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Outline
To systematically elucidate the molecular mechanism of Herba Abri intervened liver fibrosis via regulating ferroptosis using network pharmacology, molecular docking, bioinformatics, and machine learning approaches.
Active components of Abri Herba were screened via the TCMSP database. Liver fibrosis and ferroptosis-related genes were retrieved from GEO and FerrDb databases. R language was employed for batch correction, differential analysis, and weighted gene co-expression network analysis (WGCNA). Core genes were identified by intersecting targets of Abri Herba, liver fibrosis, and ferroptosis. Protein-protein interaction (PPI) networks and "drug-component-target-disease" network were constructed using STRING and Cytoscape. Functional enrichment analyses (GO and KEGG) were conducted to identify core pathways. Feature genes were screened via random forest and XGBoost algorithms, validated by Mendelian randomization (MR) analysis and receiver operating characteristic (ROC) curves, followed by molecular docking.
(1) Twelve active components and 301 predicted targets of Herba Abri were identified. Batch correction and principal component analysis (PCA) integrated 17 731 liver fibrosis-related genes and 396 ferroptosis-related genes, obtaining 27 intersection targets. (2) GO analysis revealed enrichment in oxidative stress response and nuclear receptor activity. KEGG pathways were enriched in cancer metabolism (hsa05207) and PPAR signaling (hsa03320). (3) Machine learning (random forest and XGBoost) prioritized signature genes AR and FABP4. (4) Validation via ROC curves demonstrated AUC values were 0.591 for AR and 0.623 for FABP4, supported by MR analysis. (5) Molecular docking confirmed stable interactions between AR, FABP4 and active components of Herba Abri.
Abri Herba may inhibit ferroptosis in liver fibrosis by regulating oxidative stress and lipid metabolism pathways to inhibit ferroptosis and thereby suppress liver fibrosis through AR and FABP4, providing novel evidence for the multi-target therapeutic potential of traditional Chinese medicine.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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