Dataset Information

Identification of key genes and pathways in uterine leiomyosarcoma through bioinformatics analysis.

ABSTRACT: Uterine leiomyosarcoma (uLMS) is a rare but malignant gynaecological tumour with a poor survival outcome. The present study was aimed at identifying the key genes and pathways in the development of uLMS through bioinformatics analysis. To minimize the frequency of false-positive results of the bioinformatics analysis, 3 microarrays including GSE764, GSE64763 and GSE68312 were downloaded from Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were screened out using the online tool GEO2R. Then, Gene Ontology and Kyoto Encyclopaedia of Genes and Genomes pathway enrichment analyses were performed using the Database for Annotation, Visualization and Integrated Discovery. Finally, a protein-protein interaction (PPI) network of the DEGs was constructed using Cytoscape, and module analysis was conducted using the plug-in MCODE. A total of 95 DEGs including 21 upregulated genes and 74 downregulated genes were identified. The upregulated DEGs were annotated with 'DNA metabolic process', 'nucleobase-containing compound biosynthetic process' and 'cellular macromolecule biosynthetic process', while the downregulated DEGs were annotated with 'cellular response to chemical stimulus', 'movement of cell or subcellular component' and 'response to inorganic substances'. The results of the PPI network analysis demonstrated that matrix metallopeptidase 9, apolipoprotein E, cyclin E1 and syndecan 1 were the predominant upregulated genes in uLMS. Additionally, the genes in the main module were enriched in 'proteoglycans in cancer', 'p53 signalling pathway' and 'extracellular matrix-receptor interaction'. The key genes and pathways identified in the present study may provide valuable clues for clarifying the molecular mechanism underlying the development of uLMS and demonstrate promise for use as diagnostic markers and therapeutic targets.

SUBMITTER: Zang Y

PROVIDER: S-EPMC5958873 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Identification of key genes and pathways in uterine leiomyosarcoma through bioinformatics analysis.

Zang Yuqin Y Gu Lina L Zhang Yanfang Y Wang Yingmei Y Xue Fengxia F

Oncology letters 20180416 6

Uterine leiomyosarcoma (uLMS) is a rare but malignant gynaecological tumour with a poor survival outcome. The present study was aimed at identifying the key genes and pathways in the development of uLMS through bioinformatics analysis. To minimize the frequency of false-positive results of the bioinformatics analysis, 3 microarrays including GSE764, GSE64763 and GSE68312 were downloaded from Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were screened out using the online t ...[more]

PMID: 29844831

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Project description:Nasopharyngeal carcinoma (NPC) is one of the most common malignancies in the head and neck. The aim of the current study was to identify the key pathways and genes involved in NPC through bioinformatics analysis and to identify potential molecular mechanisms underlying NPC proliferation and progression. Three gene expression profiles (GSE12452, GSE34573 and GSE64634) were downloaded from the Gene Expression Omnibus database. A total of 76 samples were analyzed, of which 59 were NPC samples and 17 were normal samples. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were subsequently conducted. The protein-protein interaction (PPI) network of the differentially expressed genes (DEGs) was constructed using Cytoscape software. Analysis of GSE12452, GSE34573 and GSE64634 datasets identified 1,301 (553 upregulated and 748 downregulated), 1,232 (348 upregulated and 884 downregulated) and 1,218 (555 upregulated and 663 downregulated) DEGs, respectively. Using Venn diagram analysis, 268 DEGs (59 upregulated and 209 downregulated) that intersected all three datasets, were selected for further analysis. The results of GO analysis revealed that upregulated DEGs were significantly enriched in biological processes, including 'cell adhesion', 'cell division', 'mitosis' and 'mitotic cell cycle'. The downregulated DEGs were mainly enriched in 'microtubule-based movement', 'cilium movement', 'cilium axoneme assembly' and 'epithelial cell differentiation'. The KEGG pathway analysis results revealed that the upregulated DEGs were highly associated with several pathways, including 'extracellular matrix-receptor interaction', 'human papillomavirus infection', 'arrhythmogenic right ventricular cardiomyopathy' and 'focal adhesion', whereas the downregulated DEGs were enriched in 'metabolic pathways', 'Huntington's disease', 'fluid shear stress and atherosclerosis' and 'chemical carcinogenesis'. On the basis of the PPI network of the DEGs, the following top 10 hub genes were identified: Dynein axonemal light intermediate chain 1, dynein axonemal intermediate chain 2, calmodulin 1, coiled-coil domain containing 114, dynein axonemal heavy chain 5, radial spoke head 9 homolog, radial spoke head component 4A, NDC80 kinetochore complex component, thymidylate synthetase and coiled-coil domain containing 39. In conclusion, by performing a comprehensive bioinformatics analysis of DEGs, putative targets that could be used to elucidate the molecular mechanisms underlying NPC were identified.

Project description:BackgroundAtherosclerosis (AS) is a chronic inflammatory disease that might induce severe cardiovascular events, such as myocardial infarction and cerebral infarction. These risk factors in the pathogenesis of AS remain uncertain and further research is needed. This study aims to explore the potential molecular mechanisms of AS by bioinformatics analyses.MethodsGSE100927 gene expression profiles, including 69 AS samples and 35 healthy controls, were downloaded from Gene Expression Omnibus database and indenfied for key genes and pathways in AS.ResultsA total of 443 differentially expressed genes (DEGs) between control and AS were identified, including 323 down-regulated genes and 120 up-regulated genes. The Gene ontology terms enriched by the up-regulated DEGs were associated with the regulation of leukocyte activation, endocytic vesicle, and cytokine binding, while the down-regulated DEGs were associated with negative regulation of cell growth, extracellular matrix, and G protein-coupled receptor binding. KEGG pathway analysis showed that the up-regulated DEGs were enriched in Osteoclast differentiation and Phagosome, while the down-regulated DEGs were enriched in vascular smooth muscle contraction and cGMP-PKG signaling pathway. Using the modular analysis of Cytoscape, we identified 3 modules mainly involved in Leishmaniasis and Osteoclast differentiation. The GSEA analysis showed the up-regulated gene sets were enriched in the ribosome, ascorbated metabolism, and propanoate metabolism. The LASSO Cox regression analysis showed the top 3 genes were TNF, CX3CR1, and COL1R1. Finally, we found these immune cells were conferred significantly higher infiltrating density in the AS group.ConclusionsOur data showed the pathway of Osteoclast differentiation and Leishmaniasis was involved in the AS process and we developed a three-gene model base on the prognosis of AS. These findings clarified the gene regulatory network of AS and may provide a novel target for AS therapy.

Dataset Information

Identification of key genes and pathways in uterine leiomyosarcoma through bioinformatics analysis.

Publications

Identification of key genes and pathways in uterine leiomyosarcoma through bioinformatics analysis.

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