Dataset Information

Identification of key differentially expressed genes associated with non?small cell lung cancer by bioinformatics analyses.

ABSTRACT: Increasing evidence has indicated that the abnormal expressions of certain genes serve important roles in tumorigenesis, progression and metastasis. The aim of the present study was to explore the key differentially expressed genes (DEGs) between non?small cell lung cancer (NSCLC) and matched normal lung tissues by analyzing 4 different mRNA microarray datasets downloaded from the Gene Expression Omnibus (GEO) database. In improving the reliability of the bioinformatics analysis, the DEGs in each dataset that met the cut?off criteria (adjust P?value <0.05 and |log2fold?change (FC)|>1) were intersected with each other, from which 195 were identified (consisting of 57 upregulated and 138 downregulated DEGs). The GO analysis results revealed that the upregulated DEGs were significantly enriched in various biological processes (BP), including cell cycle, mitosis and cell proliferation while the downregulated DEGs were significantly enriched in angiogenesis and response to drug and cell adhesion. The hub genes, including CCNB1, CCNA2, CEP55, PBK and HMMR, were identified based on the protein?protein interaction (PPI) network. The Kaplan?Meier survival analysis indicated that the high expression level of each of these hub genes correlates with poorer overall survival in all patients with NSCLC, which indicates that they may serve important roles in the progression of NSCLC. In conclusion, the DEGs and hub genes identified in the present study may contribute to the comprehensive understanding of the molecular mechanisms of NSCLC and may be used as diagnostic and prognostic biomarkers as well as molecular targets for the treatment of NSCLC.

SUBMITTER: Xiao Y

PROVIDER: S-EPMC5928621 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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Identification of key differentially expressed genes associated with non‑small cell lung cancer by bioinformatics analyses.

Xiao Yubo Y Feng Min M Ran Haiying H Han Xiao X Li Xuegang X

Molecular medicine reports 20180309 5

Increasing evidence has indicated that the abnormal expressions of certain genes serve important roles in tumorigenesis, progression and metastasis. The aim of the present study was to explore the key differentially expressed genes (DEGs) between non‑small cell lung cancer (NSCLC) and matched normal lung tissues by analyzing 4 different mRNA microarray datasets downloaded from the Gene Expression Omnibus (GEO) database. In improving the reliability of the bioinformatics analysis, the DEGs in eac ...[more]

PMID: 29532892

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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:Background:BRCA1 and BRCA2 genes are currently proven to be closely related to high lifetime risks of breast cancer. To date, the closely related genes to BRCA1/2 mutations in breast cancer remains to be fully elucidated. This study aims to identify the gene expression profiles and interaction networks influenced by BRCA1/2 mutations, so as to reflect underlying disease mechanisms and provide new biomarkers for breast cancer diagnosis or prognosis. Methods:Gene expression profiles from The Cancer Genome Atlas (TCGA) database were downloaded and combined with cBioPortal website to identify exact breast cancer patients with BRCA1/2 mutations. Gene set enrichment analysis (GSEA) was used to analyze some enriched pathways and biological processes associated BRCA mutations. For BRCA1/2-mutant breast cancer, wild-type breast cancer and corresponding normal tissues, three independent differentially expressed genes (DEGs) analysis were performed to validate potential hub genes with each other. Protein-protein interaction (PPI) networks, survival analysis and diagnostic value assessment helped identify key genes associated with BRCA1/2 mutations. Results:The regulation process of cell cycle was significantly enriched in mutant group compared with wild-type group. A total of 294 genes were identified after analysis of DEGs between mutant patients and wild-type patients. Interestingly, by the other two comparisons, we identified 43 overlapping genes that not only significantly expressed in wild-type breast cancer patients relative to normal tissues, but more significantly expressed in BRCA1/2-mutant breast patients. Based on the STRING database and cytoscape software, we constructed a PPI network using 294 DEGs. Through topological analysis scores of the PPI network and 43 overlapping genes, we sought to select some genes, thereby using survival analysis and diagnostic value assessment to identify key genes pertaining to BRCA1/2-mutant breast cancer. CCNE1, NPBWR1, A2ML1, EXO1 and TTK displayed good prognostic/diagnostic value for breast cancer and BRCA1/2-mutant breast cancer. Conclusion:Our research provides comprehensive and new insights for the identification of biomarkers connected with BRCA mutations, availing diagnosis and treatment of breast cancer and BRCA1/2-mutant breast cancer patients.

Dataset Information

Identification of key differentially expressed genes associated with non?small cell lung cancer by bioinformatics analyses.

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Identification of key differentially expressed genes associated with non‑small cell lung cancer by bioinformatics analyses.

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