CancerGeneNet

CancerGeneNet maps causal relationships between gene products frequently altered in cancers and cancer phenotypes using advanced graph algorithms to infer pathways that link cancer driver mutations to the "hallmarks of cancer".

Key Features:

• Curation integration: Integrates a significant portion of gene products altered in cancer into a comprehensive network of causal protein relationships.
• Graph algorithms: Utilizes advanced graph algorithms to infer probable pathways of causal interactions linking cancer-associated genes to specific cancer phenotypes.
• Interaction layering: Connects proteins whose activities are influenced by cancer driver mutations to proteins that directly impact the "hallmarks of cancer".
• Curated pathway annotation: Annotates curated pathways that help rationalize the pathological consequences of cancer driver mutations in various common cancers.
• MiniPathways: Introduces "MiniPathways" that illustrate regulatory circuits frequently disrupted across different types of cancers.

Scientific Applications:

• Pathway inference: Map causal pathways linking cancer-associated genes to specific cancer phenotypes.
• Mechanistic rationalization: Rationalize the pathological consequences of cancer driver mutations across common cancers using curated pathway annotations.
• Regulatory circuit analysis: Identify regulatory circuits frequently disrupted across different cancer types via "MiniPathways".
• Therapeutic target nomination: Provide a rational framework to develop strategies aimed at reversing disease phenotypes and identifying potential therapeutic targets.

Methodology:

Extensive curation integrates gene products into a network of causal protein relationships; advanced graph algorithms infer probable causal interaction pathways linking genes to cancer phenotypes; annotation produces curated pathways and "MiniPathways".