u003e, \u003cem\u003e99\u003c/em\u003e(4), 1765\u0026ndash;1817. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1152/physrev.00022.2018\u003c/span\u003e\u003cspan address=\"10.1152/physrev.00022.2018\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDixon, S. J., Lemberg, K. M., Lamprecht, M. R., Skouta, R., Zaitsev, E. M., Gleason, C. E., Patel, D. N., Bauer, A. J., Cantley, A. M., Yang, W. S., Morrison, B., \u0026amp; Stockwell, B. R. (2012). Ferroptosis: an iron-dependent form of nonapoptotic cell death. \u003cem\u003eCell\u003c/em\u003e, \u003cem\u003e149\u003c/em\u003e(5), 1060\u0026ndash;1072. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cell.2012.03.042\u003c/span\u003e\u003cspan address=\"10.1016/j.cell.2012.03.042\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerdoukas, V., Coates, T. D., \u0026amp; Cabantchik, Z. I. (2015). Iron and oxidative stress in cardiomyopathy in thalassemia. \u003cem\u003eFree radical biology \u0026amp; medicine 88(Pt A)\u003c/em\u003e, 3\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.freeradbiomed.2015.07.019\u003c/span\u003e\u003cspan address=\"10.1016/j.freeradbiomed.2015.07.019\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFang, X., Wang, H., Han, D., Xie, E., Yang, X., Wei, J., Gu, S., Gao, F., Zhu, N., Yin, X., Cheng, Q., Zhang, P., Dai, W., Chen, J., Yang, F., Yang, H. T., Linkermann, A., Gu, W., Min, J., \u0026amp; Wang, F. (2019). Ferroptosis as a target for protection against cardiomyopathy. \u003cem\u003eProceedings of the National Academy of Sciences of the United States of America\u003c/em\u003e, \u003cem\u003e116\u003c/em\u003e(7), 2672\u0026ndash;2680. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.1821022116\u003c/span\u003e\u003cspan address=\"10.1073/pnas.1821022116\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, Y. R., \u0026amp; Zweier, J. L. (2014). Cardiac mitochondria and reactive oxygen species generation. \u003cem\u003eCirculation research\u003c/em\u003e, \u003cem\u003e114\u003c/em\u003e(3), 524\u0026ndash;537. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/circresaha.114.300559\u003c/span\u003e\u003cspan address=\"10.1161/circresaha.114.300559\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBajic, V. P., Van Neste, C., Obradovic, M., Zafirovic, S., Radak, D., Bajic, V. B., Essack, M., \u0026amp; Isenovic, E. R. (2019). Glutathione Redox Homeostasis and Its Relation to Cardiovascular Disease. \u003cem\u003eOxidative medicine and cellular longevity\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e20195028181.https://doi.org/10.1155/2019/5028181\u003c/span\u003e\u003cspan address=\"20195028181.10.1155/2019/5028181\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWarde-Farley, D., Donaldson, S. L., Comes, O., Zuberi, K., Badrawi, R., Chao, P., Franz, M., Grouios, C., Kazi, F., Lopes, C. T., Maitland, A., Mostafavi, S., Montojo, J., Shao, Q., Wright, G., Bader, G. D., \u0026amp; Morris, Q. (2010). The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. \u003cem\u003eNucleic acids research\u003c/em\u003e, W214\u0026ndash;220. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/nar/gkq537\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkq537\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. 38(Web Server issue).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNewman, A. M., Liu, C. L., Green, M. R., Gentles, A. J., Feng, W., Xu, Y., Hoang, C. D., Diehn, M., \u0026amp; Alizadeh, A. A. (2015). Robust enumeration of cell subsets from tissue expression profiles. \u003cem\u003eNature methods\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(5), 453\u0026ndash;457. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nmeth.3337\u003c/span\u003e\u003cspan address=\"10.1038/nmeth.3337\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMu, J., Zhang, D., Tian, Y., Xie, Z., \u0026amp; Zou, M. H. (2020). BRD4 inhibition by JQ1 prevents high-fat diet-induced diabetic cardiomyopathy by activating PINK1/Parkin-mediated mitophagy in vivo. \u003cem\u003eJournal of molecular and cellular cardiology\u003c/em\u003e, 1491\u0026ndash;1414. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.yjmcc.2020.09.003\u003c/span\u003e\u003cspan address=\"10.1016/j.yjmcc.2020.09.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu, X., Qin, K., Iroegbu, C. D., Xiang, K., Peng, J., Guo, J., Yang, J., \u0026amp; Fan, C. (2022). Genetic analysis of potential biomarkers and therapeutic targets in ferroptosis from coronary artery disease. \u003cem\u003eJournal of cellular and molecular medicine\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e(8), 2177\u0026ndash;2190. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/jcmm.17239\u003c/span\u003e\u003cspan address=\"10.1111/jcmm.17239\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYe, H., Wang, R., Wei, J., Wang, Y., Zhang, X., \u0026amp; Wang, L. (2022). Bioinformatics Analysis Identifies Potential Ferroptosis Key Gene in Type 2 Diabetic Islet Dysfunction. \u003cem\u003eFrontiers in endocrinology\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e13904312.https://doi.org/10.3389/fendo.2022.904312\u003c/span\u003e\u003cspan address=\"13904312.10.3389/fendo.2022.904312\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWei, J., Zhao, Y., Liang, H., Du, W., \u0026amp; Wang, L. (2022). Preliminary evidence for the presence of multiple forms of cell death in diabetes cardiomyopathy. \u003cem\u003eActa pharmaceutica Sinica B\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(1), 1\u0026ndash;17. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.apsb.2021.08.026\u003c/span\u003e\u003cspan address=\"10.1016/j.apsb.2021.08.026\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, W., Li, W., Leng, Y., Xiong, Y., \u0026amp; Xia, Z. (2020). Ferroptosis Is Involved in Diabetes Myocardial Ischemia/Reperfusion Injury Through Endoplasmic Reticulum Stress. \u003cem\u003eDNA and cell biology\u003c/em\u003e, \u003cem\u003e39\u003c/em\u003e(2), 210\u0026ndash;225. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1089/dna.2019.5097\u003c/span\u003e\u003cspan address=\"10.1089/dna.2019.5097\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFang, X., Ardehali, H., Min, J., \u0026amp; Wang, F. (2023). The molecular and metabolic landscape of iron and ferroptosis in cardiovascular disease. \u003cem\u003eNature reviews Cardiology\u003c/em\u003e, \u003cem\u003e20\u003c/em\u003e(1), 7\u0026ndash;23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41569-022-00735-4\u003c/span\u003e\u003cspan address=\"10.1038/s41569-022-00735-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarwick, T. H., Ritchie, R., Shaw, J. E., \u0026amp; Kaye, D. (2018). Implications of Underlying Mechanisms for the Recognition and Management of Diabetic Cardiomyopathy. \u003cem\u003eJournal of the American College of Cardiology\u003c/em\u003e, \u003cem\u003e71\u003c/em\u003e(3), 339\u0026ndash;351. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jacc.2017.11.019\u003c/span\u003e\u003cspan address=\"10.1016/j.jacc.2017.11.019\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShah, M. S., \u0026amp; Brownlee, M. (2016). Molecular and Cellular Mechanisms of Cardiovascular Disorders in Diabetes. \u003cem\u003eCirculation research\u003c/em\u003e, \u003cem\u003e118\u003c/em\u003e(11), 1808\u0026ndash;1829. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/circresaha.116.306923\u003c/span\u003e\u003cspan address=\"10.1161/circresaha.116.306923\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSha, W., Hu, F., Xi, Y., Chu, Y., \u0026amp; Bu, S. (2021). Mechanism of Ferroptosis and Its Role in Type 2 Diabetes Mellitus. \u003cem\u003eJournal of diabetes research\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1155/2021/9999612\u003c/span\u003e\u003cspan address=\"10.1155/2021/9999612\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. 20219999612.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFan, Z., Wirth, A. K., Chen, D., Wruck, C. J., Rauh, M., Buchfelder, M., \u0026amp; Savaskan, N. (2017). Nrf2-Keap1 pathway promotes cell proliferation and diminishes ferroptosis. \u003cem\u003eOncogenesis\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(8), e371. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e.https://doi.org/10.1038/oncsis.2017.65\u003c/span\u003e\u003cspan address=\".10.1038/oncsis.2017.65\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbdalkader, M., Lampinen, R., Kanninen, K. M., Malm, T. M., \u0026amp; Liddell, J. R. (2018). Targeting Nrf2 to Suppress Ferroptosis and Mitochondrial Dysfunction in Neurodegeneration. \u003cem\u003eFrontiers in neuroscience\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e12466.https://doi.org/10.3389/fnins.2018.00466\u003c/span\u003e\u003cspan address=\"12466.10.3389/fnins.2018.00466\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShang, Y., Luo, M., Yao, F., Wang, S., Yuan, Z., \u0026amp; Yang, Y. (2020). Ceruloplasmin suppresses ferroptosis by regulating iron homeostasis in hepatocellular carcinoma cells. \u003cem\u003eCellular signalling\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e72109633.https://doi.org/10.1016/j.cellsig.2020.109633\u003c/span\u003e\u003cspan address=\"72109633.10.1016/j.cellsig.2020.109633\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu, H., Xie, Y., Zhou, Z., Hong, P., \u0026amp; Chen, J. (2023). Identification of Novel Targets for Treatment of Dilated Cardiomyopathy Based on the Ferroptosis and Immune Heterogeneity. \u003cem\u003eJournal of inflammation research\u003c/em\u003e, 162461\u0026ndash;162476. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2147/jir.s407588\u003c/span\u003e\u003cspan address=\"10.2147/jir.s407588\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRen, Z., Yu, P., Li, D., Li, Z., Liao, Y., Wang, Y., Zhou, B., \u0026amp; Wang, L. (2020). Single-Cell Reconstruction of Progression Trajectory Reveals Intervention Principles in Pathological Cardiac Hypertrophy. \u003cem\u003eCirculation\u003c/em\u003e, \u003cem\u003e141\u003c/em\u003e(21), 1704\u0026ndash;1719. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/circulationaha.119.043053\u003c/span\u003e\u003cspan address=\"10.1161/circulationaha.119.043053\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlanton, R. M., Carrillo-Salinas, F. J., \u0026amp; Alcaide, P. (2019). T-cell recruitment to the heart: friendly guests or unwelcome visitors? \u003cem\u003eAmerican journal of physiology Heart and circulatory physiology\u003c/em\u003e, \u003cem\u003e317\u003c/em\u003e(1), H124\u0026ndash;h140. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1152/ajpheart.00028.2019\u003c/span\u003e\u003cspan address=\"10.1152/ajpheart.00028.2019\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDick, S. A., Macklin, J. A., Nejat, S., Momen, A., Clemente-Casares, X., Althagafi, M. G., Chen, J., Kantores, C., Hosseinzadeh, S., Aronoff, L., Wong, A., Zaman, R., Barbu, I., Besla, R., Lavine, K. J., Razani, B., Ginhoux, F., Husain, M., Cybulsky, M. I., Robbins, C. S., \u0026amp; Epelman, S. (2019). Self-renewing resident cardiac macrophages limit adverse remodeling following myocardial infarction. \u003cem\u003eNature immunology\u003c/em\u003e, \u003cem\u003e20\u003c/em\u003e(1), 29\u0026ndash;39. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41590-018-0272-2\u003c/span\u003e\u003cspan address=\"10.1038/s41590-018-0272-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWong, A., Hamidzada, H., \u0026amp; Epelman, S. (2022). A cardioimmunologist's toolkit: genetic tools to dissect immune cells in cardiac disease. \u003cem\u003eNature reviews Cardiology\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(6), 395\u0026ndash;413. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41569-022-00701-0\u003c/span\u003e\u003cspan address=\"10.1038/s41569-022-00701-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStockwell, B. R., Friedmann Angeli, J. P., Bayir, H., Bush, A. I., Conrad, M., Dixon, S. J., Fulda, S., Gasc\u0026oacute;n, S., Hatzios, S. K., Kagan, V. E., Noel, K., Jiang, X., Linkermann, A., Murphy, M. E., Overholtzer, M., Oyagi, A., Pagnussat, G. C., Park, J., Ran, Q., Rosenfeld, C. S., Salnikow, K., Tang, D., Torti, F. M., Torti, S. V., Toyokuni, S., Woerpel, K. A., \u0026amp; Zhang, D. D. (2017). Ferroptosis: A Regulated Cell Death Nexus Linking Metabolism, Redox Biology, and Disease. \u003cem\u003eCell\u003c/em\u003e, \u003cem\u003e171\u003c/em\u003e(2), 273\u0026ndash;285. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cell.2017.09.021\u003c/span\u003e\u003cspan address=\"10.1016/j.cell.2017.09.021\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoppula, P., Zhuang, L., \u0026amp; Gan, B. (2021). Cystine transporter SLC7A11/xCT in cancer: ferroptosis, nutrient dependency, and cancer therapy. \u003cem\u003eProtein \u0026amp; cell\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(8), 599\u0026ndash;620. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s13238-020-00789-5\u003c/span\u003e\u003cspan address=\"10.1007/s13238-020-00789-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKonstorum, A., Tesfay, L., Paul, B. T., Torti, F. M., Laubenbacher, R. C., \u0026amp; Torti, S. V. (2020). Systems biology of ferroptosis: A modeling approach. \u003cem\u003eJournal of theoretical biology\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e493110222.https://doi.org/10.1016/j.jtbi.2020.110222\u003c/span\u003e\u003cspan address=\"493110222.10.1016/j.jtbi.2020.110222\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTraxler, D., Lainscak, M., Simader, E., Ankersmit, H. J., \u0026amp; Jug, B. (2017). Heat shock protein 27 acts as a predictor of prognosis in chronic heart failure patients. \u003cem\u003eClinica chimica acta; international journal of clinical chemistry\u003c/em\u003e, 473127\u0026ndash;473132. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cca.2017.08.028\u003c/span\u003e\u003cspan address=\"10.1016/j.cca.2017.08.028\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, S., Liang, M., Pan, Y., Wang, M., Gao, D., Shang, H., Su, Q., \u0026amp; Laher, I. (2020). Exercise modulates heat shock protein 27 activity in diabetic cardiomyopathy. \u003cem\u003eLife sciences\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e243117251.https://doi.org/10.1016/j.lfs.2019.117251\u003c/span\u003e\u003cspan address=\"243117251.10.1016/j.lfs.2019.117251\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePourhamidi, K., Sk\u0026auml;rstrand, H., Dahlin, L. B., \u0026amp; Rolandsson, O. (2014). HSP27 concentrations are lower in patients with type 1 diabetes and correlate with large nerve fiber dysfunction. \u003cem\u003eDiabetes care\u003c/em\u003e, \u003cem\u003e37\u003c/em\u003e(3), e49\u0026ndash;50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2337/dc13-1780\u003c/span\u003e\u003cspan address=\"10.2337/dc13-1780\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDodge, M. E., Wang, J., Guy, C., Rankin, S., Rahimtula, M., \u0026amp; Mearow, K. M. (2006). Stress-induced heat shock protein 27 expression and its role in dorsal root ganglion neuronal survival. \u003cem\u003eBrain research\u003c/em\u003e, \u003cem\u003e1068\u003c/em\u003e(1), 34\u0026ndash;48. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.brainres.2005.11.008\u003c/span\u003e\u003cspan address=\"10.1016/j.brainres.2005.11.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, Y., Liu, J., Kong, Q., Cheng, H., Tu, F., Yu, P., Liu, Y., Zhang, X., Li, C., Li, Y., Min, X., Du, S., Ding, Z., \u0026amp; Liu, L. (2019). Cardiomyocyte-specific deficiency of HSPB1 worsens cardiac dysfunction by activating NFκB-mediated leucocyte recruitment after myocardial infarction. \u003cem\u003eCardiovascular research\u003c/em\u003e, \u003cem\u003e115\u003c/em\u003e(1), 154\u0026ndash;167. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/cvr/cvy163\u003c/span\u003e\u003cspan address=\"10.1093/cvr/cvy163\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorgenstern, R., Zhang, J., \u0026amp; Johansson, K. (2011). Microsomal glutathione transferase 1: mechanism and functional roles. \u003cem\u003eDrug metabolism reviews\u003c/em\u003e, \u003cem\u003e43\u003c/em\u003e(2), 300\u0026ndash;306. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3109/03602532.2011.558511\u003c/span\u003e\u003cspan address=\"10.3109/03602532.2011.558511\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZeng, B., Ge, C., Li, R., Zhang, Z., Fu, Q., Li, Z., Lin, Z., Liu, L., Xue, Y., Xu, Y., He, J., Guo, H., Li, C., Huang, W., Song, X., \u0026amp; Huang, Y. (2020). Knockdown of microsomal glutathione S-transferase 1 inhibits lung adenocarcinoma cell proliferation and induces apoptosis. \u003cem\u003eBiomedicine \u0026amp; pharmacotherapy\u0026thinsp;=\u0026thinsp;Biomedecine \u0026amp; pharmacotherapie\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e121109562.https://doi.org/10.1016/j.biopha.2019.109562\u003c/span\u003e\u003cspan address=\"121109562.10.1016/j.biopha.2019.109562\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBr\u0026auml;utigam, L., Zhang, J., Dreij, K., Spahiu, L., Holmgren, A., Abe, H., Tew, K. D., Townsend, D. M., Kelner, M. J., Morgenstern, R., \u0026amp; Johansson, K. (2018). MGST1, a GSH transferase/peroxidase essential for development and hematopoietic stem cell differentiation. \u003cem\u003eRedox biology\u003c/em\u003e, 17171\u0026ndash;17179. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.redox.2018.04.013\u003c/span\u003e\u003cspan address=\"10.1016/j.redox.2018.04.013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuang, F., Liu, J., Xie, Y., Tang, D., \u0026amp; Kang, R. (2021). MGST1 is a redox-sensitive repressor of ferroptosis in pancreatic cancer cells. \u003cem\u003eCell chemical biology\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e(6), 765\u0026ndash;775. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ee765.https://doi.org/10.1016/j.chembiol.2021.01.006\u003c/span\u003e\u003cspan address=\"e765.10.1016/j.chembiol.2021.01.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYan, J., Ye, G., \u0026amp; Shao, Y. (2022). High expression of the ferroptosis-associated MGST1 gene in relation to poor outcome and maladjusted immune cell infiltration in uterine corpus endometrial carcinoma. \u003cem\u003eJournal of clinical laboratory analysis\u003c/em\u003e, \u003cem\u003e36\u003c/em\u003e(4), e24317. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e.https://doi.org/10.1002/jcla.24317\u003c/span\u003e\u003cspan address=\".10.1002/jcla.24317\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, Y., Xu, X., Wang, X., Zhang, C., Hu, A., \u0026amp; Li, Y. (2023). MGST1 Expression Is Associated with Poor Prognosis, Enhancing the Wnt/β-Catenin Pathway via Regulating AKT and Inhibiting Ferroptosis in Gastric Cancer. \u003cem\u003eACS omega\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(26), 23683\u0026ndash;23694. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/acsomega.3c01782\u003c/span\u003e\u003cspan address=\"10.1021/acsomega.3c01782\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGanz, T. (2013). Systemic iron homeostasis. \u003cem\u003ePhysiological reviews\u003c/em\u003e, \u003cem\u003e93\u003c/em\u003e(4), 1721\u0026ndash;1741. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1152/physrev.00008.2013\u003c/span\u003e\u003cspan address=\"10.1152/physrev.00008.2013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLakhal-Littleton, S., Wolna, M., Carr, C. A., Miller, J. J., Christian, H. C., Ball, V., Santos, A., Diaz, R., Biggs, D., Stillion, R., Holdship, P., Larner, F., Tyler, D. J., Clarke, K., Davies, B., \u0026amp; Robbins, P. A. (2015). Cardiac ferroportin regulates cellular iron homeostasis and is important for cardiac function. \u003cem\u003eProceedings of the National Academy of Sciences of the United States of America\u003c/em\u003e, \u003cem\u003e112\u003c/em\u003e(10), 3164\u0026ndash;3169. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.1422373112\u003c/span\u003e\u003cspan address=\"10.1073/pnas.1422373112\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFang, X., Cai, Z., Wang, H., Han, D., Cheng, Q., Zhang, P., Gao, F., Yu, Y., Song, Z., Wu, Q., An, P., Huang, S., Pan, J., Chen, H. Z., Chen, J., Linkermann, A., Min, J., \u0026amp; Wang, F. (2020). Loss of Cardiac Ferritin H Facilitates Cardiomyopathy via Slc7a11-Mediated Ferroptosis. \u003cem\u003eCirculation research\u003c/em\u003e, \u003cem\u003e127\u003c/em\u003e(4), 486\u0026ndash;501. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/circresaha.120.316509\u003c/span\u003e\u003cspan address=\"10.1161/circresaha.120.316509\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu, Y., Jiang, L., Wang, H., Shen, Z., Cheng, Q., Zhang, P., Wang, J., Wu, Q., Fang, X., Duan, L., Wang, S., Wang, K., An, P., Shao, T., Chung, R. T., Zheng, S., Min, J., \u0026amp; Wang, F. (2020). Hepatic transferrin plays a role in systemic iron homeostasis and liver ferroptosis. \u003cem\u003eBlood\u003c/em\u003e, \u003cem\u003e136\u003c/em\u003e(6), 726\u0026ndash;739. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1182/blood.2019002907\u003c/span\u003e\u003cspan address=\"10.1182/blood.2019002907\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cardiovascular-toxicology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cato","sideBox":"Learn more about [Cardiovascular Toxicology](http://link.springer.com/journal/12012)","snPcode":"12012","submissionUrl":"https://submission.nature.com/new-submission/12012/3","title":"Cardiovascular Toxicology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"HSPB1, MGST1, Diabetic cardiomyopathy, ferroptosis, immune infiltration","lastPublishedDoi":"10.21203/rs.3.rs-5153598/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5153598/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDiabetic cardiomyopathy (DCM) is a leading cause of death in diabetic patients. Current therapies do not adequately resolve this problem and focus only on the optimal level of blood glucose for patients. Ferroptosis plays an important role in diabetes mellitus and cardiovascular diseases. However, the role of ferroptosis in DCM remains unclear. Differentially expressed ferroptosis-related genes (DE-FRGs) were identified by intersecting GSE26887 dataset and the Ferroptosis Database (FerrDb). The associations between the DE-FRGs and immune cells in DCM, estimated by CIBERSORTx algorithm, were analyzed. Using flow cytometry (FCM) to evaluated the infiltration of immune cells of myocardial tissues. The expression of DE-FRGs, Glutathione peroxidase 4 (GPX4) and Solute carrier family 7 member 11 (SLC7A11) were examined by real-time quantitative PCR and western blotting. 3 DE-FRGs were identified, which are Heat shock protein family B (small) member 1 (HSPB1), Microsomal glutathione S-transferase 1 (MGST1) and solute carrier family 40 member 1 (SLC40A1) respectively, and they were closely linked to immune cells in DCM. In vivo, the levels of CD8\u0026thinsp;+\u0026thinsp;T cells, B cells and Treg cells were significantly decreased in the DCM group, while the levels of CD4\u0026thinsp;+\u0026thinsp;T cells, M1 cells, M2 cells and monocytes were increased. Diabetes significantly decreased HSPB1 and MGST1 levels and increased ferroptosis\u003c/p\u003e \u003cp\u003ecompared to normal group. Furthermore, ferroptosis inhibitor ferrostatin-1 (Fer-1) alleviated high-fat diet (HFD)-induced cadiomyocyte injury and rescued the ferroptosis. This study suggests that ferroptosis related gene HSPB1 and MGST1 are closely related to immune cell infiltration, which may become therapeutic targets for DCM.\u003c/p\u003e","manuscriptTitle":"Down-regulation of HSPB1 and MGST1 promote ferroptosis and impact immune infiltration in diabetic cardiomyopathy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-09 07:03:32","doi":"10.21203/rs.3.rs-5153598/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-06T20:45:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-06T20:37:46+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-09-26T06:03:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cardiovascular Toxicology","date":"2024-09-25T16:37:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cardiovascular-toxicology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cato","sideBox":"Learn more about [Cardiovascular Toxicology](http://link.springer.com/journal/12012)","snPcode":"12012","submissionUrl":"https://submission.nature.com/new-submission/12012/3","title":"Cardiovascular Toxicology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f79869d6-b1fb-4e14-8caf-2b489a4926dd","owner":[],"postedDate":"December 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-03-10T19:55:10+00:00","versionOfRecord":{"articleIdentity":"rs-5153598","link":"https://doi.org/10.1007/s12012-025-09982-6","journal":{"identity":"cardiovascular-toxicology","isVorOnly":false,"title":"Cardiovascular Toxicology"},"publishedOn":"2025-03-07 15:57:07","publishedOnDateReadable":"March 7th, 2025"},"versionCreatedAt":"2024-12-09 07:03:32","video":"","vorDoi":"10.1007/s12012-025-09982-6","vorDoiUrl":"https://doi.org/10.1007/s12012-025-09982-6","workflowStages":[]},"version":"v1","identity":"rs-5153598","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5153598","identity":"rs-5153598","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}