Dataset Information

Identification of key genes in non-small cell lung cancer by bioinformatics analysis.

ABSTRACT: Background:Non-small cell lung cancer (NSCLC) is one of the most common malignant tumors in the world, and it has become the leading cause of death of malignant tumors. However, its mechanisms are not fully clear. The aim of this study is to investigate the key genes and explore their potential mechanisms involving in NSCLC. Methods:We downloaded gene expression profiles GSE33532, GSE30219 and GSE19804 from the Gene Expression Omnibus (GEO) database and analyzed them by using GEO2R. Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes were used for the functional and pathway enrichment analysis. We constructed the protein-protein interaction (PPI) network by STRING and visualized it by Cytoscape. Further, we performed module analysis and centrality analysis to find the potential key genes. Finally, we carried on survival analysis of key genes by GEPIA. Results:In total, we obtained 685 DEGs. Moreover, GO analysis showed that they were mainly enriched in cell adhesion, proteinaceous extracellular region, heparin binding. KEGG pathway analysis revealed that transcriptional misregulation in cancer, ECM-receptor interaction, cell cycle and p53 signaling pathway were involved in. Furthermore, PPI network was constructed including 249 nodes and 1,027 edges. Additionally, a significant module was found, which included eight candidate genes with high centrality features. Further, among the eight candidate genes, the survival of NSCLC patients with the seven high expression genes were significantly worse, including CDK1, CCNB1, CCNA2, BIRC5, CCNB2, KIAA0101 and MELK. In summary, these identified genes should play an important role in NSCLC, which can provide new insight for NSCLC research.

SUBMITTER: Zhang L

PROVIDER: S-EPMC6911687 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Identification of key genes in non-small cell lung cancer by bioinformatics analysis.

Zhang Li L Peng Rui R Sun Yan Y Wang Jia J Chong Xinyu X Zhang Zheng Z

PeerJ 20191212

<h4>Background</h4>Non-small cell lung cancer (NSCLC) is one of the most common malignant tumors in the world, and it has become the leading cause of death of malignant tumors. However, its mechanisms are not fully clear. The aim of this study is to investigate the key genes and explore their potential mechanisms involving in NSCLC.<h4>Methods</h4>We downloaded gene expression profiles GSE33532, GSE30219 and GSE19804 from the Gene Expression Omnibus (GEO) database and analyzed them by using GEO2 ...[more]

PMID: 31844590

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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 IIα, 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.

Project description:BackgroundNon-small cell lung cancer (NSCLC) is the most prevalent malignant tumor of the lung cancer, for which the molecular mechanisms remain unknown. In this study, we identified novel biomarkers associated with the pathogenesis of NSCLC aiming to provide new diagnostic and therapeutic approaches for NSCLC by bioinformatics analysis.MethodsFrom the Gene Expression Omnibus database, GSE118370 and GSE10072 microarray datasets were obtained. Identifying the differentially expressed genes (DEGs) between lung adenocarcinoma and normal samples was done. By using bioinformatics tools, a protein-protein interaction (PPI) network was constructed, modules were analyzed, and enrichment analyses were performed. The expression and prognostic values of 14 hub genes were validated by the GEPIA database, and the correlation between hub genes and survival in lung adenocarcinoma was assessed by UALCAN, cBioPortal, String and Cytoscape, and Timer tools.ResultsWe found three genes (PIK3R1, SPP1, and PECAM1) that have a clear correlation with OS in the lung adenocarcinoma patient. It has been found that lung adenocarcinoma exhibits high expression of SPP1 and that this has been associated with poor prognosis, while low expression of PECAM1 and PIK3R1 is associated with poor prognosis (P < 0.05). We also found that the expression of SPP1 was associated with miR-146a-5p, while the high expression of miR-146a-5p was related to good prognosis (P < 0.05). On the contrary, the lower miR-21-5p on upstream of PIK3R1 is associated with a higher surviving rate in cancer patients (P < 0.05). Finally, we found that the immune checkpoint genes CD274(PD-L1) and PDCD1LG2(PD-1) were also related to SPP1 in lung adenocarcinoma.ConclusionsThe results indicated that SPP1 is a cancer promoter (oncogene), while PECAM1 and PIK3R1 are cancer suppressor genes. These genes take part in the regulation of biological activities in lung adenocarcinoma, which provides a basis for improving detection and immunotherapeutic targets for lung adenocarcinoma.

Project description:BackgroundSmall cell lung cancer (SCLC), the most malignant of all the lung cancer subtypes, is characterized by drug resistance. This study sought to explore the key genes and pathways associated with the chemoresistance of SCLC.MethodsThe drug sensitivity of chemosensitive and chemoresistance SCLC cell lines was measured by Cell Counting Kit-8 assays. The total RNA from chemosensitive cell line H69 and chemoresistance cell line H69AR cells was extracted and subjected to messenger RNA (mRNA) and long non-coding RNA (lncRNA) microarray analyses. The differentially expressed genes (DEGs) and the differentially expressed lncRNAs (DELs) were screened out with a threshold of a |log fold change | ≥1 and an adjusted P value <0.05. A protein-protein interaction network was constructed, and hub genes were screened out. A lncRNA-mRNA co-expression network was also constructed. Gene Ontology and Kyoto Encyclopedia of Genes, Genomes enrichment analyses and Cis-regulatory element analyses were conducted on the DEGs and the top 10 upregulated DEL-co-expressed DEGs. The expression of the key genes was further analyzed in the GSE149507 data set and validated in H69/H69AR and H446/H446DDP cells by quantitative polymerase chain reaction assays.ResultsThe microarray results showed that a total of 609 mRNAs and 394 lncRNAs were differentially expressed in the chemoresistant SCLC cells. The mammalian target of rapamycin (mTOR) signaling pathway was enriched among the DEGs, the top 10 upregulated DEL-co-expressed DEGs, and the NCRNA00173-co-expressed DEGs, which included IGF1, INS, WNT6, WNT11, WNT2B, and SESN2. IGF1, WNT2B, and SESN2 were downregulated, and WNT11 was upregulated in the SCLC tumor tissues in the GSE149507 data set. Further, IGF1, WNT6, WNT11, and WNT2B were lowlier expressed and SESN2 and NCRNA00173 were more highly expressed in the chemoresistant cells than sensitive cells.ConclusionsThe top 10 upregulated DELs containing NCRNA00173 may be involved in the regulation of drug resistance in SCLC. These DELs may regulate the genes related to the mTOR signaling pathway. These genes may also be biomarkers and potential targets for the treatment of SCLC.

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:Background: Non-small cell lung cancer (NSCLC) is the most common type of lung cancer affecting humans. However, appropriate biomarkers for diagnosis and prognosis have not yet been established. Here, we evaluated the gene expression profiles of patients with NSCLC to identify novel biomarkers. Methods: Three datasets were downloaded from the Gene Expression Omnibus (GEO) database, and differentially expressed genes were analyzed. Venn diagram software was applied to screen differentially expressed genes, and gene ontology functional analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed. Cytoscape was used to analyze protein-protein interactions (PPI) and Kaplan-Meier Plotter was used to evaluate the survival rates. Oncomine database, Gene Expression Profiling Interactive Analysis (GEPIA), and The Human Protein Atlas (THPA) were used to analyze protein expression. Quantitative real-time polymerase (qPCR) chain reaction was used to verify gene expression. Results: We identified 595 differentially expressed genes shared by the three datasets. The PPI network of these differentially expressed genes had 202 nodes and 743 edges. Survival analysis identified 10 hub genes with the highest connectivity, 9 of which (CDC20, CCNB2, BUB1, CCNB1, CCNA2, KIF11, TOP2A, NDC80, and ASPM) were related to poor overall survival in patients with NSCLC. In cell experiments, CCNB1, CCNB2, CCNA2, and TOP2A expression levels were upregulated, and among different types of NSCLC, these four genes showed highest expression in large cell lung cancer. The highest prognostic value was detected for patients who had successfully undergone surgery and for those who had not received chemotherapy. Notably, CCNB1 and CCNA2 showed good prognostic value for patients who had not received radiotherapy. Conclusion: CCNB1, CCNB2, CCNA2, and TOP2A expression levels were upregulated in patients with NSCLC. These genes may be meaningful diagnostic biomarkers and could facilitate the development of targeted therapies.

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Identification of key genes in non-small cell lung cancer by bioinformatics analysis.

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Identification of key genes in non-small cell lung cancer by bioinformatics analysis.

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