Machine learning models reveal ARHGAP11A's impact on lymph node metastasis and stemness in NSCLC

Area of Science:

• Oncology
• Molecular Biology
• Biomarker Discovery

Background:

• Non-small cell lung cancer (NSCLC) often presents at advanced stages, increasing metastasis risk.
• Identifying biomarkers for lymph node metastasis is crucial for effective NSCLC management.
• Existing treatments face challenges due to disease complexity and metastatic potential.

Purpose of the Study:

• To identify novel genes associated with lymph node metastasis in NSCLC using Weighted Gene Co-expression Network Analysis (WGCNA).
• To investigate the prognostic and predictive value of identified genes, particularly ARHGAP11A, in NSCLC.
• To evaluate the therapeutic potential of targeting ARHGAP11A in NSCLC.

Main Methods:

• Weighted Gene Co-expression Network Analysis (WGCNA) to identify metastasis-related genes.
• Cluster analysis for correlating gene expression with prognosis and immunotherapy outcomes.
• Machine learning models for diagnostic and prognostic prediction; cell function experiments to assess ARHGAP11A's role.

Main Results:

• WGCNA identified 25 genes linked to lymph node metastasis, tumor invasion, growth, and stemness pathways.
• ARHGAP11A was highlighted as a key prognostic gene associated with lymph node metastasis and poor patient outcomes.
• Reduced ARHGAP11A expression inhibited NSCLC cell proliferation, metastasis, and stemness; molecular docking suggested therapeutic potential.

Conclusions:

• ARHGAP11A serves as a potential biomarker for lymph node metastasis in NSCLC.
• Targeting ARHGAP11A may offer a novel therapeutic strategy by diminishing tumor stemness and metastasis.
• The study provides a foundation for developing new diagnostic and therapeutic approaches for NSCLC.