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A Computational Strategy to Identify Hub Genes in Pathway Analysis of Gamma Tocotrienol-treated MCF-7 Cells and
Dalli Kumari1,2, Govindappa Nagendra1,2, Kuruvalli Gouthami3
1Department of Chemistry, REVA University, Rukmini Knowledge Park, Kattigenahalli, Yelahanka, Bangalore, 560064, Karnataka, India.
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.
Insights
Natural compounds Quercetin and Kaempferol show promise for breast cancer treatment. Computational analysis identified these phytochemicals as potential therapeutic agents targeting key genes, offering a safer, plant-based alternative to synthetic drugs.
Area of Science:
- Bioinformatics
- Computational Biology
- Phytochemistry
Background:
- Breast cancer is a prevalent malignancy requiring novel therapies.
- Medicinal plants offer safe, cost-effective anticancer agents with lower toxicity.
- Research into plant-derived compounds is crucial for developing new breast cancer treatments.
Purpose of the Study:
- To identify potential biomarkers and signaling pathways in breast cancer using bioinformatics.
- To computationally screen phytochemicals for anticancer properties.
- To evaluate natural compounds as potential lead compounds for breast cancer drug discovery.
Main Methods:
- Bioinformatic analysis of Gene Expression Omnibus (GEO) dataset GSE21946.
- Gene Ontology and KEGG pathway analysis to identify significant pathways.
- Protein-protein interaction (PPI) network analysis to identify key gene candidates (EGR1, JUN, SREBF1, TGIF1).
- Computational screening of 10 phytochemicals using SwissADME and admetSAR servers.
- Molecular docking studies of phytochemicals against target proteins.
Main Results:
- Differential gene expression analysis revealed 250 differentially expressed genes (DEGs).
- Four key hub genes (EGR1, JUN, SREBF1, TGIF1) were identified through PPI network analysis.
- Quercetin and Kaempferol demonstrated strong binding affinities and H-bond interactions with all four target proteins in docking studies.
- These natural compounds showed higher efficacy compared to FDA-approved drugs.
Conclusions:
- Quercetin and Kaempferol are potent bioactive compounds with potential for breast cancer therapy.
- These findings support the development of traditional medicine-based therapeutic strategies.
- The study provides insights for lead optimization in breast cancer drug discovery.
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