Integrated analysis of immunity and ferroptosis related tumor microenvironment in a novel risk score model for lung adenocarcinoma prognosis

Abstract Objectives The study aims to discover biomarkers related to ferroptosis immunisation and predict their potential role in the prognosis of lung adenocarcinoma through the FER-IMMUscore model, so as to provide assistance for subsequent studies on ferroptosis and immunity in lung adenocarcinoma. Methods Relevant datasets were sourced from TCGA-LUAD, FerrDb, and ImmPort. Using LASSO regression, 11 ferroptosis-immunity-prognosis-related genes were analyzed to develop the FER-IMMUscore. Model validation employed GEO datasets (GSE68465, GSE30219) and quantitative reverse transcription polymerase chain reaction (qRT-PCR). Immune cell infiltration was quantified via CIBERSORT, and tumor immune dysfunction/exclusion (TIDE) analysis was performed. Associations with Tumor Mutation Burden (TMB), genomic enrichment, clinical features, and immune microenvironment profiles were assessed. Results The model identified LIFR and NR5A2 as key genes. High-risk patients (stratified by FER-IMMUscore) had a poorer prognosis than the low-risk groups. Validation in GEO datasets confirmed the model’s prognostic value. qRT-PCR demonstrated differential expression of LIFR in lung adenocarcinoma. High-risk patients exhibited distinct immune microenvironments characterized by elevated immune evasion signatures, altered immune cell infiltration patterns, and higher TIDE scores. Significant correlations with TMB, genomic alterations, and clinical staging were observed. Conclusions The FER-IMMUscore model reliably stratifies lung adenocarcinoma patients into prognostic subgroups, with high-risk cases exhibiting distinct molecular profiles (LIFR/NR5A2), immunosuppressive microenvironments, and adverse clinical outcomes.