Network pharmacology and bioinformatics analysis of themechanism of kaempferol in the treatment of breast cancer and construction of a mechanism-related prognostic model
Background and Aims:Kaempferol is a natural flavonoid compound that can regulate various processes and activities associated with cancer,such as the cell cycle,oxidative stress,apoptosis,proliferation,metastasis,and angiogenesis. Its potential for treating breast cancer has been validated in some studies,but its mechanism of action remains unclear. Therefore,this study was conducted to explore the potential targets and pathways of kaempferol in the treatment of breast cancer and establish a prognostic model.Methods:Using network pharmacology and bioinformatics approaches,the intersection targets of kaempferol for the treatment of breast cancer were obtained from databases such as HERB,GeneCards,STRING,PubChem,RCSB PDB,and TCGA. Protein interaction,GO analysis,and KEGG pathway enrichment analysis were performed,followed by molecular docking for validation. Prognostic-related genes were identified through LASSO-Cox regression analysis to establish a risk score model. The prognostic analysis for each gene,and the correlation between risk score and immune infiltration were analyzed. Results:Network pharmacology analysis identified 55 intersection targets. Protein interaction analysis revealed five potential key genes (Akt1,Bcl2,CASP3,ESR1,AR). GO analysis showed that kaempferol treatment of breast cancer involved 1604 biological process entries,20 cellular component entries,and 121 molecular function entries. KEGG pathway enrichment mainly included pathways related to chemical carcinogenesis,reactive oxygen species signaling,AGE-RAGE signaling in diabetic complications,TNF signaling,and IL-17 signaling. Molecular docking results indicated that kaempferol had a strong affinity for key targets,with the best binding effect observed with ESR1 (-9.1 kcal/mol). Ten prognostic genes (Bcl2,CYP1B1,DPP4,GSTM1,GSTM2,MMP1,NCOA2,NOS2,NR1I3,PTGS2) were obtained through LASSO-Cox regression analysis,and a risk score model was established. The AUC of this model for predicting breast cancer patient prognosis was greater than 0.5. Single-gene survival analysis indicated that higher expression of Bcl2 (HR=0.61,95％ CI=0.43-0.86,P=0.005),CYP1B1 (HR=0.68,95％ CI=0.49-0.94,P=0.022),GSTM1 (HR=0.68,95％ CI=0.47-0.98,P=0.037),GSTM2 (HR=0.64,95％ CI=0.46-0.90,P=0.010),and PTGS2 (HR=0.62,95％ CI=0.44-0.86,P=0.005) correlated with a higher overall survival (OS),while higher expression of MMP1 (HR=1.72,95％ CI=1.22-2.41,P=0.002),NCOA2 (HR=1.71,95％ CI=1.12-2.60,P=0.013),NOS2 (HR=1.67,95％ CI=1.20-2.32,P=0.002),and NR1I3 (HR=1.69,95％ CI=1.21-2.37,P=0.002) was associated with worse OS. The risk score was negatively correlated with the infiltration of T cells,CD8+T cells,myeloid dendritic cells,NK cells,and B cells,and positively correlated with monocyte/macrophage infiltration (all P<0.05).Conclusion:Kaempferol exerts therapeutic effects on breast cancer through multiple targets and pathways. The prognostic genes identified based on its related targets,along with the established prognostic model,can guide clinical treatment of breast cancer. The correlation between the prognostic model and immune infiltration provides direction for future experimental studies.
Breast NeoplasmsKaempferolNetwork PharmacologyComputational Biology
维普
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