Bioinformatics Approach to mTOR Signaling Pathway-Associated Genes and Cancer Etiopathogenesis.

Area of Science:

• Oncology
• Systems Biology
• Bioinformatics

Background:

• The mechanistic target of rapamycin (mTOR) kinase is crucial for cell growth and metabolism, and its dysregulation is implicated in cancer.
• Current pathway curation methods have limitations in identifying actionable biomarkers for the mTOR axis.
• A network-aware, reproducible framework is needed to integrate multi-omics data for comprehensive biomarker discovery.

Purpose of the Study:

• To develop and validate a pan-cancer, network-aware framework for identifying mTOR pathway biomarkers.
• To distinguish broadly shared drivers from tumor-specific nodes within the mTOR network.
• To prioritize non-mutated, network-proximal candidates for biomarker validation.

Main Methods:

• Assembled and harmonized mTOR-related genes and interactions from multiple databases (GeneCards, KEGG, STRING, UniProt, PathCards).
• Developed a model linking genotype to pathway architecture, expression rewiring, and phenotype.
• Applied pan-cancer alteration mapping, expression-based activity scoring, and topology-aware network propagation (PageRank, Random Walk with Restart).

Main Results:

• Identified significant enrichment for mTOR signaling and amino acid response pathways.
• Corroborated disease associations between mTOR and its partners (e.g., TSC2, RICTOR) across carcinomas.
• Distinguished shared drivers (e.g., PTEN, PIK3CA) from lineage-enriched nodes and prioritized novel candidates based on network proximity and activity signatures.

Conclusions:

• The developed framework offers a transparent, reproducible method for unifying curated biology, genomics, and network topology for mTOR axis research.
• The approach refines biomarker discovery by prioritizing non-mutated, topology-inferred candidates and distinguishing shared from tumor-specific drivers.
• This framework supports the validation of prioritized candidates and the design of correlative studies linking pathway activity to clinical response.