Open Access

Identification of a long non‑coding RNA signature for predicting prognosis and biomarkers in lung adenocarcinoma

  • Authors:
    • Xiaolin Yu
    • Yanxia Zhang
  • View Affiliations

  • Published online on: February 17, 2020     https://doi.org/10.3892/ol.2020.11400
  • Pages: 2793-2800
  • Copyright: © Yu et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )
Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )


Abstract

Long non‑coding RNAs (lncRNAs) have a number of functions in various cellular processes and are potential prognostic factors for lung adenocarcinoma (LUAD). A gene risk model could provide novel evidence to improve the prediction of overall outcomes and provide more potential biomarkers. The present study aimed improve a previously published method of gene signature construction to make it more robust and accurate. The lncRNA expression profiles from 594 patients with LUAD were obtained from The Cancer Genome Atlas (TCGA) database and samples were divided into high‑ and low‑risk groups based on median risk scores calculated using a prognosis‑related risk score formula. Univariate Cox regression, least absolute shrinkage and selection operator algorithm and multivariate Cox regression were performed to construct a gene signature based on the differentially expressed lncRNAs in patients with LUAD. The robustness and accuracy of the present model was assessed using area under the calculated curves (AUC) and Kaplan‑Meier (K‑M) survival analysis of the high‑ and low‑risk cohorts. Potential biomarkers associated with survival status were then identified using K‑M survival analysis and potential biomarker functions were predicted using enrichment analysis of co‑expressed mRNAs. The gene signature constructed contained 44 lncRNAs. The AUCs for 3‑ and 5‑year survival with the model were 0.836 and 0.818, respectively, of a time‑dependent receiver operator characteristic curve. Moreover, lncRNAs AC124804.1 and MIR34AHG were identified using K‑M survival analysis and the potential function of these two lncRNAs was predicted using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional enrichment. The present lncRNA model provides novel insight which may improve prediction of prognosis for patients with LUAD and identify potentially novel biomarkers for the diagnosis.
View Figures
View References

Related Articles

Journal Cover

April-2020
Volume 19 Issue 4

Print ISSN: 1792-1074
Online ISSN:1792-1082

Sign up for eToc alerts

Recommend to Library

Copy and paste a formatted citation
x
Spandidos Publications style
Yu X and Yu X: Identification of a long non‑coding RNA signature for predicting prognosis and biomarkers in lung adenocarcinoma. Oncol Lett 19: 2793-2800, 2020
APA
Yu, X., & Yu, X. (2020). Identification of a long non‑coding RNA signature for predicting prognosis and biomarkers in lung adenocarcinoma. Oncology Letters, 19, 2793-2800. https://doi.org/10.3892/ol.2020.11400
MLA
Yu, X., Zhang, Y."Identification of a long non‑coding RNA signature for predicting prognosis and biomarkers in lung adenocarcinoma". Oncology Letters 19.4 (2020): 2793-2800.
Chicago
Yu, X., Zhang, Y."Identification of a long non‑coding RNA signature for predicting prognosis and biomarkers in lung adenocarcinoma". Oncology Letters 19, no. 4 (2020): 2793-2800. https://doi.org/10.3892/ol.2020.11400