ABSTRACT
OBJECTIVE: We searched for a predictive biomarker that also predicts whether patients would benefit from immune checkpoint blockade (ICB) treatment from a few angles, because existing biomarkers no longer wholly replicate the interconnections of distinctive elements in the tumor microenvironment (TME). METHODS: We identified 55 pivotal IRGs by performing a WGCNA and univariate Cox regression analysis on a lung adenocarcinoma dataset from the TCGA database. The IRGPI model was then constructed using multivariate Cox regression analysis, which identified 16 genes and verified the use of the GSE68465 database. The AUC of the IRGPI was compared to those of the current biomarkers to determine its predictive potential. Then we examined the molecular and immunological properties of ICB and assessed its effectiveness using CTLA4 expression and TIDE. RESULTS: Patients with a high IRGPI had a later clinical stage, more severe symptoms, and a worse prognosis. Patients with a low IRGPI had a higher immune escape potential and were less responsive to immunotherapy. CONCLUSION: The IRGPI may be a biomarker for determining the prognosis of patients and whether they respond favorably to ICB therapy.