# NetG2P: Network-based genotype-to-phenotype transformation identifies key signaling crosstalk for prognosis in pan-cancer study

## TL;DR

NetG2P uses network analysis to translate genetic data into cancer prognosis insights, identifying key signaling interactions for personalized treatment strategies.

## Contribution

NetG2P introduces a novel network-based approach to transform genotype into phenotype, revealing signaling crosstalk critical for cancer prognosis.

## Key findings

- Critical oncogenic features represent signaling crosstalk and serve as functional units for cancer prognosis.
- Cancer types are categorized into 'distributed' and 'modular' networks based on pathway interactions.
- NetG2P predicts novel drug targets and repurposing candidates for high-risk cancer groups.

## Abstract

Despite advances in whole-genome sequencing and identifying cancer-associated genetic alterations, understanding the influence of multiple genetic alterations collectively on cancer phenotypes remains challenging, owing to mutation pattern complexity and variability. Here, we present the NETwork-based Genotype-to-Phenotype Transformation (NetG2P), which utilizes network propagation to translate genomic information into pathway interaction networks. Using the Cancer Genome Atlas dataset across 10 cancer types, we conducted a pan-cancer analysis using NetG2P to uncover critical oncogenic features associated with cancer prognosis using machine learning and explainable artificial intelligence models.

Our results suggest that these features, which primarily represent signaling crosstalk, can serve as functional units for determining cancer prognosis. Network analysis of these critical oncogenic features reveals distinct patterns among cancer types, categorizing them into “distributed” and “modular” networks based on pathway interactions. Applying this technique to cancer cell line data has helped predict novel drug targets for high-risk groups and proposed candidates for drug repurposing.

NetG2P generates patient-specific networks of critical oncogenic features and suggests personalized treatments, hence advancing precision medicine in oncology.

The online version contains supplementary material available at 10.1186/s12915-026-02559-x.