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Outline
The aim of this study is to explore the bioinformatics-based screening of natural active ingredients for chronic atrophic gastritis (CAG) from the perspective of ferroptosis using machine learning and network pharmacology, thereby opening a new horizon for the treatment of CAG.
Datasets related to CAG were retrieved from the GEO database, and ferroptosis-related targets were collected from the FerrDb database. The R package GEO-query was used to obtain the expression profiles and clinical information of the CAG-related dataset GSE27411, followed by screening of differential targets and CAG differentially expressed targets were used to construct a protein-protein interaction (PPI) network, and the phenotypes of the key targets were analyzed using the VarElect tool. Support vector machine (SVM), a machine learning algorithm, was employed to further screen disease target genes. These natural active ingredients take the overlapping targets of ferroptosis and CAG as their potential action targets. Gene Ontology (GO) functional enrichment analysis and WikiPathwayss enrichment analysis were performed on the potential targets using the R package clusterProfiler. LASSO regression was utilized to screen natural active ingredients related to ferroptosis and CAG. The Coremine database was used to identify small-molecule drugs, and the TCMSP database was employed to find the natural drugs from which these small-molecule natural products are derived as well as their corresponding targets, followed by molecular docking analysis.
The GSE27411 dataset was selected for analysis. After differential expression analysis, 993 CAG-related differential targets were identified, along with 455 ferroptosis-related targets. A total of 29 overlapping targets were obtained through intersection analysis. Functional enrichment analysis revealed that these overlapping targets were associated with lipids and therosclerosis, reactive oxygen species (ROS), the RIG-I-like receptor signaling pathway, the FoxO signaling pathway, among others. Seven diagnostic genes were identified via LASSO-COX regression analysis: JUN, EGFR, BRD4, ATG5, BACH1, QSOX1, and NR1D2. Coremine identified several small-molecule drugs, including Apabetalone, cobaltiprotoporphyrin, sulmazole, 4-(4-fluorophenyl)-2-(4-hydroxyphenyl)-5-(4-pyridyl) imidazole, tin protoporphyrin IX, Chloroquine and Gefitinib, all of which exhibited favorable molecular docking result. Among these, cobaltiprotoporphyrin showed the optimal docking performance, suggesting its potential significance for CAG treatment.
Through bioinformatics approaches and drug regularity analysis, this study provides basic theoretical and data support for the treatment of CAG from the perspective of ferroptosis, and offers a new entry point for the search of therapeutic drugs targeting CAG.
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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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