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Войти в исследования Current medicinal chemistry · 2026
A Computational Strategy to Identify Hub Genes in Pathway Analysis of Gamma Tocotrienol-treated MCF-7 Cells and Molecular Docking Study Using Selected Phytochemicals as Therapeutic Agents.
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Kumari Dalli, Nagendra Govindappa, Gouthami Kuruvalli, Damodara Reddy Vaddi
Аннотация
INTRODUCTION: Breast cancer is a highly prevalent malignancy in women, necessitating the discovery of novel therapeutic approaches. Researchers have focused on various medicinal plants for their therapeutic benefits, including anti-carcinogenic properties. These plants are abundant in nature, generally safe, cost-effective, and exhibit lower toxicity compared to currently available synthetic cancer treatments.
MATERIALS AND METHODS: This study employed a bioinformatic approach to identify potential biomarkers and signaling pathways. Analysis of Gene Expression Omnibus (GEO) dataset GSE21946 (Gamma tocotrienol-treated MCF-7 cells) revealed 250 differentially expressed genes (DEGs), including 175 upregulated and 75 downregulated genes. Gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses identified significant functional pathways and candidate genes. Protein- protein interaction (PPI) network analysis further revealed four key gene candidates: EGR1, JUN, SREBF1, and TGIF1.
RESULTS: This study also evaluated the identification of potent bioactive compounds through computational screening of 10 phytochemicals with established anticancer properties to inform the development of effective breast cancer therapies. Using the SwissADME and admetSAR online servers, these phytochemicals were assessed for pharmacokinetic properties and pharmacophore features.
DISCUSSION: Docking studies were conducted with the four hub gene targets. The results indicated that Quercetin (-6.1 to -7.7 Kcal/mol) and Kaempferol (-6.0 to -7.3 Kcal/mol) exhibited the highest negative binding affinities and strongest H-bond interactions with all four targeted proteins when compared to FDA-approved standard drugs Alpelisib, Capivasertib, Elacestrant, and Silibinin.
CONCLUSION: These findings may facilitate the development of traditional medicinebased therapeutic strategies and provide insights for potential lead optimization in breast cancer drug discovery.
MATERIALS AND METHODS: This study employed a bioinformatic approach to identify potential biomarkers and signaling pathways. Analysis of Gene Expression Omnibus (GEO) dataset GSE21946 (Gamma tocotrienol-treated MCF-7 cells) revealed 250 differentially expressed genes (DEGs), including 175 upregulated and 75 downregulated genes. Gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses identified significant functional pathways and candidate genes. Protein- protein interaction (PPI) network analysis further revealed four key gene candidates: EGR1, JUN, SREBF1, and TGIF1.
RESULTS: This study also evaluated the identification of potent bioactive compounds through computational screening of 10 phytochemicals with established anticancer properties to inform the development of effective breast cancer therapies. Using the SwissADME and admetSAR online servers, these phytochemicals were assessed for pharmacokinetic properties and pharmacophore features.
DISCUSSION: Docking studies were conducted with the four hub gene targets. The results indicated that Quercetin (-6.1 to -7.7 Kcal/mol) and Kaempferol (-6.0 to -7.3 Kcal/mol) exhibited the highest negative binding affinities and strongest H-bond interactions with all four targeted proteins when compared to FDA-approved standard drugs Alpelisib, Capivasertib, Elacestrant, and Silibinin.
CONCLUSION: These findings may facilitate the development of traditional medicinebased therapeutic strategies and provide insights for potential lead optimization in breast cancer drug discovery.