Prognostic Model Establishment and Immune Microenvironment Analysis of Lung Adenocarcinoma Based on Ferroptosis-Related Long Noncoding RNAs

Abstract

Abstract Background: Lung cancer is a common malignant tumor, which is divided into many subtypes. Lung adenocarcinoma (LUAD) is a most common subtype. More and more studies have confirmed that ferroptosis is involved in the occurrence and development of lung cancer. In this paper, we studied the prognostic ferroptosis-related long noncoding RNAs (FRLs) to build a LUAD-related prognosis model. Methods: We first downloaded the relevant data of 598 patients from the TCGA-LUAD dataset of The Cancer Genome Atlas, and then randomly divided them into training group and testing group in a 1:1 ratio. After that, we used Pearson correlation analysis and univariate Cox regression analysis to determine the FRLs related to prognosis. Then, according to the least absolute shrinkage and selection operator (LASSO) algorithm, the risk model was constructed using the optimized prognostic FRLs subset. We further used the receiver operating characteristic (ROC) curve and survival analysis to evaluate the performance of our model, meanwhile, Cox regression analysis was performed to analyze the risk score (RS). Finally, we also carried out gene set enrichment analysis (GSEA) , and differential analysis of immune-related genes and m6a-related genes. Results: In this study, we identified a total of 34 FRLs associated with the prognosis of lung adenocarcinoma, and established a prognostic model with 7 of them. Kaplan-Meier analysis showed that relevant characteristics of patients in high-risk group were correlated with poorer prognosis. The AUC value of our model was quite ideal, indicating that it could accurately predict the prognosis of LUAD patients. Further GSEA results showed that FRLs of individuals in high-risk groups were mainly enriched in cell cycle and related regulatory pathways, while those in low-risk groups were mainly enriched in immune-related pathways. We also employed immune function analysis and immune checkpoints expression analysis, and found that CCR, check-point, HLA, T cell co−inhibition, T cell co−stimulation and Type II IFN Reponse had significant differences between two groups, while most immune checkpoints had higher expression levels in low-risk groups. Conclusion: Our research has proved that FRls could indeed be used as a prognostic feature to build a prognostic model of lung adenocarcinoma. On the basis of this theory, it is of great significance and value to further study new treatment methods.