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
Puntos clave
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.
Resumen
Our signature, which is linked to immunotherapy response, aids in predicting the prognosis and immunotherapy outcomes of NSCLC patients, thereby offering valuable insights for their clinical management.
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 | Synapse