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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

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...

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Autores principales: Liu, Xiao, Wang, Jun, Chen, Mei, Liu, Shilan, Yu, Xiaodan, Wen, Fuqiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove Medical Press 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6345189/
https://www.ncbi.nlm.nih.gov/pubmed/30718962
http://dx.doi.org/10.2147/OTT.S183944
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author Liu, Xiao
Wang, Jun
Chen, Mei
Liu, Shilan
Yu, Xiaodan
Wen, Fuqiang
author_facet Liu, Xiao
Wang, Jun
Chen, Mei
Liu, Shilan
Yu, Xiaodan
Wen, Fuqiang
author_sort Liu, Xiao
collection PubMed
description 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.
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spelling pubmed-63451892019-02-04 Combining data from TCGA and GEO databases and reverse transcription quantitative PCR validation to identify gene prognostic markers in lung cancer Liu, Xiao Wang, Jun Chen, Mei Liu, Shilan Yu, Xiaodan Wen, Fuqiang Onco Targets Ther Original Research 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. Dove Medical Press 2019-01-21 /pmc/articles/PMC6345189/ /pubmed/30718962 http://dx.doi.org/10.2147/OTT.S183944 Text en © 2019 Liu et al. 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 (http://creativecommons.org/licenses/by-nc/3.0/). 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.
spellingShingle Original Research
Liu, Xiao
Wang, Jun
Chen, Mei
Liu, Shilan
Yu, Xiaodan
Wen, Fuqiang
Combining data from TCGA and GEO databases and reverse transcription quantitative PCR validation to identify gene prognostic markers in lung cancer
title Combining data from TCGA and GEO databases and reverse transcription quantitative PCR validation to identify gene prognostic markers in lung cancer
title_full Combining data from TCGA and GEO databases and reverse transcription quantitative PCR validation to identify gene prognostic markers in lung cancer
title_fullStr Combining data from TCGA and GEO databases and reverse transcription quantitative PCR validation to identify gene prognostic markers in lung cancer
title_full_unstemmed Combining data from TCGA and GEO databases and reverse transcription quantitative PCR validation to identify gene prognostic markers in lung cancer
title_short Combining data from TCGA and GEO databases and reverse transcription quantitative PCR validation to identify gene prognostic markers in lung cancer
title_sort combining data from tcga and geo databases and reverse transcription quantitative pcr validation to identify gene prognostic markers in lung cancer
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6345189/
https://www.ncbi.nlm.nih.gov/pubmed/30718962
http://dx.doi.org/10.2147/OTT.S183944
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