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Lung adenocarcinoma (LUAD) is the leading cause of cancer-related death worldwide, and its underlying mechanism remains unclear. Accumulating evidence has highlighted that long non-coding RNA (lncRNA) acts as competitive endogenous RNA (ceRNA) and plays an important role in the occurrence and development of LUAD. Here, we comprehensively analyzed and provided an overview of the lncRNAs, miRNAs, and mRNAs associated with LUAD from The Cancer Genome Atlas (TCGA) database. Then, differentially expressed lncRNAs (DElncRNA), miRNAs (DEmiRNA), and mRNAs (DEmRNA) were used to construct a lncRNA-miRNA-mRNA regulatory network according to interaction information from miRcode, TargetScan, miRTarBase, and miRDB. Finally, the RNAs of the network were analyzed for survival and submitted for Cox regression analysis to construct prognostic indicators. A total of 1123 DElncRNAs, 95 DEmiRNAs, and 2296 DEmRNAs were identified (|log2FoldChange| (FC) &gt; 2 and false discovery rate (FDR) or adjusted P value &lt; 0.01). The ceRNA network was established based on this and included 102 lncRNAs, 19 miRNAs, and 33 mRNAs. The DEmRNAs in the ceRNA network were found to be enriched in various cancer-related biological processes and pathways. We detected 22 lncRNAs, 12 mRNAs, and 1 miRNA in the ceRNA network that were significantly associated with the overall survival of patients with LUAD (P &lt; 0.05). We established three prognostic prediction models and calculated the area under the 1,3,5-year curve (AUC) values of lncRNA, mRNA, and miRNA, respectively. Among them, the prognostic index (PI) of lncRNA showed good predictive ability which was 0.737, 0.702 and 0.671 respectively, and eight lncRNAs can be used as candidate prognostic biomarkers for LUAD. In conclusion, our study provides a new perspective on the prognosis and diagnosis of LUAD on a genome-wide basis, and develops independent prognostic biomarkers for LUAD.

Comprehensive analysis of prognostic biomarkers in lung adenocarcinoma based on aberrant lncRNA–miRNA–mRNA networks and Cox regression models