Construction of a circRNA-Related Prognostic Risk Score Model for Predicting the Immune Landscape of Lung Adenocarcinoma

The purpose of this study was to construct a circular RNA (circRNA)-related competing endogenous RNA (ceRNA) regulatory network and risk score model for lung adenocarcinoma (LUAD). The relationship of the risk score to immune landscape and sensitivity to chemotherapy and targeted therapy of LUAD was assessed. We downloaded mRNA and miRNA expression data, along with clinical information, from The Cancer Genome Atlas (TCGA) program, and circRNA expression data from the Gene Expression Omnibus (GEO) database and identified differently expressed circRNA (DEcircRNA), miRNA (DEmiRNA), and mRNA (DEmRNA) using R software. We then constructed the circRNA-related network using bioinformatics method. The risk score model was established by LASSO Cox regression analysis based on 10 hub genes. In addition, the risk score model was an independent predictor for overall survival (OS) in both the TCGA and CPTAC datasets. Patients in the high-risk group had shorter OS and disease-free survival (DFS) than those in the low-risk group and were more sensitive to chemotherapy and targeted therapy. The types of tumor-infiltrating immune cells were different in the high- and low-risk groups. Our data revealed that the circRNA-related risk score model is closely associated with the level of immune cell infiltration in the tumor and the effects of adjuvant treatment. This network may be useful in designing personalized treatments for LUAD patients.