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

Area of Science:

• Genomics and Bioinformatics
• Cancer Research
• Molecular Biology

Background:

• Lung cancer is a leading cause of cancer mortality globally.
• Tumor heterogeneity, including genetic alterations and microenvironment, complicates pathogenesis.
• Understanding gene regulatory networks is crucial for deciphering complex diseases like lung cancer.

Purpose of the Study:

• To develop a novel weighted gene regulatory network reconstruction method.
• To integrate gene expression and DNA methylation data for biologically informed networks.
• To identify pathogenic and driver genes in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC).

Main Methods:

• Developed a weighted gene regulatory network reconstruction method using maximum entropy and Markov chain entropy principles.
• Integrated gene expression and DNA methylation data.
• Defined a network methylation index to assess oncogenic or tumor-suppressive roles of gene methylation.

Main Results:

• Identified a stable core set of pathogenic genes in lung cancer.
• Discovered genes with significant expression changes (e.g., CD74, HGF) and stable expression (e.g., BRAF, KDM6A).
• Uncovered potential driver genes (e.g., CORO2B, C20orf194) linked to disease stage, gender, and smoking status.

Conclusions:

• The novel method provides a comprehensive understanding of non-small cell lung cancer (NSCLC) mechanisms.
• Gene methylation plays a significant role, acting as either oncogenic or tumor-suppressive.
• The findings pave the way for improved therapeutic strategies targeting lung cancer.