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

BACKGROUND

The aim of this study is to integrate immune and metabolism-related genes in order to construct a predictive model for predicting the prognosis and treatment response of LUAD(lung adenocarcinoma) patients, aiming to address the challenges posed by this highly lethal and heterogeneous disease.

MATERIAL AND METHODS

Using TCGA-LUAD as the training subset, differential gene expression analysis, batch survival analysis, Lasso regression analysis, univariate and multivariate Cox regression analysis were performed to construct prognostic related gene models. GEO queue as validation subsets, is used to validate build Riskscore. Then, we explore the Riskscore and mutation status, immune cell infiltration, the relationship between immune therapy and chemotherapy, and build the model of the nomogram.

RESULTS

The Riskscore has been determined to be composed of seven gene. In the high-risk group defined by this score, both early-stage and advanced-stage LUAD patients exhibit a decreased overall survival rate. The mutation status of patients as well as immune cell infiltration show associations with the Riskscore value obtained from these genes' expression levels. Furthermore, there exist variations in response to immunotherapy as well as sensitivity to commonly used chemotherapy drugs among different individuals. Lastly, when using a column line plot model based on the calculated Riskscore values, we obtain a concordance index (C-index) was 0 .716 (95% CI 0.671-0.762), and time-dependent ROC predicted probabilities of 1-, 3- and 5-year survival for LUAD patients were 0.752, 0.725 and 0.654, respectively.

CONCLUSION

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.