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:BackgroundLung cancer is the most commonly diagnosed cancer and the leading cause of cancer-related death worldwide. Moreover, it is highly susceptible to distant metastasis, which is the main cause of pain in advanced lung cancer, and frequently occurs in the bone. This study aimed to identify the differentially expressed genes (DEGs) related to metastatic bone disease in lung cancer using bioinformatics methods and to analyze the risk factors influencing the incidence of secondary bone metastasis in lung cancer.MethodsGene expression profiles from the GSE175601 and GSE10799 datasets in the Gene Expression Omnibus (GEO) database were analyzed to screen for the DEGs associated with lung cancer bone metastasis. The STRING database was used to construct a protein-protein interaction (PPI) network, and the MCODE plugin was used to identify the key genes. The expression of these important genes in lung tumor tissues and their correlation with prognosis were validated in The Cancer Genome Atlas (TCGA) database. An examination of clinical data from patients diagnosed with stage IV lung adenocarcinoma treated at the Anhui No. 2 Provincial People's Hospital was conducted. Immunohistochemistry was used to examine the expression of key genes in lung cancer tumor tissues. A binary logistic regression analysis was conducted to examine the interactions in the expression of critical genes associated with bone metastasis in lung carcinoma patients.ResultsIn total, 59 DEGs were identified in the GSE175601 and GSE10799 datasets through Venn diagram construction. The PPI network analysis revealed two significant modules and eight candidate genes (LAPTM5, LCP2, CD53, ARHGAP25, C1QA, DES, MYH11, and VIM). According to TCGA database analysis, in carcinogenic tissues of the lung, the expression of these eight critical genes is downregulated. Further, only the lung cancer patients who had high expressions of ARHGAP25 had an improved progress-free interval (PFI) (P<0.05), disease-specific survival (DSS), and overall survival (OS). Of the 49 with stage IV lung adenocarcinoma patients included in the study, 27 (55.10%) developed bone metastasis. The immunohistochemical (IHC) results indicated that the expression score of ARHGAP25 was significantly lower in the group with bone metastasis (3.93±2.95) than the group without bone metastasis (6.64±3.62) (P=0.006). The proportion of patients with low ARHGAP25 expression was significantly higher in the group with bone metastasis (70.37%, 19/27) than the group without bone metastasis (31.82%, 7/22) (P=0.007). The binary logistic regression analysis identified serum alkaline phosphatase (ALP) and ARHGAP25 expression levels as independent risk factors for the occurrence of secondary bone metastatic disease in lung carcinoma patients.ConclusionsThe key gene ARHGAP25 identified through bioinformatics for lung cancer bone metastasis was significantly downregulated. Its low expression constitutes an independent risk factor for secondary bone metastatic disease in patients with lung carcinoma.

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.

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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