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Generation of a Compendium of Transcription Factor Cascades and Identification of Potential Therapeutic Targets Using
Sonish Sivarajkumar1,2, Romy Roy3, Pratyush Tandale4
1Molecular Robotics, Kochi 682022, Kerala, India.
Background:
Transcription factors (TFs) are critical regulators of gene expression, and their dysregulation is implicated in diseases like cancer. This study aims to create a comprehensive resource of TF cascades to identify potential therapeutic targets.
Methods:
We extracted TF interactions from the STRING database, constructed a knowledge graph using graph machine learning, and performed pathway enrichment analysis with Enrichr. Network analysis and PageRank identified influential TFs.
Results:
We generated 81,488 unique TF cascades, with the longest containing 62 TFs. Key TFs (e.g., MYC, TP53, STAT3) were identified, and enriched pathways included cancer-related processes. A knowledge graph and dataset were made publicly available.
Conclusions:
This compendium of TF cascades provides a valuable resource for understanding TF interactions and identifying novel drug targets for precision therapeutics.
Insights
This study maps transcription factor (TF) cascades, revealing key regulators like MYC and TP53. The findings offer a new resource for developing targeted cancer therapies.
Area of Science:
- Genomics
- Systems Biology
- Bioinformatics
Background:
- Transcription factors (TFs) regulate gene expression; their dysregulation is linked to diseases, notably cancer.
- Identifying TF regulatory networks is crucial for understanding disease mechanisms and developing treatments.
Purpose of the Study:
- To construct a comprehensive compendium of transcription factor cascades.
- To identify key TFs and associated biological pathways for potential therapeutic targeting.
Main Methods:
- TF interactions were sourced from the STRING database.
- A knowledge graph was built using graph machine learning, followed by pathway enrichment analysis.
- Network analysis and PageRank algorithm were employed to pinpoint influential TFs.
Main Results:
- A dataset of 81,488 unique TF cascades was generated, with the longest cascade involving 62 TFs.
- Influential TFs such as MYC, TP53, and STAT3 were identified.
- Enrichment analysis highlighted cancer-related biological pathways, and a knowledge graph was made publicly accessible.
Conclusions:
- The developed TF cascade compendium serves as a valuable resource for dissecting TF interactions.
- This resource facilitates the identification of novel drug targets for precision medicine approaches in cancer therapy.
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