Integrative multi-omics analysis of metabolite–protein interaction networks across different stages of coronary heart disease

Immuno-Metabolic Reprogramming in Metabolic Syndrome and Its Cardiovascular Complications: An Integrative Bioinformatics Study

Metabolic syndrome (MeS) is a major risk factor for cardiovascular disease and is characterized by chronic low-grade inflammation, immune dysregulation, and metabolic abnormalities. However, the molecular mechanisms linking MeS to diabetic coronary artery disease (DMCAD) remain incompletely understood. Publicly available peripheral blood mononuclear cell (PBMC) transcriptomic datasets of MeS and DMCAD were analyzed using an integrative bioinformatics approach. Differentially expressed genes (DEGs) were identified using the limma package, followed by functional enrichment, protein–protein interaction (PPI) network construction, weighted gene co-expression network analysis (WGCNA), gene set enrichment analysis (GSEA), and miRNA regulatory network analysis. Candidate genes were further evaluated using an independent type 2 diabetes mellitus (T2DM) dataset for external transcriptomic validation. Integrated analyses identified immune-inflammatory and immuno-metabolic pathways as central features of both MeS and DMCAD. Enrichment analyses highlighted cytokine signaling, leukocyte activation, chemotaxis, complement activation, oxidative stress, and vascular inflammatory responses. Network analyses identified CD86, CD33, CCR1, C5AR1, FPR1, CXCL16, and LILRA5 as key hub genes associated with immune regulation and cardiometabolic dysfunction. External transcriptomic validation supported the relevance of CD33, CD86, and LILRA5. miRNA network analysis identified members of the miR-17/92 family and miR-146a-5p as potential upstream regulators. TAM 2.0 enrichment analysis further linked these miRNAs to metabolic syndrome, diabetes mellitus, atherosclerosis, coronary heart disease, immune response, inflammation, and angiogenesis. Our findings suggest that coordinated immune-inflammatory and metabolic signaling networks contribute to the progression from MeS to DMCAD. The identified hub genes and miRNAs may serve as potential biomarkers and therapeutic targets for inflammation-driven cardiometabolic disease.

Metabolic Heterogeneity Across Heart Failure Subtypes Defined by Integrative Multi-Omics Analysis

Transcriptomics-based identification of shared biomarkers across type 2 diabetes, mild cognitive impairment, and uric acid metabolism

Uric acid metabolism is associated with the development of type 2 diabetes mellitus (T2DM), cardiometabolic, and cardiovascular diseases. Additionally, T2DM patients often exhibit mild cognitive impairment (MCI). However, the underlying mechanisms remain unclear. This study aims to identify and validate biomarkers associated with uric acid metabolism in T2DM and MCI, with the goal of discovering potential diagnostic and therapeutic targets to improve the quality of life for T2DM patients. Transcriptomic data for T2DM, MCI and uric acid metabolism-related genes were sourced from public databases. Biomarkers were screened using machine learning and validated for expression. Subsequent analyses included functional enrichment, immune infiltration, subcellular localization, and drug prediction. Three biomarkers—HP, ITGB3, and SELP—were identified. All showed significantly elevated expression in the T2DM group (p < 0.05). HP and ITGB3 were primarily enriched in ribosome-related pathways, primary immunodeficiency, and adherens junction processes. Immune infiltration analysis revealed that immature B cells and plasmacytoid dendritic cells were significantly enriched in T2DM. HP showed the strongest positive correlation with plasmacytoid dendritic cells (cor = 0.65, FDR <0.05), while ITGB3 exhibited the strongest positive correlation with immature B cells (cor = 0.76, FDR <0.05). Several potential therapeutic drugs were predicted, including calcifediol (score = −99.93) and meclofenamic acid (score = −99.89). This study identified three candidate biomarkers co-dysregulated across T2DM and MCI transcriptomes and associated with uric acid metabolism. Given the exploratory sample sizes, these findings are considered hypothesis-generating and require validation in larger independent cohorts.

Genetic insights into coronary heart disease: A multi-omics approach

Aim: Coronary heart disease (CHD) is a leading cause of mortality worldwide, with a complex interplay of genetic and environmental factors influencing its development. Genome-wide association studies (GWAS) have identified multiple genetic loci associated with CHD, providing crucial insights into its pathophysiology. However, the full spectrum of genetic contributors and their biological mechanisms remains to be elucidated. Methodology: This study integrates GWAS data with various ontology analyses to identify key genetic determinants of CHD. Variants associated with CHD were retrieved from public datasets and analysed using bioinformatics tools to explore their biological significance. Pathway enrichment, protein-protein interaction (PPI) networks, and clustering algorithms delineated functional relationships among candidate genes. Additionally, microRNA (miRNA) interactions were assessed to understand post-transcriptional regulatory mechanisms. Results: These findings revealed novel insights into CHD genetics, confirming known loci such as 9p21.3 (CDKN2B-AS1), COL4A2 and PHACTR1, while uncovering their broader functional roles in vascular remodelling, inflammation, and lipid metabolism. Enrichment and miRNA analyses highlighted new regulatory layers involving TGF-beta and AGE-RAGE pathways, and miRNAs like hsa-miR-147b and hsa-miR-4790-5p, suggesting previously unrecognized mechanisms in CHD pathogenesis. Interpretation: This study contributes to understanding CHD genetics by integrating multi-omic data to highlight relevant genetic factors and associated biological pathways. Key words: Coronary heart disease, Functional enrichment analysis, Genome-wide association studies, Genetic risk factors, Precision medicine

An Integrative Transcriptomic, Network Pharmacology, and Molecular Docking Analysis of the Ferroptosis–Fibrosis Axis in Cardiomyopathy with Exploratory Relevance to Diabetic Cardiomyopathy

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.

Common Molecular Mechanisms and Candidate Drug Targets in Type 2 Diabetes Mellitus and Atherosclerotic Cardiovascular Disease.

This study examined the mechanisms underlying the comorbidity between type 2 diabetes mellitus (T2DM) and atherosclerotic cardiovascular disease (ASCVD), while identifying potential therapeutic targets. Common differentially expressed genes (C-DEGs) between T2DM and ASCVD were extracted from the GSE78721 and GSE12288 datasets. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, protein-protein interaction (PPI) network construction, hub gene identification, and Drug-Gene Interaction Database (DGIdb) analysis were conducted. The association between hub C-DEGs and immune-infiltrating cells was analyzed using the CIBERSORT method. Expression levels of hub C-DEGs were quantified through qRT-PCR and Western blot analyses. A total of 32 C-DEGs were identified, comprising 20 upregulated and 12 downregulated genes. C-DEGs were predominantly enriched in key pathways, including viral myocarditis, arrhythmogenic right ventricular cardiomyopathy, hypertrophic cardiomyopathy, and dilated cardiomyopathy. PPI analysis revealed 29 nodes and 39 edges, leading to the identification of eight hub C-DEGs (HSP90B1, PLAU, SLPI, TOP3A, NCF4, PRF1, TUBA1C, and CS) across both datasets. Furthermore, hub C-DEGs (TOP3A, SLPI, NCF4, PRF1, and PLAU) demonstrated significant correlations with immune-infiltrating cell levels. Drugs specifically targeting these hub C-DEGs present promising candidates for the treatment of T2DM and ASCVD. Additionally, the expression of hub C-DEGs at both mRNA and protein levels was validated in patients with T2DM and ASCVD. An integrated bioinformatics analysis facilitated the screening of candidate therapeutic targets, mechanisms, and drugs for T2DM and ASCVD, offering new insights into molecular therapies for these conditions.