miR-190, CDK1, MCM10 and NDC80 predict the prognosis of the patients with lung cancer

Lung cancer (LC), which includes small-cell lung carcinoma (SCLC) and non-small-cell lung carcinoma (NSCLC), is common and has a high fatality rate. This study aimed to reveal the prognostic mechanisms of LC. GSE30219 was extracted from the Gene Expression Omnibus (GEO) database, and included 293 LC...

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Main Authors: Gao Li-Wei, Wang Guo-Liang
Format: Article
Language:English
Published: Sciendo 2019-01-01
Series:Romanian Journal of Laboratory Medicine
Subjects:
Online Access:https://doi.org/10.2478/rrlm-2019-0001
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spelling doaj-dbb88f20bc07413da213856221f636fd2021-09-05T14:01:30ZengSciendoRomanian Journal of Laboratory Medicine2284-56232019-01-01271152410.2478/rrlm-2019-0001rrlm-2019-0001miR-190, CDK1, MCM10 and NDC80 predict the prognosis of the patients with lung cancerGao Li-Wei0Wang Guo-Liang1Department of Oncology, General Hospital of Pingmei Shenma Medical Group, Pingdingshan, Henan 467000, ChinaDepartment of Research & Development, Henan Zhongping Genetic Technology Co., Ltd., Zhengzhou, Henan 450000, ChinaLung cancer (LC), which includes small-cell lung carcinoma (SCLC) and non-small-cell lung carcinoma (NSCLC), is common and has a high fatality rate. This study aimed to reveal the prognostic mechanisms of LC. GSE30219 was extracted from the Gene Expression Omnibus (GEO) database, and included 293 LC samples and 14 normal lung samples. Differentially expressed genes (DEGs) were identified using the Limma package, and subjected to pathway enrichment analysis using DAVID. MicroRNAs (miRNAs) targeting the DEGs were predicted using Webgestalt. Cytoscape software was used to build a protein-protein interaction (PPI) network and to identify significant network modules. Survival analysis was conducted using Survminer and Survival packages, and validation was performed using The Cancer Genome Atlas (TCGA) dataset. The good and poor prognosis groups contained 518 DEGs. miR-190, miR-493, and miR-218 for the upregulated genes and miR-302, miR-200, and miR-26 for the downregulated genes were predicted. Three network modules (module 1, 2, and 3) were identified from the PPI network. CDK1, MCM10, and NDC80 were the core nodes of module 1, 2, and 3, respectively. In module 1, CDK1 interacted with both CCNB1 and CCNB2. Additionally, CDK1, CCNB1, CCNB2, MCM10, and NDC80 expression levels correlated with clinical survival and were identified as DEGs in both GSE30219 and the TCGA dataset. miR-190, miR-493, miR-218, miR-200, and miR-302 might act in LC by targeting the DEGs. CDK1, CCNB1, CCNB2, MCM10, and NDC80 might also influence the prognosis of LC.https://doi.org/10.2478/rrlm-2019-0001lung cancerdifferentially expressed genesmicrornasenrichment analysisprotein-protein interaction network
collection DOAJ
language English
format Article
sources DOAJ
author Gao Li-Wei
Wang Guo-Liang
spellingShingle Gao Li-Wei
Wang Guo-Liang
miR-190, CDK1, MCM10 and NDC80 predict the prognosis of the patients with lung cancer
Romanian Journal of Laboratory Medicine
lung cancer
differentially expressed genes
micrornas
enrichment analysis
protein-protein interaction network
author_facet Gao Li-Wei
Wang Guo-Liang
author_sort Gao Li-Wei
title miR-190, CDK1, MCM10 and NDC80 predict the prognosis of the patients with lung cancer
title_short miR-190, CDK1, MCM10 and NDC80 predict the prognosis of the patients with lung cancer
title_full miR-190, CDK1, MCM10 and NDC80 predict the prognosis of the patients with lung cancer
title_fullStr miR-190, CDK1, MCM10 and NDC80 predict the prognosis of the patients with lung cancer
title_full_unstemmed miR-190, CDK1, MCM10 and NDC80 predict the prognosis of the patients with lung cancer
title_sort mir-190, cdk1, mcm10 and ndc80 predict the prognosis of the patients with lung cancer
publisher Sciendo
series Romanian Journal of Laboratory Medicine
issn 2284-5623
publishDate 2019-01-01
description Lung cancer (LC), which includes small-cell lung carcinoma (SCLC) and non-small-cell lung carcinoma (NSCLC), is common and has a high fatality rate. This study aimed to reveal the prognostic mechanisms of LC. GSE30219 was extracted from the Gene Expression Omnibus (GEO) database, and included 293 LC samples and 14 normal lung samples. Differentially expressed genes (DEGs) were identified using the Limma package, and subjected to pathway enrichment analysis using DAVID. MicroRNAs (miRNAs) targeting the DEGs were predicted using Webgestalt. Cytoscape software was used to build a protein-protein interaction (PPI) network and to identify significant network modules. Survival analysis was conducted using Survminer and Survival packages, and validation was performed using The Cancer Genome Atlas (TCGA) dataset. The good and poor prognosis groups contained 518 DEGs. miR-190, miR-493, and miR-218 for the upregulated genes and miR-302, miR-200, and miR-26 for the downregulated genes were predicted. Three network modules (module 1, 2, and 3) were identified from the PPI network. CDK1, MCM10, and NDC80 were the core nodes of module 1, 2, and 3, respectively. In module 1, CDK1 interacted with both CCNB1 and CCNB2. Additionally, CDK1, CCNB1, CCNB2, MCM10, and NDC80 expression levels correlated with clinical survival and were identified as DEGs in both GSE30219 and the TCGA dataset. miR-190, miR-493, miR-218, miR-200, and miR-302 might act in LC by targeting the DEGs. CDK1, CCNB1, CCNB2, MCM10, and NDC80 might also influence the prognosis of LC.
topic lung cancer
differentially expressed genes
micrornas
enrichment analysis
protein-protein interaction network
url https://doi.org/10.2478/rrlm-2019-0001
work_keys_str_mv AT gaoliwei mir190cdk1mcm10andndc80predicttheprognosisofthepatientswithlungcancer
AT wangguoliang mir190cdk1mcm10andndc80predicttheprognosisofthepatientswithlungcancer
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