Bioinformatics Analysis Based on Iron Death-Related LncRNAs: Construction of a Clinical Prognostic Model for Lung Adenocarcinoma and Correlation Analysis of Immune Infiltration

Abstract

Objective: A prognostic model for iron death-related LncRNA in lung adenocarcinoma was constructed through the TCGA cancer database, and immune correlation was analyzed for patients in the high-low risk group in the model. This study has certain guiding significance for the diagnosis and treatment of lung adenocarcinoma, and has the necessity of research. Methods: Get gene expression data and clinical data of lung adenocarcinoma tumor tissue and normal lung tissue in TCGA database, select LncRNA, adopt single and multivariate Cox risk regression model and establish the LncRNA prognosis risk prediction model, score patients according to the model, and compare high risk and low risk groups to obtain immune related differences between high and low risk groups. Results: A total of 55 differential LncRNAs related to the prognosis of iron death were obtained. Based on univariate and multivariate Cox regression analysis, a multivariate prognostic risk prediction model was established for 16 LncRNAs. A high patient risk score suggests a poor prognosis. Immunocorrelation analysis showed that in the high and low risk groups: Dendritic cells, B cells, mast cells and other immune cells (P < 0.001), APC co-inhibition, APC co-stimulation, chemotactic factor receptor, immune checkpoint and other immune functions (P < 0.01), IDO2, HHLA2, CD276, BTLA and other immune checkpoint related genes (P < 0.001) had significant statistical difference. Conclusion: The prognostic model based on 16 iron death-related LncRNA can well predict the prognosis of lung adenocarcinoma patients, and the iron death-related LncRNA involved in the model construction obtained in the study may be related to immune infiltration levels, and may even be a novel target for immune checkpoint inhibitor therapy.