GCA, GPCR Cancer Axes

GCA, GPCR Cancer Axes identifies dysregulated GPCR receptor–ligand–enzyme interaction networks in cancer transcriptomics to reveal clinically relevant signaling axes.

Key Features:

• Interaction Network Derivation: Constructs networks linking GPCRs to cognate ligands and biosynthetic enzymes across cancer datasets to reveal differential regulation among cancer contexts.
• Pathway Enrichment Analysis: Analyzes biosynthetic enzyme expression to identify pathway enrichment that mirrors metabolomics-derived activity signatures as surrogates for GPCR responses to organic ligands.
• Patient Survival Correlation: Integrates receptor, ligand, or enzyme expression with patient survival data to identify significant associations and improve patient stratification, indicating combined effects of activated GPCR networks on cancer phenotypes.
• Identification of Actionable Axes: Detects signaling axes involving GPCRs and neurotransmitter biosynthetic enzymes across molecular subtypes, including muscarinic, adenosine, 5-hydroxytryptamine, and chemokine receptors, and links them to drugs with anti-growth effects in large-scale cancer cell drug screens.

Scientific Applications:

• Therapeutic Target Discovery: Prioritizes GPCR signaling axes associated with patient survival and growth inhibition as candidate therapeutic targets in oncology.
• Cancer Subtype-Specific Insights: Provides molecular subtype-specific identification of GPCR axes to inform subtype-tailored hypotheses and analyses.
• Enhanced Drug Screening: Correlates GPCR axis activity with drug sensitivity from large-scale cancer cell screens to inform compound selection and interpretation.

Methodology:

Performs comprehensive analysis of cancer transcriptomics datasets to derive GPCR–ligand–enzyme interaction networks and pathway enrichment. Analyzes biosynthetic enzyme expression and integrates metabolomics-derived activity signatures. Correlates receptor, ligand, or enzyme expression with patient survival and drug sensitivity data, including large-scale cancer cell drug screens.