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Bioinformatics analysis of esophageal cancer unveils an integrated mRNA‑lncRNA signature for predicting prognosis

  • Authors:
    • Tian Lan
    • Zunqiang Xiao
    • Hua Luo
    • Kunlun Su
    • Ouou Yang
    • Chengni Zhan
    • Yunyan Lu

  • View Affiliations

  • Published online on: December 11, 2019     https://doi.org/10.3892/ol.2019.11208
  • Pages: 1434-1442

  • Copyright: © Lan et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Esophageal cancer (ESCA) carries a poor prognosis among gastrointestinal malignancies. The present study developed a signature based on mRNAs and long non‑coding RNAs (lncRNAs) to predict prognosis in ESCA by using The Cancer Genome Atlas database. By using least absolute shrinkage and selection operator penalized regression, a set of RNAs (three mRNAs and two lncRNAs) was identified and used to build a risk score system of ESCA prognosis, which was used to stratify patients having considerable diverse survival in the training set [hazard ratio (HR), 3.932; 95% CI, 1.555‑9.944; P<0.002] into high‑ and low‑risk groups. The authentication of the results was achieved through the test set (HR, 3.150; 95% CI, 1.113‑8.918; P<0.02) and the entire set (HR, 3.181; 95% CI, 1.686‑6.006; P<0.0002). The results from multivariate Cox proportional hazard regression analysis in the entire set suggested that the prognostic significance of this signature may be independent of patients' clinicopathological characteristics. Furthermore, this signature was associated with several molecular signaling pathways of cancer according to Gene Set Enrichment Analysis. In addition, a nomogram was built and the risk score and TNM stage were integrated to estimate the 1‑ and 3‑year overall survival rates. The results from the present study demonstrated that the integrated mRNA‑lncRNA signature may be considered as a novel biomarker for the prognosis of ESCA.
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February-2020
Volume 19 Issue 2

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

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  • Source Normalized Impact per Paper (SNIP): 0.574
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Spandidos Publications style
Lan T, Xiao Z, Luo H, Su K, Yang O, Zhan C and Lu Y: Bioinformatics analysis of esophageal cancer unveils an integrated mRNA‑lncRNA signature for predicting prognosis. Oncol Lett 19: 1434-1442, 2020.
APA
Lan, T., Xiao, Z., Luo, H., Su, K., Yang, O., Zhan, C., & Lu, Y. (2020). Bioinformatics analysis of esophageal cancer unveils an integrated mRNA‑lncRNA signature for predicting prognosis. Oncology Letters, 19, 1434-1442. https://doi.org/10.3892/ol.2019.11208
MLA
Lan, T., Xiao, Z., Luo, H., Su, K., Yang, O., Zhan, C., Lu, Y."Bioinformatics analysis of esophageal cancer unveils an integrated mRNA‑lncRNA signature for predicting prognosis". Oncology Letters 19.2 (2020): 1434-1442.
Chicago
Lan, T., Xiao, Z., Luo, H., Su, K., Yang, O., Zhan, C., Lu, Y."Bioinformatics analysis of esophageal cancer unveils an integrated mRNA‑lncRNA signature for predicting prognosis". Oncology Letters 19, no. 2 (2020): 1434-1442. https://doi.org/10.3892/ol.2019.11208