An Integrative Transcriptomic, Network Pharmacology, and Molecular Docking Analysis of the Ferroptosis–Fibrosis Axis in Cardiomyopathy with Exploratory Relevance to Diabetic Cardiomyopathy
The findings are consistent with a fibrosis-dominant remodeling signature and suggest potential network-level links between ferroptosis-associated processes and cardiac fibrosis and require validation in independent cohorts and experimental studies.
Abstract
Background: Diabetic cardiomyopathy (DCM) is characterized by metabolic dysfunction, inflammation, extracellular matrix (ECM) remodeling, and myocardial fibrosis. Increasing evidence suggests that ferroptosis-associated oxidative injury may contribute to cardiac remodeling; however, the interaction between ferroptosis-related pathways and fibrosis-associated molecular networks remains incompletely understood. This study explored the ferroptosis–fibrosis axis using an integrative transcriptomic and systems pharmacology framework. Methods: Differentially expressed genes were identified from the GSE5406 myocardial transcriptomic dataset comparing nonfailing donor hearts with ischemic and idiopathic cardiomyopathy samples and analyzed using functional enrichment, protein–protein interaction, and disease-association approaches. Cross-dataset comparison and exploratory sample-level external evaluation were performed using the independent GSE263297 DCM-related dataset. Candidate genes were further evaluated by receiver operating characteristic (ROC) analysis and machine learning-based feature selection using least absolute shrinkage and selection operator (LASSO), random forest, and support vector machine-recursive feature elimination (SVM-RFE). Representative compounds associated with fibrosis-, oxidative stress-, inflammation-, and ferroptosis-related pathways were subsequently assessed by molecular docking against TGFBR1, STAT3, GPX4, AKT1, SMAD3, and ACSL4. Results: Transcriptomic analyses highlighted ECM organization, collagen-containing ECM, and fibrosis-related pathways as dominant biological themes. Cross-dataset comparison showed partial preservation of transcriptional patterns between independent myocardial cohorts, with 20 of 51 evaluated genes demonstrating concordant expression direction across datasets. ROC analysis identified LUM and ASPN as having the highest area under the curve (AUC) values among candidate genes, whereas COL1A1, COL1A2, and COL3A1 also showed elevated AUC values. Machine learning analyses identified FCN3, HOPX, CNN1, and GLUL as the core signature consistently prioritized across all three algorithms, whereas LUM was additionally identified by two of three algorithms. Internal validation yielded a cross-validated AUC of 0.934 (95% CI: 0.820–1.000), and exploratory sample-level external evaluation of the four-gene signature in GSE263297 yielded an AUC of 0.673 (95% CI: 0.380–0.967). Exploratory docking analyses suggested potential structural compatibility between several candidate compounds and fibrosis-, inflammation-, and ferroptosis-associated targets, with comparatively lower predicted binding-energy values observed for selected ligand–target combinations. Conclusions: The findings are consistent with a fibrosis-dominant remodeling signature and suggest potential network-level links between ferroptosis-associated processes and cardiac fibrosis. These observations should be regarded as exploratory and hypothesis-generating and require validation in independent cohorts and experimental studies.
Globally, myocardial infarction (MI) remains a major cause of morbidity and mortality. Macrophage-mediated inflammation and ferroptosis-related stress responses have both been implicated in MI; however, transcriptomic identification of macrophage- and ferroptosis-related candidate genes requires rigorous control of false-positive findings. In this revised study, two peripheral blood transcriptomic datasets, GSE29532 and GSE48060, were integrated after probe annotation, independent normalization, merging based on common genes, and ComBat batch correction. Batch-correction quality was evaluated using both visualization and quantitative metrics. Differential expression analysis was performed using the limma package with adj.P.Val < 0.05 as the primary threshold. A total of 213 differentially expressed genes were identified, including 101 upregulated and 112 downregulated genes. Intersecting FDR-supported DEGs with macrophage-related and ferroptosis-related gene sets identified seven primary MFRDEGs: SMAD7, MMD, PTPN6, DDIT3, AKR1C3, PHF21A, and ACSL1. GO enrichment analysis was interpreted as exploratory functional annotation because of the small input gene set, whereas whole-ranked-gene GSEA highlighted TNFA/NF-kB inflammatory signaling, complement-related innate immune signaling, and reactive oxygen species-related transcriptional programs. Because the revised PPI analysis did not produce reliable interactions among the seven primary MFRDEGs, candidate gene prioritization was performed instead of defining interaction-derived hub genes. ssGSEA suggested a robust neutrophil-related alteration in MI, while macrophage- and monocyte-related signatures showed trend-level changes after FDR correction. Exploratory ROC analysis was performed for selected candidate genes. RT-qPCR validation further showed that SMAD7, PTPN6, DDIT3, PHF21A, and ACSL1 were increased, whereas MMD and AKR1C3 were decreased in AMI peripheral blood samples, consistent with the transcriptomic results. These findings provide FDR-supported candidate-level evidence linking macrophage- and ferroptosis-related transcriptional alterations to immune signatures in MI and warrant further validation in larger cohorts and mechanistic studies.
Guo-Qin Wang, Zhulin Zhang, Guan-Rui Yang et al.· PLoS ONE· 0 citations
Diabetic cardiomyopathy (DCM) is a critical pathological driver of heart failure in diabetic patients, primarily characterized by progressive myocardial fibrosis. Nevertheless, the core molecular network linking upstream metabolic dysregulation to the aberrant activation of downstream cardiac fibroblasts remains largely elusive. In this study, we combined in vivo and in vitro approaches with bioinformatics analysis. A high‐fat diet‐induced mouse model of diabetic myocardial fibrosis was established, and transcriptome sequencing was performed to screen for hub genes, which were subsequently validated in two independent DCM datasets. Single‐cell RNA sequencing revealed that PDK4 and ACOT1 were upregulated in cardiac fibroblasts under pathological conditions. In vitro experiments confirmed that high glucose induced the expression of PDK4, ACOT1, and fibrotic markers in human primary cardiac fibroblasts. Molecular docking predicted a potential interaction between PDK4 and ACOT1. Collectively, our findings identify PDK4 and ACOT1 as evolutionarily conserved metabolic hub genes associated with myocardial fibrosis in DCM, suggesting a putative “metabolism–fibrosis axis” and providing potential therapeutic targets.
Huan Liu, Guanming Qi, Sheng-Rong Ouyang et al.· Journal of Diabetes Research· 0 citations
Dilated cardiomyopathy (DCM) is characterized by left ventricular dilation and systolic dysfunction and is associated with mitochondrial dysfunction and immune-inflammatory activation. However, aging-related molecular signatures and mitochondrial regulatory pathways in DCM remain incompletely understood. This study analyzed six bulk transcriptomic datasets and one single-cell RNA sequencing dataset from the Gene Expression Omnibus database. After data normalization, batch correction, and cell-type annotation, aging- and mitochondria-related candidate genes were identified using differential expression analysis, weighted gene co-expression network analysis, and protein-protein interaction network construction. Core genes were further screened using least absolute shrinkage and selection operator regression, random forest, and support vector machine-recursive feature elimination. Immune-cell infiltration, cell-cell communication, and molecular subtyping analyses were performed to characterize the cardiac immune microenvironment in DCM. A total of 66 aging-related genes and 16 mitochondria-related genes were associated with DCM and were mainly enriched in hypoxia-inducible factor-1 signaling, oxidative phosphorylation, and nitric oxide synthase-related pathways. Machine learning and single-cell RNA sequencing analyses identified SERPINE1, TGFB2, CYBB, and TLR2 as core genes. CYBB and TLR2 were highly expressed in monocytes and macrophages, whereas SERPINE1 and TGFB2 were predominantly expressed in stromal cells. Immune landscape analysis showed increased pro-inflammatory macrophage activation and altered cell-cell communication in DCM samples. Based on core gene expression, DCM samples were divided into two molecular subtypes associated with vascular endothelial growth factor signaling and primary bile acid biosynthesis, respectively. This protocol provides an integrated framework for identifying candidate biomarkers and molecular subtypes in DCM.
Yuna Zhou· Journal of Visualized Experi...· 0 citations
INTRODUCTION
Characterized as an idiopathic primary myocardial disorder, dilated cardiomyopathy (DCM) predominantly affects children and elderly adults. However, a lack of specific clinical symptoms and reliable biomarkers impedes timely diagnosis and rational clinical management of DCM.
METHODS
Whole-genome expression profiles (GSE120895, GSE9800) were retrieved from the Gene Expression Omnibus database via the GEOquery R package. A series of bioinformatic methods were employed, including DEG, GSVA, WGCNA, GO/KEGG enrichment, PPI, and immune infiltration analysis. Key biomarkers were validated by qRT-PCR in a Doxorubicin (DOX)-induced DCM model.
RESULTS
In total, 629 differentially expressed genes (DEGs) were screened out between DCM and control groups. Combined analysis of DEGs and WGCNA outputs identified 13 hub genes overlapping with oxidative stress-associated gene modules. Receiver Operating Characteristic (ROC) curve analyses confirmed that these hub genes exhibit favorable diagnostic efficiency for DCM. Functional enrichment results showed that these genes are mainly enriched in transmembrane transport and nucleotide metabolism pathways. Immune infiltration analysis indicated significantly elevated infiltration levels of five immune cell subsets in DCM myocardial tissues. In vivo experiments verified the significant upregulation of five core hub genes in DOX-induced DCM mice. By screening hub genes with diagnostic potency based on public transcriptome datasets and validating their expression alterations in a DOX-induced DCM animal model, this study provides partial experimental evidence to support the above bioinformatic outcomes.
DISCUSSION
Through integrative analysis of multiple datasets and molecular biology validation, this study provides robust evidence supporting the involvement of these hub genes in DCM pathogenesis. Although validation was limited to a single murine model, the findings lay the groundwork for future mechanistic studies and clinical exploration of these candidate genes.
CONCLUSION
Hub genes including MVP, WISP1, FCN1, AMPD3, RARRES1, FTL, and KRT14 possess promising auxiliary diagnostic potential, which provides novel clues for subsequent clinical evaluation research on DCM.
Background Diabetes markedly increases the risk of heart failure, yet the molecular signals connecting metabolic stress, myocardial fibrosis, and impaired cardiac repair remain incompletely understood. Methods We integrated bulk transcriptomic data from postischemic hearts (GSE26887), fibrosis‐related genes from the CTD database, protein–protein interaction (PPI) analysis, and pathway enrichment (GO, KEGG, GSEA, and GSVA) to identify candidate mediators in diabetic versus nondiabetic heart failure. Single‐cell RNA‐sequencing datasets were analyzed with Seurat to map the cellular distribution of key genes. Pan‐cancer analyses using TCGA cohorts were performed to evaluate prognostic, immune, and tumor mutation burden (TMB) correlations. Mechanistic validation was conducted in human cardiac progenitor cells (CPCs) exposed to normoglycemia or high glucose with S100A9 knockdown or overexpression, recombinant S100A8/A9, and a neutralizing S100A9 antibody. Cell viability (CCK‐8); qPCR panels for fibrosis, inflammation, and metabolic genes; ROS production (DCF‐DA and MitoSOX); and mitochondrial respiration were quantified. Results Differential expression and PPI network analyses identified S100A8 as a fibrosis‐related hub specifically enriched in diabetic heart failure. Single‐cell mapping revealed predominant S100A8 expression in CPCs rather than mature cardiomyocytes. Pathway analyses linked S100A8 to collagen fibril organization, ECM–receptor interaction, oxidative phosphorylation, and fatty acid β‐oxidation. Functionally, high glucose upregulated S100A8/S100A9 and profibrotic and proinflammatory genes in CPCs, increased total and mitochondrial ROS, and reduced basal, ATP‐linked, and maximal respiration and spare capacity. S100A9 knockdown partially restored CPC proliferation, redox balance, and mitochondrial function, whereas S100A9 overexpression or recombinant S100A8/A9 further exacerbated oxidative stress and bioenergetic failure; S100A9 neutralization attenuated these effects. Pan‐cancer analyses showed that high S100A8 expression was associated with adverse prognosis, altered immune infiltration, and increased TMB in several TCGA cohorts. Conclusions S100A8/S100A9 emerges as a central mediator linking hyperglycemia‐induced oxidative stress, metabolic inflexibility, and fibrotic reprogramming of CPCs, thereby promoting diabetic heart failure. S100A8/S100A9 may serve as a biomarker and therapeutic target at the interface of immunometabolism, cardiac regeneration, and cardio‐oncology.