Dataset Information

Identification of genes associated with cancer progression and prognosis in lung adenocarcinoma: Analyses based on microarray from Oncomine and The Cancer Genome Atlas databases.

ABSTRACT:

Background

Lung adenocarcinoma (LUAD) accounts for approximately 40% of all lung cancer patients. There is an urgent need to understand the mechanisms of cancer progression in LUAD and to identify useful biomarkers to predict prognosis.

Methods

In this study, Oncomine database was used to identify potential genes contributed to cancer progression. Bioinformatics analysis including pathway enrichment and text mining was used to explain the potential roles of identified genes in LUAD. The Cancer Genome Atlas database was used to analyze the association of gene expression with survival result.

Results

Our results indicated that 80 genes were significantly dysregulated in LUAD according to four microarrays covering 356 cases of LUAD and 164 cases of normal lung tissues. Twenty genes were consistently and stably dysregulated by more than twofold. Ten of 20 genes had a relationship with overall survival or disease-free survival in a cohort of 516 LUAD patients, and 19 genes were associated with tumor stage, gender, age, lymph node, or smoking. Low expression of AGER and high expression of CCNB1 were specifically associated with poor survival.

Conclusion

Our findings implicate AGER and CCNB1 might be potential biomarkers for diagnosis and prognosis targets for LUAD.

SUBMITTER: Liu W

PROVIDER: S-EPMC6393652 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Publications

Identification of genes associated with cancer progression and prognosis in lung adenocarcinoma: Analyses based on microarray from Oncomine and The Cancer Genome Atlas databases.

Liu Wei W Ouyang Songyun S Zhou Zhigang Z Wang Meng M Wang Tingting T Qi Yu Y Zhao Chunling C Chen Kuisheng K Dai Liping L

Molecular genetics & genomic medicine 20181216 2

<h4>Background</h4>Lung adenocarcinoma (LUAD) accounts for approximately 40% of all lung cancer patients. There is an urgent need to understand the mechanisms of cancer progression in LUAD and to identify useful biomarkers to predict prognosis.<h4>Methods</h4>In this study, Oncomine database was used to identify potential genes contributed to cancer progression. Bioinformatics analysis including pathway enrichment and text mining was used to explain the potential roles of identified genes in LUA ...[more]

PMID: 30556321

Similar Datasets

Project description:Previous studies have investigated the association between HOXA13 and non-small cell lung cancer. However, the role of HOXA13 expression in the occurrence and progression of lung adenocarcinoma (LUAD) has not yet been investigated. In the present study, HOXA13-related data mining of The Cancer Genome Atlas (TCGA), polymerase chain reaction (PCR) data from our cases and the case information in Oncomine was conducted for validation. The expression data of HOXA13 in lung cancer cell lines were also collected from the Cancer Cell Line Encyclopedia (CCLE) database for further verification. A comprehensive meta-analysis of the expression of HOXA13 was also performed, integrating the data of TCGA, in-house PCR and Oncomine. Genes that were co-expressed with HOXA13 were subsequently identified through cBioPortal and Multi Experiment Matrix (MEM), and the potential role and mechanism of HOXA13 in LUAD was investigated. The expression value of HOXA13 in the LUAD group, which comprised 237 cases, was 3.74±2.694, significantly higher than its expression value in the non-cancerous group (0.92±0.608, P<0.001). The pooled SMD for HOXA13 was 0.346 (95% CI, 0.052-0.640; P=0.068; I2=51.3%; P=0.021), The meta-analysis of diagnostic tests revealed that the area under the summary receiver operating characteristic curve (SROC) was 0.78 (95% CI, 0.75-0.82). The results demonstrated that HOXA13 is highly expressed in LUAD. In addition to the studies on HOXA13 expression in tissues, the expression data of HOXA13 in lung cancer cell lines were also collected from the CCLE database for further verification of these conclusions. Genes that were co-expressed with HOXA13 were identified for pathway analysis. The most enriched Gene Ontology terms in the genes co-expressed with HOXA13 were positive regulation of transcription from RNA polymerase II promoter, signal transduction and positive regulation of GTPase activity in biological process; cytoplasm, integral component of membrane and plasma membrane in cellular component; and significantly involved in protein binding, transcription factor activity, sequence-specific DNA binding and sequence-specific DNA binding in molecular function. Kyoto Encyclopedia of Genes and Genomes analysis revealed that these target genes were clearly involved in Pathways in cancer, Proteoglycans in cancer and cAMP signaling pathway. The hub genes obtained from the four protein-protein interaction networks were associated with HOXA13. The results of the bioinformatics research in the present study revealed that HOXA13 may influence the expression of these hub genes in such a way as to promote the occurrence and development of LUAD. In conclusion, the expression of HOXA13 in patients with LUAD and its potential clinical value were analyzed comprehensively in the present study using data from a variety of sources. Through bioinformatics analysis, evidence that HOXA13 may promote the occurrence and development of LUAD was obtained.

Project description:BACKGROUNDS:Lung adenocarcinoma (LUAD) is one of the most common malignancies, and is a serious threat to human health. The aim of the present study was to assess potential biomarkers for the prognosis of LUAD through the analysis of gene expression microarrays. METHODS:The gene expression data for GSE118370 was downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between normal lung and LUAD samples were screened using the R language. The DAVID database was used to analyze the functions and pathways of DEGs. The STRING database was used to the map protein-protein interaction (PPI) networks, and these were visualized with the Cytoscape software. Finally, the prognostic analysis of the hub gene in the PPI network was performed using the Kaplan-Meier tool. RESULTS:A total of 406 downregulated and 203 upregulated DEGs were identified. The GO analysis results revealed that downregulated DEGs were significantly enriched in angiogenesis, calcium ion binding and cell adhesion. The upregulated DEGs were significantly enriched in the extracellular matrix disassembly, collagen catabolic process, chemokine-mediated signaling pathway and endopeptidase inhibitor activity. The KEGG pathway analysis revealed that downregulated DEGs were enriched in neuroactive ligand-receptor interaction, hematopoietic cell lineage and vascular smooth muscle contraction, while upregulated DEGs were enriched in phototransduction. In addition, the top 10 hub genes and the most closely interacting modules of the top 3 proteins in the PPI network were screened. Finally, the independent prognostic value of each hub gene in LUAD patients was analyzed through the Kaplan-Meier plotter. Seven hub genes (ADCY4, S1PR1, FPR2, PPBP, NMU, PF4, and GCG) were closely correlated to overall survival time. CONCLUSION:The discovery of these candidate genes and pathways reveals the etiology and molecular mechanisms of LUAD, providing ideas and guidance for the development of new therapeutic approaches to LUAD.

Dataset Information

Identification of genes associated with cancer progression and prognosis in lung adenocarcinoma: Analyses based on microarray from Oncomine and The Cancer Genome Atlas databases.

Background

Methods

Results

Conclusion

Publications

Identification of genes associated with cancer progression and prognosis in lung adenocarcinoma: Analyses based on microarray from Oncomine and The Cancer Genome Atlas databases.

Similar Datasets

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