Generation of a Compendium of Transcription Factor Cascades and Identification of Potential Therapeutic Targets Using Graph Machine Learning

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.