Dataset Information

Integrated analysis of differentially expressed genes in breast cancer pathogenesis.

ABSTRACT: The present study aimed to detect the differences between breast cancer cells and normal breast cells, and investigate the potential pathogenetic mechanisms of breast cancer. The sample GSE9574 series was downloaded, and the microarray data was analyzed to identify differentially expressed genes (DEGs). Gene Ontology (GO) cluster analysis using the GO Enrichment Analysis Software Toolkit platform and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis for DEGs was conducted using the Gene Set Analysis Toolkit V2. In addition, a protein-protein interaction (PPI) network was constructed, and target sites of potential transcription factors and potential microRNA (miRNA) molecules were screened. A total of 106 DEGs were identified in the current study. Based on these DEGs, a number of bio-pathways appear to be altered in breast cancer, including a number of signaling pathways and other disease-associated pathways, as indicated by KEGG pathway clustering analysis. ATF3, JUND, FOSB and JUNB were detected in the PPI network. Finally, the most significant potential target sites of transcription factors and miRNAs in breast cancer, which are important in the regulation of gene expression, were identified. The results indicated that miR-93, miR-302A, miR-302B, miR-302C, miR-302D, miR-372, miR-373, miR-520E and miR-520A were closely associated with the occurrence and development of breast cancer. Therefore, changes in the expression of these miRNAs may alter cell metabolism and trigger the development of breast cancer and its complications.

SUBMITTER: Chen D

PROVIDER: S-EPMC4473354 | biostudies-literature | 2015 Jun

REPOSITORIES: biostudies-literature

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Integrated analysis of differentially expressed genes in breast cancer pathogenesis.

Chen Daobao D Yang Hongjian H

Oncology letters 20150423 6

The present study aimed to detect the differences between breast cancer cells and normal breast cells, and investigate the potential pathogenetic mechanisms of breast cancer. The sample GSE9574 series was downloaded, and the microarray data was analyzed to identify differentially expressed genes (DEGs). Gene Ontology (GO) cluster analysis using the GO Enrichment Analysis Software Toolkit platform and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis for DEGs was conducted using the ...[more]

PMID: 26137106

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Project description:Background:The mortality rate associated with ovarian cancer ranks the highest among gynecological malignancies. However, the cause and underlying molecular events of ovarian cancer are not clear. Here, we applied integrated bioinformatics to identify key pathogenic genes involved in ovarian cancer and reveal potential molecular mechanisms. Results:The expression profiles of GDS3592, GSE54388, and GSE66957 were downloaded from the Gene Expression Omnibus (GEO) database, which contained 115 samples, including 85 cases of ovarian cancer samples and 30 cases of normal ovarian samples. The three microarray datasets were integrated to obtain differentially expressed genes (DEGs) and were deeply analyzed by bioinformatics methods. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments of DEGs were performed by DAVID and KOBAS online analyses, respectively. The protein-protein interaction (PPI) networks of the DEGs were constructed from the STRING database. A total of 190 DEGs were identified in the three GEO datasets, of which 99 genes were upregulated and 91 genes were downregulated. GO analysis showed that the biological functions of DEGs focused primarily on regulating cell proliferation, adhesion, and differentiation and intracellular signal cascades. The main cellular components include cell membranes, exosomes, the cytoskeleton, and the extracellular matrix. The molecular functions include growth factor activity, protein kinase regulation, DNA binding, and oxygen transport activity. KEGG pathway analysis showed that these DEGs were mainly involved in the Wnt signaling pathway, amino acid metabolism, and the tumor signaling pathway. The 17 most closely related genes among DEGs were identified from the PPI network. Conclusion:This study indicates that screening for DEGs and pathways in ovarian cancer using integrated bioinformatics analyses could help us understand the molecular mechanism underlying the development of ovarian cancer, be of clinical significance for the early diagnosis and prevention of ovarian cancer, and provide effective targets for the treatment of ovarian cancer.

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Dataset Information

Integrated analysis of differentially expressed genes in breast cancer pathogenesis.

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Integrated analysis of differentially expressed genes in breast cancer pathogenesis.

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