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Identification of prognostic markers of lung cancer through bioinformatics analysis and in vitro experiments

Lung cancer is one of the most common types of cancer worldwide. Understanding the molecular mechanisms underlying the development and progression of lung cancer may improve early diagnosis, treatment and prognosis. The aim of the present study was to examine the pathogenesis of lung cancer and to i...

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Autores principales: Ling, Bo, Liao, Xianjiu, Huang, Yuanhe, Liang, Lingling, Jiang, Yan, Pang, Yaqin, Qi, Guangzi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: D.A. Spandidos 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6910184/
https://www.ncbi.nlm.nih.gov/pubmed/31789390
http://dx.doi.org/10.3892/ijo.2019.4926
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author Ling, Bo
Liao, Xianjiu
Huang, Yuanhe
Liang, Lingling
Jiang, Yan
Pang, Yaqin
Qi, Guangzi
author_facet Ling, Bo
Liao, Xianjiu
Huang, Yuanhe
Liang, Lingling
Jiang, Yan
Pang, Yaqin
Qi, Guangzi
author_sort Ling, Bo
collection PubMed
description Lung cancer is one of the most common types of cancer worldwide. Understanding the molecular mechanisms underlying the development and progression of lung cancer may improve early diagnosis, treatment and prognosis. The aim of the present study was to examine the pathogenesis of lung cancer and to identify potentially novel biomarkers. Gene expression datasets of patients with lung cancer were obtained from the Gene Expression Omnibus. Genes which were most closely associated with lung cancer (core genes) were screened by weighted gene co-expression network analysis. In vitro cell based experiments were further utilized to verify the effects of the core genes on the proliferation of lung cancer cells, adhesion between cells and the matrix, and the associated metabolic pathways. Based on WGCNA screening, two gene modules and five core genes closely associated with lung cancer, including immunoglobulin superfamily member 10 (IGSF10) from the turquoise module, and ribonucleotide reductase regulatory subunit M2, protein regulator of cytokinesis 1, kinesin family member (KIF)14 and KIF2C from the brown module were identified as relevant. Survival analysis and differential gene expression analysis showed that there were significant differences in IGSF10 expression levels between the healthy controls and patients with lung cancer. In patients with lung cancer, IGSF10 expression was decreased, and the overall survival time of patients with lung cancer was significantly shortened. An MTT and colony formation assay showed that IGSF10-knockout significantly increased proliferation of lung cancer cells, and Transwell assays and adhesion experiments further suggested that the adhesion between cells and the matrix was significantly increased in IGSF10-knockout cells. Gene Set Enrichment Analysis showed that the expression level of IGSF10 was significantly associated with the activation of the integrin-β1/focal adhesion kinase (FAK) pathway. Western blotting revealed that knockout of IGSF10 resulted in the activation of the integrin-β1/FAK pathway, as the protein expression levels of integrin-β1, phosphorylated (p)-FAK and p-AKT were significantly upregulated. Activation of the integrin-β1/FAK pathway, following knockout of IGSF10, affected the proliferation and adhesion of lung cancer cells. Therefore, IGSF10 my serve as a potential prognostic marker of lung cancer.
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spelling pubmed-69101842019-12-18 Identification of prognostic markers of lung cancer through bioinformatics analysis and in vitro experiments Ling, Bo Liao, Xianjiu Huang, Yuanhe Liang, Lingling Jiang, Yan Pang, Yaqin Qi, Guangzi Int J Oncol Articles Lung cancer is one of the most common types of cancer worldwide. Understanding the molecular mechanisms underlying the development and progression of lung cancer may improve early diagnosis, treatment and prognosis. The aim of the present study was to examine the pathogenesis of lung cancer and to identify potentially novel biomarkers. Gene expression datasets of patients with lung cancer were obtained from the Gene Expression Omnibus. Genes which were most closely associated with lung cancer (core genes) were screened by weighted gene co-expression network analysis. In vitro cell based experiments were further utilized to verify the effects of the core genes on the proliferation of lung cancer cells, adhesion between cells and the matrix, and the associated metabolic pathways. Based on WGCNA screening, two gene modules and five core genes closely associated with lung cancer, including immunoglobulin superfamily member 10 (IGSF10) from the turquoise module, and ribonucleotide reductase regulatory subunit M2, protein regulator of cytokinesis 1, kinesin family member (KIF)14 and KIF2C from the brown module were identified as relevant. Survival analysis and differential gene expression analysis showed that there were significant differences in IGSF10 expression levels between the healthy controls and patients with lung cancer. In patients with lung cancer, IGSF10 expression was decreased, and the overall survival time of patients with lung cancer was significantly shortened. An MTT and colony formation assay showed that IGSF10-knockout significantly increased proliferation of lung cancer cells, and Transwell assays and adhesion experiments further suggested that the adhesion between cells and the matrix was significantly increased in IGSF10-knockout cells. Gene Set Enrichment Analysis showed that the expression level of IGSF10 was significantly associated with the activation of the integrin-β1/focal adhesion kinase (FAK) pathway. Western blotting revealed that knockout of IGSF10 resulted in the activation of the integrin-β1/FAK pathway, as the protein expression levels of integrin-β1, phosphorylated (p)-FAK and p-AKT were significantly upregulated. Activation of the integrin-β1/FAK pathway, following knockout of IGSF10, affected the proliferation and adhesion of lung cancer cells. Therefore, IGSF10 my serve as a potential prognostic marker of lung cancer. D.A. Spandidos 2019-11-28 /pmc/articles/PMC6910184/ /pubmed/31789390 http://dx.doi.org/10.3892/ijo.2019.4926 Text en Copyright: © Ling et al. This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
spellingShingle Articles
Ling, Bo
Liao, Xianjiu
Huang, Yuanhe
Liang, Lingling
Jiang, Yan
Pang, Yaqin
Qi, Guangzi
Identification of prognostic markers of lung cancer through bioinformatics analysis and in vitro experiments
title Identification of prognostic markers of lung cancer through bioinformatics analysis and in vitro experiments
title_full Identification of prognostic markers of lung cancer through bioinformatics analysis and in vitro experiments
title_fullStr Identification of prognostic markers of lung cancer through bioinformatics analysis and in vitro experiments
title_full_unstemmed Identification of prognostic markers of lung cancer through bioinformatics analysis and in vitro experiments
title_short Identification of prognostic markers of lung cancer through bioinformatics analysis and in vitro experiments
title_sort identification of prognostic markers of lung cancer through bioinformatics analysis and in vitro experiments
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6910184/
https://www.ncbi.nlm.nih.gov/pubmed/31789390
http://dx.doi.org/10.3892/ijo.2019.4926
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