Comprehensive Summary
In this study, researchers used three machine learning methods to create a novel diagnostic model for non-small cell lung cancer (NSCLC) identification. In particular, the study used gene expression data from GEO and TCGA databases to produce this model, after which a variety of statistical analyses were performed to validate the model. The diagnostic model produced consisted of six core exosome-related NSCLC differently expressed genes and found that the model demonstrated a high degree of accuracy in predicting performance (AUC>0.98). Additional analysis indicated that this is a clinically acceptable model. The researchers also performed qRT-PCR to ensure the reliability of gene expression, providing insight on the disease development of NSCLC.
Outcomes and Implications
This study created a novel and accurate diagnostic model for the identification of NSCLC biomarkers. One difficult part about NSCLC diagnoses is the lack of early biomarker detection. Using the accurate model described, health-care providers can identify early signs of NSCLCs and potentially start treatment earlier, preventing disease progression. Additionally, health-care professionals can use the model to personalize treatment care, improving health outcomes and employing the most effective treatments for a given patient.