Bioinformatic analysis and experimental validation identify prognostic hub genes associated with apigenin response in breast cancer

Abstract Background Breast cancer (BC) has emerged as the second most common cancer globally, making the exploration of its molecular mechanisms and novel therapeutic targets an urgent priority. Objectives This study aimed to identify key genes mediating the anti-BC effects of apigenin through bioinformatics analysis. Methods Weighted gene co-expression network analysis (WGCNA) was used to identify BC-associated gene modules (turquoise, yellow, and brown). Genes from these modules were intersected with differentially expressed genes (DEGs) from apigenin-treated BC cells, and then protein-protein interaction (PPI) analysis was performed to identify hub genes. The TCGA‑BRCA dataset, the Gene Expression Profiling Interactive Analysis (GEPIA), and the Human Protein Atlas were employed for expression validation, as well as for prognostic and immune-infiltration analyses. KEGG enrichment analysis, molecular docking, and Western blotting were carried out to elucidate pathways, ligand-binding affinities, and the changes of protein-level expression. Results The intersection analysis and PPI screening jointly identified 10 hub genes. Validation using TCGA-BRCA, GEPIA, and the Human Protein Atlas confirmed that these genes exhibited distinct expression patterns between tumor and normal tissues, along with significant associations with survival outcomes and immune-cell infiltration. KEGG enrichment analysis suggested that these hub genes are involved in arginine and proline metabolism, as well as in cancer-related pathways. Molecular docking and Western blotting demonstrated that apigenin exhibits high binding affinity for IPO4, RRP9, and METTL1, and that the downregulation of their protein expression accompanies this. Conclusion The 10 hub genes could serve as potential prognostic biomarkers and candidate therapeutic targets for apigenin-based BC therapy, thereby providing mechanistic insights into the antitumor effects mediated by this traditional Chinese medicine (TCM) monomer.

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Publication Details

Journal
Research Square
Published
2026-11-03
DOI
https://doi.org/10.21203/rs.3.rs-11049725/v1
Primary Topic
Flavonoids in Medical Research
Type
preprint
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preprint

Bioinformatic analysis and experimental validation identify prognostic hub genes associated with apigenin response in breast cancer

Hang Gao, Zhengrong Zhang, Yu Sheng, Hang Song et al.
Research Square
Flavonoids in Medical Research
preprint

Bioinformatic analysis and experimental validation identify prognostic hub genes associated with apigenin response in breast cancer

Hang Gao, Zhengrong Zhang, Yu Sheng, Hang Song, Yang Ge, Fei Lu, Fengyuan Chen
preprint en

Abstract

Abstract Background Breast cancer (BC) has emerged as the second most common cancer globally, making the exploration of its molecular mechanisms and novel therapeutic targets an urgent priority. Objectives This study aimed to identify key genes mediating the anti-BC effects of apigenin through bioinformatics analysis. Methods Weighted gene co-expression network analysis (WGCNA) was used to identify BC-associated gene modules (turquoise, yellow, and brown). Genes from these modules were intersected with differentially expressed genes (DEGs) from apigenin-treated BC cells, and then protein-protein interaction (PPI) analysis was performed to identify hub genes. The TCGA‑BRCA dataset, the Gene Expression Profiling Interactive Analysis (GEPIA), and the Human Protein Atlas were employed for expression validation, as well as for prognostic and immune-infiltration analyses. KEGG enrichment analysis, molecular docking, and Western blotting were carried out to elucidate pathways, ligand-binding affinities, and the changes of protein-level expression. Results The intersection analysis and PPI screening jointly identified 10 hub genes. Validation using TCGA-BRCA, GEPIA, and the Human Protein Atlas confirmed that these genes exhibited distinct expression patterns between tumor and normal tissues, along with significant associations with survival outcomes and immune-cell infiltration. KEGG enrichment analysis suggested that these hub genes are involved in arginine and proline metabolism, as well as in cancer-related pathways. Molecular docking and Western blotting demonstrated that apigenin exhibits high binding affinity for IPO4, RRP9, and METTL1, and that the downregulation of their protein expression accompanies this. Conclusion The 10 hub genes could serve as potential prognostic biomarkers and candidate therapeutic targets for apigenin-based BC therapy, thereby providing mechanistic insights into the antitumor effects mediated by this traditional Chinese medicine (TCM) monomer.

Research Square
Anhui University of Traditional Chinese Medicine (CN)
Flavonoids in Medical Research
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