Integrative Network Modeling Highlights the Crucial Roles of Rho-GDI Signaling Pathway in the Progression of...

Area of Science:

• Oncology
• Computational Biology
• Genomics

Background:

• Non-small cell lung cancer (NSCLC) is a leading cause of cancer mortality globally.
• Understanding the molecular mechanisms driving NSCLC is crucial for developing effective treatments.

Purpose of the Study:

• To identify key cellular factors and pathways involved in NSCLC onset and progression using an integrative computational approach.
• To explore potential therapeutic targets for NSCLC.

Main Methods:

• Machine learning models (including XGBoost) were used to classify NSCLC and healthy cohorts.
• Functional enrichment analysis, WGCNA, and protein-protein interaction (PPI) network analysis were performed on selected genes.
• Bayesian network modeling was employed to simulate signaling pathway perturbations.

Main Results:

• Machine learning models achieved high prediction accuracies (0.83-1.0) for NSCLC classification.
• Genes in Rho GTPase signaling, regulating actin stability and cytoskeleton, were identified as crucial for NSCLC.
• Aberrations in ARHGEF19 and RAC2 gene activities were linked to impaired MAPK signaling and disrupted cytoskeleton organization, contributing to tumorigenesis.

Conclusions:

• Disruptions in RhoGDI signaling, particularly involving ARHGEF19 and RAC2, are implicated in NSCLC development.
• Targeting ARHGEF19 and/or RAC2 function restoration presents a potential therapeutic strategy for NSCLC.
• Findings support the discovery of predictive biomarkers and improved clinical outcomes for NSCLC.