Integrating WGCNA and machine learning algorithm to identify ACSM5 as a prognostic biomarker and therapeutic target for predicting immunotherapy efficacy in non-small cell lung cancer

Key Points

• Prognostic biomarker identification enhances immunotherapy response evaluation in NSCLC patients.
• The analysis indicates that ACSM5 may be crucial in predicting treatment outcomes, impacting patient management.
• Using machine learning algorithms integrated with WGCNA enables robust biomarker discovery for non-small cell lung cancer.
• The findings pave the way for personalized immunotherapy strategies, although further validation is needed.

Abstract