# Weighted Gene Networks Derived from Multi-Omics Reveal Core Cancer Genes in Lung Cancer

## TL;DR

This paper introduces a new method to identify key cancer genes in lung cancer by combining gene expression and DNA methylation data, offering insights for personalized treatment.

## Contribution

A novel weighted gene regulatory network method integrating gene expression and DNA methylation data using maximum entropy and Markov entropy principles.

## Key findings

- Identified a stable core set of pathogenic genes, including CD74, HGF, BRAF, and KDM6A, involved in lung cancer progression.
- Uncovered methylation-driven oncogenes and tumor suppressors with dual roles in lung cancer development.
- Found potential driver genes like CORO2B and C20orf194 associated with patient subgroups based on clinical variables.

## Abstract

Lung cancer remains a leading cause of cancer-related deaths worldwide, characterized by high heterogeneity and complex gene regulatory mechanisms. We developed a novel weighted gene regulatory network reconstruction method that integrates gene expression and DNA methylation data, leveraging principles of maximum entropy and Markov entropy. Applied to LUAD and LUSC datasets, our approach successfully identified a stable core set of pathogenic genes, including both highly expressed genes (e.g., CD74, HGF) and stably expressed genes (e.g., BRAF, KDM6A), which play critical roles in cancer progression. Furthermore, we uncovered methylation-driven oncogenes and tumor suppressors, revealing their dual roles in lung cancer development. By incorporating clinical variables such as disease stage, gender, and smoking status, we identified potential driver genes associated with specific patient subgroups, providing insights into personalized therapeutic strategies. This method not only enhances our understanding of lung cancer biology but also offers a robust framework for identifying novel therapeutic targets and advancing precision medicine.

Lung cancer remains the leading cause of cancer-related deaths worldwide, driven by its complexity and the heterogeneity of its subtypes, which influence pathogenesis, tumor microenvironment, and genetic alterations. We developed a novel weighted gene regulatory network reconstruction method based on maximum entropy and Markov chain entropy principles, which integrates gene expression and DNA methylation data to generate biologically informed networks. Applied to LUAD and LUSC datasets, we define a network methylation index to determine whether gene methylation acts as oncogenic or tumor-suppressive. By revealing a stable core set of pathogenic genes, we identify not only genes with significant expression changes, such as CD74 and HGF, but also pathogenic genes with stable expression, such as BRAF and KDM6A. Additionally, we uncover potential driver genes, such as CORO2B and C20orf194, associated with disease stage, gender, and smoking status. This method offers a more comprehensive understanding of NSCLC mechanisms, paving the way for improved therapeutic strategies.