Integrating machine learning with total network controllability analysis to identify therapeutic targets for cancer treatment (IMAGE)

The Hong Kong Polytechnic University

Integrating machine learning with total network controllability analysis to identify therapeutic targets for cancer treatment

Caption

Control hubs or cancer-keeper genes (CKGs) in the gene regulatory network (GRN) (A) Topological structure of GRN. Node size is proportional to node degree. The network contains 7,030 genes and 103,360 directed interactions. (B) Proportions of the driver nodes, control hubs, and sensitive control hubs in GRN. (C) Node degrees of different types of genes in GRN. Control hubs have a significantly higher average degree than the other genes. (D) Control hubs of GRN are enriched in the context of essentiality, evolutionary conservation, cell signaling, protein abundance, posttranslational modifications (PTMs), regulators of cell proliferation, diseases, virus targets, drug targets, and immune regulation. (E) Proportions of sensitive CKGs and CKGs in ten key cancer signaling pathways with significant genetic variations. Most of the genes in these pathways are CKGs. (F) Two TCGA-analyzed signaling pathways of BLCA. FGFR3 and RPS6KA3, two control hubs, reside upstream of the most mutated genes in the pathways. Removing the edge from FGFR3 to RPS6KA3 will change their node types in the control scheme and make them sensitive CKGs.

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© 2025 Research and Innovation Office, The Hong Kong Polytechnic University. All Rights Reserved.

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