Abstract
BACKGROUND: Lung cancer remains one of the most prevalent and lethal cancers globally, often diagnosed at advanced stages, which impedes effective treatment. Recent advancements have highlighted exosomes as valuable biomarkers for early detection, prognosis, and therapeutic interventions in lung cancer. Exosomes, which carry molecular information from tumor cells, reflect tumor development and metastasis, offering potential for precision medicine. OBJECTIVE: This study aimed to develop a prognostic prediction model for lung cancer therapy based on miRNA profiling in exosomes. By performing bioinformatics analyses, we identified miRNAs and target genes associated with lung cancer treatment and their potential relationship with patient survival outcomes. MATERIALS AND METHODS: Using the GSE207715 dataset, we applied machine learning models and a Transformer-based deep learning approach to predict nivolumab treatment efficacy in lung cancer patients. Additionally, miRNA-target gene interactions were predicted via miRNA databases, followed by Gene Ontology and KEGG pathway enrichment analyses. A Cox proportional hazards regression model was used to assess the relationship between miRNA expression and patient survival. RESULTS: Significant differences were observed in the miRNA profiles of exosomes from patients with different nivolumab treatment outcomes, though the differences were relatively small. Machine learning models achieved prediction accuracies ranging from 0.6731 to 0.6923, while the deep learning model outperformed these methods with an accuracy of 0.9412. The hsa-let-7c miRNA showed statistical significance in multivariate survival risk analysis (p = 0.0152). CONCLUSION: This study demonstrates the potential of miRNA profiling in exosomes for predicting treatment efficacy and survival in lung cancer patients. The deep learning model's ability to capture subtle miRNA expression differences provides a robust platform for personalized treatment strategies in non-small cell lung cancer.