Globally,
non-small cell lung cancer (NSCLC) is the most common
malignancy and its prognosis remains poor because of the lack of reliable early diagnostic
biomarkers. The
competitive endogenous RNA (
ceRNA) network plays an important role in the
tumorigenesis and prognosis of NSCLC.
Tumor immune microenvironment (TIME) is valuable for predicting the response to
immunotherapy and determining the prognosis of NSCLC patients. To understand the TIME-related
ceRNA network, the
RNA profiling datasets from the Genotype-Tissue Expression and The
Cancer Genome Atlas databases were analyzed to identify the mRNAs,
microRNAs, and lncRNAs associated with the differentially expressed genes. Weighted gene co-expression network analysis revealed that the brown module of mRNAs and the turquoise module of lncRNAs were the most important. Interactions among
microRNAs, lncRNAs, and mRNAs were prognosticated using miRcode, miRDB, TargetScan, miRTarBase, and starBase databases. A prognostic model consisting of 13 mRNAs was established using univariate and multivariate Cox regression analyses and validated by the receiver operating characteristic (ROC) curve. The 22 immune infiltrating cell types were analyzed using the CIBERSORT algorithm, and results showed that the high-risk score of this model was related to poor prognosis and an immunosuppressive TIME. A
lncRNA-
miRNA-
mRNA ceRNA network that included 69 differentially expressed lncRNAs (DElncRNAs) was constructed based on the five mRNAs obtained from the prognostic model. ROC survival analysis further showed that the seven DElncRNAs had a substantial prognostic value for the overall survival (OS) in NSCLC patients; the area under the curve was 0.65. In addition, the high-risk group showed drug resistance to several chemotherapeutic and targeted drugs including
cisplatin,
paclitaxel,
docetaxel,
gemcitabine, and
gefitinib. The differential expression of five mRNAs and seven lncRNAs in the
ceRNA network was supported by the results of the HPA database and RT-qPCR analyses. This comprehensive analysis of a
ceRNA network identified a set of
biomarkers for prognosis and TIME prediction in NSCLC.