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Five genes may predict metastasis in non-small cell lung cancer using bioinformatics analysis.

ABSTRACT: Lung cancer is one of the most common types of malignancy worldwide. The prognosis of lung cancer is poor, due to the onset of metastases. The aim of the present study was to examine lung cancer metastasis-associated genes. To identify novel metastasis-associated targets, our previous study detected the differentially expressed mRNAs and long non-coding RNAs between the large-cell lung cancer high-metastatic 95D cell line and the low-metastatic 95C cell line by microarray assay. In the present study, these differentially expressed genes (DEGs) were analyzed via bioinformatics methods, including Gene Ontology functional analysis and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. A protein-protein interaction network was subsequently constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins online database and Cytoscape software, and 17 hub genes were screened out on the basis of connectivity degree. These hub genes were further validated in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) using the online Gene Expression Profiling Interactive Analysis database. A total of seven hub genes were identified to be significantly differentially expressed in LUAD and LUSC. The prognostic information was detected using Kaplan-Meier plotter. As a result, five genes were revealed to be closely associated with the overall survival time of patients with lung cancer, including phosphoinositide-3-kinase regulatory subunit 1, FYN, thrombospondin 1, nonerythrocytic ?-spectrin 1 and secreted phosphoprotein 1. In addition, lung cancer and adjacent lung tissue samples were used to validate these hub genes by reverse transcription-quantitative polymerase chain reaction. In conclusion, the results of the present study may provide novel metastasis-associated therapeutic strategies or potential biomarkers in non-small cell lung cancer.

SUBMITTER: Sun R

PROVIDER: S-EPMC6607402 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Five genes may predict metastasis in non-small cell lung cancer using bioinformatics analysis.

Sun Ruiying R Meng Xia X Wang Wei W Liu Boxuan B Lv Xin X Yuan Jingyan J Zeng Lizhong L Chen Yang Y Yuan Bo B Yang Shuanying S

Oncology letters 20190619 2

Lung cancer is one of the most common types of malignancy worldwide. The prognosis of lung cancer is poor, due to the onset of metastases. The aim of the present study was to examine lung cancer metastasis-associated genes. To identify novel metastasis-associated targets, our previous study detected the differentially expressed mRNAs and long non-coding RNAs between the large-cell lung cancer high-metastatic 95D cell line and the low-metastatic 95C cell line by microarray assay. In the present s ...[more]

PMID: 31423239

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Project description:Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, accounting for ~80% of all lung cancer cases. The aim of the present study was to identify key genes and pathways in NSCLC, in order to improve understanding of the mechanism of lung cancer. The GSE33532 gene expression dataset, containing 20 normal and 80 NSCLC samples, was used. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to obtain the enrichment data of differently expressed genes (DEGs). Disease modules within NSCLC were constructed by Cytoscape, using protein-protein interaction (PPI) from the Search Tool for the Retrieval of Interacting Genes database. In addition, the Kaplan Meier plotter KMplot was used to assess the top hub genes in the PPI network. As a result, 1,795 genes were identified in NSCLC; 729 were upregulated and 1,066 were downregulated. The results of the GO analysis indicated that the upregulated DEGs were significantly enriched in 'biological processes' (BP), including 'cell cycle and nuclear division'; the downregulated DEGs were also significantly enriched in BP, including 'response to wounding', 'anatomical structure morphogenesis' and 'response to stimulus'. Upregulated DEGs were also enriched in 'cell cycle', 'DNA replication' and the 'tumor protein 53 signaling pathway', while the downregulated DEGs were also enriched in 'complement and coagulation cascades', 'malaria' and 'cell adhesion molecules'. The top 9 hub genes were cyclin-dependent kinase 9 (CDK1), polo-like kinase 1, aurora kinase B, cell division cycle 20, baculoviral initiator of apoptosis repeat containing 5, mitotic checkpoint serine/threonine kinase B, proliferating cell nuclear antigen (PCNA), centromere protein A and MAD2 mitotic arrest deficient-like 1, and the KMplot results revealed that the high expression levels of these genes resulted in significantly low survival rates, compared with low expression samples (P<0.05), with the exception of PCNA and CDK1. In the pathway crosstalk analysis, 26 nodes and 41 interactions were divided into two groups: One module of the two groups primarily included 'metabolism of amino acid' and the other primarily contained 'tumor necrosis signaling' pathways. In conclusion, the present study assisted in improving the understanding of the molecular mechanisms underlying NSCLC development, and the results may help the understanding of the biological mechanism of NSCLC.

Project description:BackgroundNon-small cell lung cancer (NSCLC) is a common malignancy with a high morbidity and mortality rate worldwide, but the driver genes and signaling pathways involved are largely unclear. Herein, our study aimed to identify significant genes with poor outcome and underlying mechanisms in NSCLC using bioinformatics analyses.MethodsGene expression profiles (GSE33532, GSE19188, GSE102287, GSE27262), including 319 NSCLC and 232 adjacent lung tissues, were downloaded from the GEO database. Differentially expressed genes (DEGs) were identified by the GEO2R online tool. Functional and pathway enrichment analyses were performed via the DAVID database. The protein-protein interactions (PPIs) of these DEGs were constructed by the STRING website and visualized by the Cytoscape software platform. The expression of hub genes in NSCLC was validated through the GEPIA database. Kaplan-Meier plotter was used to analyse the survival rate with multivariate Cox regression. The expression of protein tyrosine kinase 2 (PTK2) in NSCLC and adjacent lung tissues was evaluated on the UALCAN database platform.ResultsA total of 225 significant DEGs were obtained between NSCLC and adjacent lung tissues, containing 52 upregulated genes and 173 downregulated genes. The DEGs were clustered based on functions and signaling pathways that may be closely associated with NSCLC occurrence. A total of 174 DEGs were identified from the PPI network complex. Top 10 hub genes were selected by CytoHubba plugin. As independent predictors, seven genes (COL1A1, ADAM12, VWF, OGN, EDN1, CAV1, ITGA8) were associated with poor prognosis in NSCLC via multivariate Cox regression (P<0.01). Four genes (VWF, CAV1, ITGA8, COL1A1) were found to be significantly enriched in the focal adhesion pathway (P=1.04E-04) and to be upstream regulators of PTK2. PTK2 was upregulated in NSCLC and associated with poor survival prognosis in lung squamous cell carcinoma (LUSC).ConclusionsTaken together, the important genes and pathways in NSCLC were identified by using integrated bioinformatics analysis. PTK2 could be a key gene associated with the biological process of NSCLC formation and progression and a potential therapeutic target for NSCLC treatment.

Project description:Non-small cell lung cancer (NSCLC) is a major cause of cancer-associated mortality worldwide, and bone metastasis is the most prevalent event observed in patients with advanced NSCLC. However, the pathogenesis of bone metastases has not been fully elucidated. In the present study, differentially expressed genes (DEGs) were identified by gene expression microarray analysis of NSCLC tissue samples with or without bone metastases. Subsequently, collagen type 6A1 (COL6A1) was chosen as the target gene through Ingenuity Pathway Analysis and reverse transcription-quantitative (RT-q) PCR validation of the top eight DEGs. COL6A1 was overexpressed or knocked down, and the proliferation and invasion of NSCLC cells was assessed using Cell Counting Kit-8, colony formation and Transwell invasion assays. Additionally, the osteogenic capacity of HOB and hES-MP 002.5 cells was assessed using RT-qPCR, western blotting, Alizarin Red and alkaline phosphatase staining. A total of 364 DEGs were identified in NSCLC tissues with bone metastases compared with NSCLC tissues without bone metastases, including 140 upregulated and 224 downregulated genes. Gene Ontology analysis results demonstrated that the upregulated and downregulated genes were primarily enriched in 'cellular process', 'metabolic process' and 'biological regulation'. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis revealed that the upregulated genes were primarily enriched in 'cysteine and methionine metabolism', 'oxidative phosphorylation' and 'ribosome', whereas the downregulated genes were primarily enriched in the 'transcriptional misregulation in cancer', 'ribosome' and 'mitophagy-animal' pathways. COL6A1 was highly expressed in NSCLC tissue samples with bone metastases. Functionally, COL6A1 overexpression induced the proliferation and invasion of HARA cells, and its knockdown inhibited the proliferation and invasion of HARA-B4 cells. Finally, it was demonstrated that HOB and hES-MP 002.5 cells exhibited osteogenic capacity, and overexpression of COL6A1 in HARA cells increased the adhesion of these cells to the osteoblasts, whereas knockdown of COL6A1 in HARA-B4 cells reduced their adhesive ability. In conclusion, COL6A1 may serve as a potential diagnostic marker and therapeutic target for bone metastasis in NSCLC.

Project description:Non-small cell lung cancer (NSCLC) is a leading cause of mortality worldwide. However, the pathogenesis of NSCLC remains to be fully elucidated. Therefore, the present study aimed to explore the differential expression of mRNAs and microRNAs (miRNAs/miRs) in NSCLC and to determine how these RNA molecules interact with one another to affect disease progression. Differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) were identified from the GSE18842, GSE32863 and GSE29250 datasets downloaded from the Gene Expression Omnibus (GEO database). Functional and pathway enrichment analysis were performed based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. STRING, Cytoscape and MCODE were applied to construct a protein-protein interaction (PPI) network and to screen hub genes. The interactions between miRNAs and mRNAs were predicted using miRWalk 3.0 and a miRNA-mRNA regulatory network was constructed. The prognostic value of the identified hub genes was then evaluated via Kaplan-Meier survival analyses using datasets from The Cancer Genome Atlas. A total of 782 DEGs and 46 DEMs were identified from the 3 GEO datasets. The enriched pathways and functions of the DEGs and target genes of the DEMs included osteoclast differentiation, cell adhesion, response to a drug, plasma membrane, extracellular exosome and protein binding. A subnetwork composed of 11 genes was extracted from the PPI network and the genes in this subnetwork were mainly involved in the cell cycle, cell division and DNA replication. A miRNA-gene regulatory network was constructed with 247 miRNA-gene pairs based on 6 DEMs and 210 DEGs. Kaplan-Meier survival analysis indicated that the expression of ubiquitin E2 ligase C, cell division cycle protein 20, DNA topoisomerase II?, aurora kinase A and B, cyclin B2, maternal embryonic leucine zipper kinase, slit guidance ligand 3, phosphoglucomutase 5, endomucin, cysteine dioxygenase type 1, dihydropyrimidinase-like 2, miR-130b, miR-1181 and miR-127 was significantly associated with overall survival of patients with lung adenocarcinoma. In the present study, a miRNA-mRNA regulatory network in NSCLC was established, which may provide future avenues for scientific exploration and therapeutic targeting of NSCLC.

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Five genes may predict metastasis in non-small cell lung cancer using bioinformatics analysis.

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Five genes may predict metastasis in non-small cell lung cancer using bioinformatics analysis.

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