Prediction of prognosis, efficacy of lung adenocarcinoma by machine learning model based on immune and metabolic related genes

In conclusion, we have successfully developed a predictive model incorporating immune and metabolism-related genes, encompassing gene expression levels of CAT/CCL20/GPI/INSL4 NT5E/GSTA3/GNPNAT1. This comprehensive model not only enables the prognosis prediction for LUAD patients but also facilitates the prediction of their response to first-line chemotherapy drugs and immune checkpoint inhibitors, thus demonstrating its broad potential in clinical applications. However, our study still has limitations as it is based on TCGA and GEO databases with limited pathological characteristics of patients. Therefore, more practical and valuable factors are needed to predict efficacy. The crosstalk between metabolism and immunity remains to be explored. Finally, this study lacks experimental evidence for the underlying gene expression of prognosis and further research is required.