Abstract
Background: Lung adenocarcinoma (LUAD) is the most common histological subtype of lung cancer worldwide. Until now, the molecular mechanisms underlying LUAD progression have not been fully explained. This study aimed to identify a competing endogenous RNA (ceRNA) network in LUAD.Methods: Differentially expressed lncRNAs (DELs), miRNAs (DEMs), and mRNAs (DEGs) were identified from The Cancer Genome Atlas (TCGA) database with a |log2FC| > 1.0 and a false discovery rate (FDR) < 0.05. Then, these DELs, DEMs, and DEGs were used to construct the initial ceRNA network. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), protein-protein interaction (PPI) network, and survival analyses were performed to analyse these DEGs involved in the ceRNA network. Subsequently, the drug-gene interaction database (DGIdb) was utilized to select candidate LUAD drugs interacting with significant DEGs. Then, lasso-penalized Cox regression and multivariate Cox regression models were used to construct the risk score system. Kaplan-Meier (K-M) survival curves and receiver operating characteristic (ROC) curves were utilized to validate the reliability of the risk score system. Finally, based on the correlations between DELs and DEGs involved in the risk score system, the final ceRNA network was identified. Meanwhile, the GEPIA2 database and immunohistochemical (IHC) results were utilized to validate the expression levels of selected DEGs. GEPIA2 was further used to verify the correlations between DEGs and DELs.Results: A total of 340 DELs, 29 DEMs, and 218 DEGs were selected to construct the initial ceRNA network. Functional enrichment analyses indicated that 218 DEGs were significantly enriched in the GO terms “nucleoplasm”, “transcription factor complex”, “protein binding”, and “metal ion binding”, whereas these DEGs were associated with the KEGG pathway terms “microRNAs in cancer”, “pathways in cancer”, “cell cycle”, “HTLV-1 infection”, and the “PI3K-Akt signalling pathway”. K-M survival analysis of all differentially expressed genes involved in the ceRNA network identified 24 DELs, 4 DEMs, and 29 DEGs, all of which were significantly correlated with LUAD progression (P < 0.05). Furthermore, 15 LUAD drugs interacting with 29 DEGs were selected. After lasso-penalized Cox regression and multivariate Cox regression modelling, 4 DEGs, PRKCE, DLC1, LATS2, and DPY19L1, were incorporated into the risk score system. The area under the curve (AUC) values of the time-dependent ROC curves at 3 years and 5 years were both higher than 0.5. Finally, the correlation coefficients between these 4 DEGs and their corresponding DELs involved in the ceRNA network suggested that there were 2 DEL-DEG pairs, NAV2-AS2 – PRKCE (r = 0.430, P < 0.001) and NAV2-AS2 – LATS2 (r = 0.338, P < 0.001). Considering the previously constructed ceRNA network, NAV2-AS2 – mir-31 – PRKCE and NAV2-SA2 – mir-31 – LATS2 were identified.Conclusions: The lncRNA-miRNA-mRNA ceRNA network plays an essential role in LUAD. These results may improve our understanding and provide novel mechanistic insights to explore diagnostics, tumourigenesis, prognosis, and therapeutic drugs for LUAD patients.
Integrated Analysis of a Risk Score System Predicting Prognosis and a ceRNA Network for Differentially Expressed lncRNAs in Multiple Myeloma
