PPISURV

PPISURV analyzes gene interactomes to identify associations between interactome expression profiles and cancer patient survival across public clinical expression datasets.

Key Features:

• Gene Interactome Analysis: Examines expression profiles of an input gene's interactome—including protein-protein interactions, regulatory and signaling pathways, and post-translational modifications—rather than individual gene expression (examples: p53, p73, p21).
• Data Integration: Integrates data from multiple public databases to construct comprehensive gene interactomes encompassing protein-protein interactions, regulatory networks, and signaling pathways.
• Survival Outcome Correlation: Correlates interactome expression levels with survival rates across more than 40 publicly available clinical expression datasets covering approximately 8,000 patients and multiple tumor types.

Scientific Applications:

• Biomarker discovery: Identify novel prognostic biomarkers by analyzing the aggregated expression of gene interactomes.
• Functional interpretation: Elucidate multifunctional roles of genes in cancer by situating them within their interaction networks.
• Therapeutic target exploration: Prioritize candidate therapeutic targets within gene interaction networks that associate with patient survival.

Methodology:

Constructs gene interactomes by integrating protein-protein interaction, regulatory network, and signaling pathway data from public databases, then correlates interactome expression profiles with survival outcomes across more than 40 clinical expression datasets (~8,000 patients).