Dataset Information

[Bioinformatic screening of key genes controlling the development and progression of lung cancer].

ABSTRACT: Lung cancer is the most common cancer in the world. The gene expression profiling of lung cancer has been extensively investigated. However, only a few studies have identified the possible pathways and significant genes related to lung cancer. The aim of this study is to explore the large number of lung cancer-related microarray datasets and identify the crucial genes that can benefit the understanding of the progression and development of this disease.To identify the genes that effected lung cancer at the mRNA level, gene set enrichment analysis was used to analyze six selected gene expression datasets.Among the six gene expression datasets, 3 up-regulated and 26 down-regulated pathways were found by gene set enrichment analysis. We found 11 significant genes with P < 0.05 from the results of tight junction meta-analysis of the six data sets.The tight junction pathway plays an important role in the study of the occurrence and development of lung cancer. Significant genes within the pathways will be further discussed in future studies.

SUBMITTER: Wang J

PROVIDER: S-EPMC6000034 | biostudies-literature | 2012 Nov

REPOSITORIES: biostudies-literature

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[Bioinformatic screening of key genes controlling the development and progression of lung cancer].

Wang Junlong J Shi Junjie J Liu Wenzhou W Sun Yu Y Zhou Huafu H

Zhongguo fei ai za zhi = Chinese journal of lung cancer 20121101 11

<h4>Background and objective</h4>Lung cancer is the most common cancer in the world. The gene expression profiling of lung cancer has been extensively investigated. However, only a few studies have identified the possible pathways and significant genes related to lung cancer. The aim of this study is to explore the large number of lung cancer-related microarray datasets and identify the crucial genes that can benefit the understanding of the progression and development of this disease.<h4>Method ...[more]

PMID: 23164350

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Project description:Background Lung cancer is one of the most common cancers and the primary cause of cancer-related deaths in the world. The 5-year survival of lung cancer patients is lower than 15%. As a common subtype of lung cancer, lung adenocarcinoma still has a high morbidity and mortality, although many strategies have been made, such as surgical operation, chemotherapy, targeted therapy. The use of gene expression microarray has provided a feasible and effective approach for the study on lung cancer. However, the biomarkers and potential therapeutic targets of lung adenocarcinomas are still not completely identified. Our study is aimed to find biomarkers and therapeutic targets of lung adenocarcinomas by identifying the key protein-coding gene in lung adenocarcinomas by bioinformatical approaches. Methods We selected and obtained messenger RNA microarray datasets from Gene Expression Omnibus database to identify differentially expressed genes between lung adenocarcinomas and normal lung tissue. The differentially expressed genes were clarified by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, the protein-protein interaction (PPI) network and statistical analyses. Subsequently, quantitative real-time PCR was used to verify the results of bioinformatic analysis. Results We obtained 1,264, 896 and 408 differentially expressed genes from GSE32863, GSE43458 and GSE63459, respectively. The 242 common differentially expressed genes in three datasets were related to cell adhesion molecules, ECM-receptor interaction, leukocyte transendothelial migration according to KEGG analysis. GO analysis showed that these common differentially expressed genes were enriched in tumor-related functions. ASPM, CCNB2, CDC20, CDC45, MELK, TOP2A and UBE2T and KIAA0101 have the strongest protein-protein interaction relationships based on protein-protein interaction networks. Survival analysis showed that these nine genes were closely related to the survival of lung adenocarcinomas. The further qRT-PCR assays indicated that seven key genes (ASPM, CCNB2, CDC20, CDC45, MELK, TOP2A and UBE2T) display differential profile between clinical lung adenocarcinoma specimens and their matched normal tissues. Conclusions ASPM, CCNB2, CDC20, CDC45, MELK, TOP2A and UBE2T may be key protein coding genes in lung adenocarcinoma, and deserve further study to verify their feasibility and effectiveness as biomarkers and therapeutic targets for lung adenocarcinomas.

Project description:Background: The molecular mechanism of tumorigenesis remains to be fully understood in breast cancer. It is urgently required to identify genes that are associated with breast cancer development and prognosis and to elucidate the underlying molecular mechanisms. In the present study, we aimed to identify potential pathogenic and prognostic differentially expressed genes (DEGs) in breast adenocarcinoma through bioinformatic analysis of public datasets. Methods: Four datasets (GSE21422, GSE29431, GSE42568, and GSE61304) from Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) dataset were used for the bioinformatic analysis. DEGs were identified using LIMMA Package of R. The GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) analyses were conducted through FunRich. The protein-protein interaction (PPI) network of the DEGs was established through STRING (Search Tool for the Retrieval of Interacting Genes database) website, visualized by Cytoscape and further analyzed by Molecular Complex Detection (MCODE). UALCAN and Kaplan-Meier (KM) plotter were employed to analyze the expression levels and prognostic values of hub genes. The expression levels of the hub genes were also validated in clinical samples from breast cancer patients. In addition, the gene-drug interaction network was constructed using Comparative Toxicogenomics Database (CTD). Results: In total, 203 up-regulated and 118 down-regulated DEGs were identified. Mitotic cell cycle and epithelial-to-mesenchymal transition pathway were the major enriched pathways for the up-regulated and down-regulated genes, respectively. The PPI network was constructed with 314 nodes and 1,810 interactions, and two significant modules are selected. The most significant enriched pathway in module 1 was the mitotic cell cycle. Moreover, six hub genes were selected and validated in clinical sample for further analysis owing to the high degree of connectivity, including CDK1, CCNA2, TOP2A, CCNB1, KIF11, and MELK, and they were all correlated to worse overall survival (OS) in breast cancer. Conclusion: These results revealed that mitotic cell cycle and epithelial-to-mesenchymal transition pathway could be potential pathways accounting for the progression in breast cancer, and CDK1, CCNA2, TOP2A, CCNB1, KIF11, and MELK may be potential crucial genes. Further, it could be utilized as new biomarkers for prognosis and potential new targets for drug synthesis of breast cancer.

Dataset Information

[Bioinformatic screening of key genes controlling the development and progression of lung cancer].

Publications

[Bioinformatic screening of key genes controlling the development and progression of lung cancer].

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