A novel metabolic-immune related signature predicts prognosis and immunotherapy response in lung adenocarcinoma - Xiaolong Tang; Chumei Qi; Honghong Zhou; Yongshuo Liu (Heliyon, 2022)
Background: Lung adenocarcinoma (LUAD) is one of the most frequent types of lung cancer, with a high mortality and recurrence rate. This study aimed to design a RiskScore to predict the prognosis and immunotherapy response of LUAD patients due to a lack of metabolic and immune-related prognostic models. Methods: To identify prognostic genes and generate a RiskScore, we conducted differential gene expression analysis, bulk survival analysis, Lasso regression analysis, and univariate and multivariate Cox regression analysis using TCGA-LUAD as a training subset. GSE31210 and GSE50081 were used as validation subsets to validate the constructed RiskScore. Following that, we explored the connection between RiskScore and clinicopathological characteristics, immune cells infiltration, and immunotherapy. In addition, we investigated into RiskScore's biological roles and constructed a Nomogram model. Results: A RiskScore was identified consisting of five genes (DKK1, CCL20, NPAS2, GNPNAT1 and MELTF). In the RiskScore-high group, LUAD patients showed decreased overall survival rates and shorter progression-free survival. Multiple clinicopathological characteristics and immune cells infiltration in TME, in particular, have been linked to RiskScore. Of note, RiskScore-related genes have been implicated to substance metabolism, carcinogenesis, and immunological pathways, among other things. Finally, the C-index of the RiskScore-based Nomogram model was 0.804 (95% CI: 0.783–0.825), and time-dependent ROC predicted probabilities of 1-, 3- and 5-year survival for LUAD patients were 0.850, 0.848 and 0.825, respectively. Conclusion: The RiskScore, which integrated metabolic and immunological features with DKK1, CCL20, NPAS2, GNPNAT1, and MELTF, could reliably predict prognosis and immunotherapy response in LUAD patients. Moreover, the RiskScore-based Nomogram model had a promising clinical application.