Identification of Prognostic Biomarkers Based on the Inflammatory and Immune Features in Lung Adenocarcinoma

Abstract

Background: Immune response in the context of lung adenocarcinoma affects patient prognosis and immunotherapy efficiency. Therefore, we aimed to identify an effective prognostic model for facilitating further treatment. Methods: First, T cell inflammatory and immune scores were calculated based on an online dataset. Second, optimal genes related to the T cell inflammatory and immune scores were screened using weighted gene co-expression network analysis, differential expression analysis, and survival analysis. Finally, an inflammatory immune-related risk model was established and evaluated. Immune cell infiltration, functional characteristics, mutation features, immune checkpoint blockage genes, human leukocyte antigen genes, immune cell marker genes, and drug sensitivity were analyzed between the high- and low-immune-related risk score groups. Results: An eight-gene immune-related risk model was established and determined to perform well in regard to predicting lung adenocarcinoma survival. Different immune-related risk groups exhibited different immune cell infiltration characteristics, mutation frequencies, tumor mutation burdens, immune checkpoint blockage genes, human leukocyte antigen genes, immune cell marker genes, and drug sensitivities. Moreover, the differentially expressed genes between the high- and low-immune-related risk groups were enriched in the functions and pathways related to immune response and cancer progression. Conclusions: The immune-related risk model established in this study possesses a potential predictive value for patient prognosis and immunotherapy response, thus indicating its potential applicability for use in clinical practice.