Assessing the Prognostic Capability of Immune-Related Gene Scoring Systems in Lung Adenocarcinoma

Background. Lung adenocarcinoma (LUAD) is the commonest of the subtypes of lung cancer histologically. For this study, we intended to analyze the expression profiling of the immune-related genes (IRGs) from an independently available public database and developed a potent signature predictive of patients’ prognosis. Methods. Gene expression profiles and the clinical data of lung adenocarcinoma were gathered from the Gene Expression Omnibus database (GEO) and The Cancer Genome Atlas (TCGA), and the obtained data were split into a training set (n = 226), test set (n = 83), and validation set (n = 400). IRGs were then gathered from the ImmPort database. A prognostic model was constructed by analyzing the training set. Then the GO and KEGG analysis was performed, and a gene correlation prognostic nomogram was constructed. Finally, external validation, such as immune infiltration and immunohistochemistry, was performed. Results. The 110 genes were significant by univariate Cox regression analysis and randomized survival forest algorithm for the training set and showed a good distinction between the low-risk-score and high-risk-score groups in the training set (P