RTNsurvival

RTNsurvival integrates regulons from the RTN package with per-sample two-tailed Gene Set Enrichment Analysis (GSEA)-derived differential Enrichment Scores (dES) to evaluate associations between gene regulatory networks and cohort survival statistics.

Key Features:

• RTN regulon integration: Integrates regulons generated by the RTN package into downstream analyses.
• Two-tailed GSEA per sample: Computes per-sample differential Enrichment Scores (dES) using a two-tailed Gene Set Enrichment Analysis (GSEA).
• Survival assessment from dES distribution: Uses the distribution of per-sample dES values across a cohort to assess survival statistics.
• Gene regulatory network construction: Constructs gene regulatory networks composed of transcription factors and their putative target genes (regulons).
• eQTL-linked regulon enrichment: Identifies regulons enriched for genes linked to risk loci via expression quantitative trait loci (eQTLs).
• Overlapping regulon cluster detection: Detects overlapping regulons, reporting 36 overlapping regulons that form a distinct cluster within the network.
• Risk-associated transcription factors: Notes that risk-associated transcription factors driving identified regulons are frequently mutated in cancer.
• Subtype and cell-population correlation: Assigns transcription factor subgroups that correlate with breast cancer subtypes (ER+ luminal A/B and ER-negative basal-like) and with luminal epithelial cell populations.

Scientific Applications:

• Breast cancer risk locus interpretation: Maps eQTL-linked genes to regulons to identify shared regulatory mechanisms underlying breast cancer susceptibility.
• Subtype-specific survival analysis: Associates regulon activity (dES) with survival across ER+ (luminal A/B) and ER-negative (basal-like) breast cancer subtypes.
• Identification of candidate regulatory drivers: Highlights transcription factors within risk-associated regulons as candidate drivers or therapeutic targets in cancer.
• Generalization to other diseases: Applies the regulon-plus-survival framework to other disease cohorts by integrating RTN regulons with survival data.

Methodology:

Integrates RTN-generated regulons; constructs gene regulatory networks of transcription factors and putative target genes; applies a two-tailed GSEA to compute per-sample differential Enrichment Scores (dES); uses the distribution of dES across samples to assess survival statistics; identifies regulons enriched for genes linked to risk loci via expression quantitative trait loci (eQTLs).