Back to Journals » OncoTargets and Therapy » Volume 11

Twenty-gene-based prognostic model predicts lung adenocarcinoma survival

Authors Zhao K, Li Z, Tian H

Received 1 December 2017

Accepted for publication 8 April 2018

Published 12 June 2018 Volume 2018:11 Pages 3415—3424

DOI https://doi.org/10.2147/OTT.S158638

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Dr Samir Farghaly


Kai Zhao,1,2 Zulei Li,2 Hui Tian,1

1Department of Thoracic Surgery, Qilu Hospital of Shandong University, Jinan, Shandong Province, China; 2Department of Thoracic Surgery, Zibo Central Hospital, Zibo, Shandong Province, China

Introduction: Lung adenocarcinoma (LAC) accounts for more than a half of non-small cell lung cancer with high morbidity and mortality. Progression of treatment has not accelerated the improvement of its prognosis. Hence, it is an urgent need to develop novel biomarkers for its early diagnosis and treatment.
Materials and methods: In this study, we proposed to identify LAC survival-related genes through comprehensive analysis of large-scale gene expression profiles. LAC gene expression data sets were obtained from The Cancer Genome Atlas (TCGA). Identification of differentially expressed genes (DEGs) in LAC compared with adjacent normal lung tissues was first performed followed by univariate Cox regression analysis to obtain genes that are significantly associated with LAC survival (SurGenes). Then, we conducted sure independence screening (SIS) for SurGenes to identify more reliable genes and the prognostic signature for LAC survival prediction. Another two lung cancer data sets from TCGA and Gene Expression Omnibus (GEO) were used for the validation of prognostic signature.
Results: A total of 20 genes were obtained, which were significantly associated with the overall survival (OS) of LAC patients. The prognostic signature, a weighted linear combination of the 20 genes, could successfully separate LAC samples with high OS from those with low OS and had robust predictive performance for survival (training set: p-value <2.2×10-16; testing set: p-value =2.04×10-5, area under the curve (AUC) =0.615). Combined with GEO data set, we obtained four genes, that is, FUT4, SLC25A42, IGFBP1, and KLHDC8B that are found in both the prognostic signature and DEGs of LAC in GEO data set.
Discussion: The prognostic signature combined with multi-gene expression profiles provides a moderate OS prediction for LAC and should be helpful for appropriate treatment method selection.

Keywords:
GEO, lung adenocarcinoma, SIS, survival, TCGA

Creative Commons License This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution - Non Commercial (unported, v3.0) License. By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms.

Download Article [PDF]  View Full Text [HTML][Machine readable]