MinNetRank

MinNetRank prioritizes personalized cancer driver genes by integrating mutation-specific weighting, interaction network topology, and a minimum-strategy aggregation of multi-omics sample rankings to support biomarker discovery and patient stratification.

Key Features:

• Mutation Weighting: Assigns specific weights to various mutation types to reflect their biological significance.
• Network-Based Ranking: Ranks genes using network topology by considering both incoming and outgoing degrees of interaction networks.
• Minimum Strategy Integration: Integrates multi-omics data via a minimum strategy that consolidates sample-specific gene rankings into a population-level ranking.
• Performance Metrics: Demonstrates superior precision, F1 score, and partial area under the curve (AUC) across six cancer datasets.
• Novel Driver Gene Discovery: Uniquely identified SP1 as a candidate driver gene for liver hepatocellular carcinoma with differential RNA and protein expression between tumor and normal samples.
• Clinical Relevance: The top seven prioritized genes stratify patients into subtypes with statistically significant overall survival differences across five cancer types.

Scientific Applications:

• Driver Gene Prioritization: Prioritizes candidate driver genes from multi-omics cancer datasets for downstream biological validation.
• Biomarker Discovery: Identifies marker genes whose expression or mutation profiles correlate with tumor versus normal differences.
• Patient Stratification: Enables stratification of patients into subtypes associated with differential overall survival.
• Personalized Oncology: Supports personalized medicine approaches by providing sample-specific and population-level gene rankings.

Methodology:

Sets mutation-specific weights, leverages network-based interactions while considering incoming and outgoing node degrees, and integrates multi-omics sample-specific rankings into a population-level ranking using a minimum strategy.