Cargando…
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...
Autores principales: | , , , , , , |
---|---|
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 |
_version_ | 1783479061619671040 |
---|---|
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. |
format | Online Article Text |
id | pubmed-6910184 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | D.A. Spandidos |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT lingbo identificationofprognosticmarkersoflungcancerthroughbioinformaticsanalysisandinvitroexperiments AT liaoxianjiu identificationofprognosticmarkersoflungcancerthroughbioinformaticsanalysisandinvitroexperiments AT huangyuanhe identificationofprognosticmarkersoflungcancerthroughbioinformaticsanalysisandinvitroexperiments AT lianglingling identificationofprognosticmarkersoflungcancerthroughbioinformaticsanalysisandinvitroexperiments AT jiangyan identificationofprognosticmarkersoflungcancerthroughbioinformaticsanalysisandinvitroexperiments AT pangyaqin identificationofprognosticmarkersoflungcancerthroughbioinformaticsanalysisandinvitroexperiments AT qiguangzi identificationofprognosticmarkersoflungcancerthroughbioinformaticsanalysisandinvitroexperiments |