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To analyze iron death genes and related pathogenesis in chronic thromboembolic pulmonary hypertension (CTEPH) based on bioinformatics, and to screen potential traditional Chinese medicine (TCM) active ingredients for treating CTEPH through iron death related pathways.
The differentially expressed genes in dataset GSE130391 were analyzed by R language, and the genes related to iron death were obtained from FerrDB database. The intersection of the two genes was selected, and the intersection genes were enriched by Kyoto encyclopedia of genes and genomes (KEGG) and gene ontology (GO). The intersection genes were analyzed by random forest algorithm, and the key genes were obtained. The immune infiltration analysis of GSE130391 was performed by cibesort algorithm. Potential TCM active ingredients were screened by cMAP database, and the binding stability and affinity of TCM active ingredients and key targets were analyzed by molecular docking and molecular dynamics simulation.
A total of 878 DEGs were obtained, 264 iron death related genes and 14 intersection genes were obtained from FerrDB database. There were 630 items in GO enrichment analysis, and 13 pathways were enriched by KEGG. Three key genes were obtained by random forest algorithm. Immunoinfiltration analysis showed that dendritic cells and mast cells were inhibited in CTEPH group, and immunoinfiltration correlation showed that mast cells were strongly correlated with M1 macrophages, M1 macrophages were strongly correlated with T cells, and the key gene arachidonic acid 12-lipoxygenase 12R type (ALOX12B) was positively correlated with M2 macrophages. Cytokine signal transduction inhibitor 1 (SOCS1) was negatively correlated with M2-type macrophages. The active ingredients of traditional Chinese medicine were ononanthine and rotensin screened in cMAP database. Molecular docking and molecular dynamics simulation analysis showed that rotensin and ALOX12B had stable binding energy and strong affinity.
The therapeutic targets related to iron death in CTEPH are found by bioinformatics method and the active components of Chinese medicine that can be targeted for intervention are screened.
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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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