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Combining data from TCGA and GEO databases and reverse transcription quantitative PCR validation to identify gene prognostic markers in lung cancer

Authors Liu X, Wang J, Chen M, Liu S, Yu X, Wen F

Received 17 August 2018

Accepted for publication 5 December 2018

Published 21 January 2019 Volume 2019:12 Pages 709—720

DOI https://doi.org/10.2147/OTT.S183944

Checked for plagiarism Yes

Review by Single-blind

Peer reviewers approved by Ms Justinn Cochran

Peer reviewer comments 2

Editor who approved publication: Dr Leo Jen-Liang Su


Xiao Liu,1–3,* Jun Wang,3,* Mei Chen,3 Shilan Liu,3 Xiaodan Yu,3 Fuqiang Wen1,2

1Department of Respiratory and Critical Care Medicine, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, China; 2Division of Pulmonary Diseases, State Key Laboratory of Biotherapy of China, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, China; 3Department of Respiratory and Critical Care Medicine, The Fifth People’s Hospital of Chengdu, Chengdu, Sichuan, China

*These authors contributed equally to this work

Background: The aim of this study was to predict and explore the possible mechanism and clinical value of genetic markers in the development of lung cancer with a combined database to screen the prognostic genes of lung cancer.
Materials and methods: Common differential genes in two gene expression chips (GSE3268 and GSE10072 datasets) were investigated by collecting and calculating from Gene Expression Omnibus and The Cancer Genome Atlas databases using R language. Five markers of gene composition (ribonucleotide reductase regulatory subunit M2 [RRM2], trophoblast glycoprotein [TPBG], transmembrane protease serine 4[TMPRFF4], chloride intracellular channel 3 [CLIC3], and WNT inhibitory factor-1 [WIF1]) were found by the stepwise Cox regression function when we further screened combinations of gene models, which were more meaningful for prognosis. By analyzing the correlation between gene markers and clinicopathological parameters of lung cancer and its effect on prognosis, the TPBG gene was selected to analyze differential expression, its possible pathways and functions were predicted using gene set enrichment analysis (GSEA), and its protein interaction network was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database; then, quantitative PCR and the Oncomine database were used to verify the expression differences of TPBG in lung cancer cells and tissues.
Results: The expression levels of five genetic markers were correlated with survival prognosis, and the total survival time of the patients with high expression of the genetic markers was shorter than those with low expression (P<0.001). GSEA showed that these high-expression samples enriched the gene sets of cell adhesion, cytokine receptor interaction pathway, extracellular matrix receptor pathway, adhesion pathway, skeleton protein regulation, cancer pathway and TGF-β pathway.
Conclusion: The high expression of five gene constituent markers is a poor prognostic factor in lung cancer and may serve as an effective biomarker for predicting metastasis and prognosis of patients with lung cancer.

Keywords: lung cancer, prognostic genes, GEO, TCGA, bioinformatics analysis, TPBG

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