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The study aimed to elucidate the mechanisms underlying the occurrence and development of lung adenocarcinoma, and to reveal long non‑coding RNA (lncRNA) prognostic factors to identify patients at high risk of disease recurrence or metastasis. Based on extensive RNA sequencing data and clinical survival prognosis information from patients with lung adenocarcinoma, obtained from The Cancer Genome Atlas and the Gene Expression Omnibus databases, a co‑expression network of lncRNAs with different expression levels was built using weighted correlation network analysis and MetaDE.ES. The prognostic lncRNAs were identified using the Cox proportional hazards model and Kaplan‑Meier survival curves to construct a risk scoring system. The reliability of the system was confirmed in validation datasets. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed on the genes significantly associated with the prognostic lncRNAs using gene set enrichment analysis. A total of 58 and 1,633 differentially expressed lncRNAs and mRNAs were identified, respectively. Considering the module stability, annotation, correlation between modules and clinical factors, and the differential expression levels of lncRNAs, 32 differentially expressed lncRNAs were selected from the brown, red, blue, green and yellow modules for subsequent survival analysis. A signature‑based risk scoring system involving five lncRNAs [DIAPH2 antisense RNA 1, FOXN3 antisense RNA 2, long intergenic non‑protein coding RNA 652, maternally expressed 3 and RHPN1 antisense RNA 1 (head to head)] was developed. The system successfully distinguished between low‑ and high‑risk prognostic samples. System effectiveness was further verified using two independent validation datasets. Further KEGG pathway analysis indicated that the target genes of the five prognostic lncRNAs were associated with a number of cellular processes and signaling pathways, including the cell receptor‑mediated signaling and cell adhesion pathways. A five‑lncRNA signature predicts the prognosis of patients with lung adenocarcinoma. These prognostic lncRNAs may be potential diagnostic markers. The present results may help elucidate the pathogenesis of lung adenocarcinoma.

Screening of important lncRNAs associated with the prognosis of lung adenocarcinoma, based on integrated bioinformatics analysis