An Integrative Transcriptomic, Network Pharmacology, and Molecular Docking Analysis of the Ferroptosis-Fibrosis Axis...

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.