Integrated analysis reveals microRNA networks coordinately expressed with key proteins in breast cancer
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE58215 | GEO | 2015/01/31
SECONDARY ACCESSION(S): PRJNA253578
REPOSITORIES: GEO
Similar Datasets
Project description:BackgroundThe role played by microRNAs in the deregulation of protein expression in breast cancer is only partly understood. To gain insight, the combined effect of microRNA and mRNA expression on protein expression was investigated in three independent data sets.MethodsProtein expression was modeled as a multilinear function of powers of mRNA and microRNA expression. The model was first applied to mRNA and protein expression for 105 selected cancer-associated genes and to genome-wide microRNA expression from 283 breast tumors. The model considered both the effect of one microRNA at a time and all microRNAs combined. In the latter case the Lasso penalized regression method was applied to detect the simultaneous effect of multiple microRNAs.ResultsAn interactome map for breast cancer representing all direct and indirect associations between the expression of microRNAs and proteins was derived. A pattern of extensive coordination between microRNA and protein expression in breast cancer emerges, with multiple clusters of microRNAs being associated with multiple clusters of proteins. Results were subsequently validated in two independent breast cancer data sets. A number of the microRNA-protein associations were functionally validated in a breast cancer cell line.ConclusionsA comprehensive map is derived for the co-expression in breast cancer of microRNAs and 105 proteins with known roles in cancer, after filtering out the in-cis effect of mRNA expression. The analysis suggests that group action by several microRNAs to deregulate the expression of proteins is a common modus operandi in breast cancer.
| S-EPMC4396592 | biostudies-literature
Project description:This SuperSeries is composed of the SubSeries listed below. Refer to individual Series
2015-01-31 | E-GEOD-58215 | biostudies-arrayexpress
Project description:BACKGROUND: The role played by microRNAs in the deregulation of protein expression in breast cancer is only partly understood. To gain insight, the combined effect of microRNA and mRNA expression on protein expression was investigated in three independent data sets. METHODS: Protein expression was modeled as a multilinear function of powers of mRNA and microRNA expression. The model was first applied to mRNA and protein expression for 105 selected cancer-associated genes and to genome-wide microRNA expression from 283 breast tumors. The model considered both the effect of one microRNA at a time and all microRNAs combined. In the latter case the Lasso penalized regression method was applied to detect the simultaneous effect of multiple microRNAs. RESULTS: An interactome map for breast cancer representing all direct and indirect associations between the expression of microRNAs and proteins was derived. A pattern of extensive coordination between microRNA and protein expression in breast cancer emerges, with multiple clusters of microRNAs being associated with multiple clusters of proteins. Results were subsequently validated in two independent breast cancer data sets. A number of the microRNA-protein associations were functionally validated in a breast cancer cell line. CONCLUSIONS: A comprehensive map is derived for the co-expression in breast cancer of microRNAs and 105 proteins with known roles in cancer, after filtering out the in-cis effect of mRNA expression. The analysis suggests that group action by several microRNAs to deregulate the expression of proteins is a common modus operandi in breast cancer.
2015-02-02 | GSE58212 | GEO
Project description:BACKGROUND: The role played by microRNAs in the deregulation of protein expression in breast cancer is only partly understood. To gain insight, the combined effect of microRNA and mRNA expression on protein expression was investigated in three independent data sets. METHODS: Protein expression was modeled as a multilinear function of powers of mRNA and microRNA expression. The model was first applied to mRNA and protein expression for 105 selected cancer-associated genes and to genome-wide microRNA expression from 283 breast tumors. The model considered both the effect of one microRNA at a time and all microRNAs combined. In the latter case the Lasso penalized regression method was applied to detect the simultaneous effect of multiple microRNAs. RESULTS: An interactome map for breast cancer representing all direct and indirect associations between the expression of microRNAs and proteins was derived. A pattern of extensive coordination between microRNA and protein expression in breast cancer emerges, with multiple clusters of microRNAs being associated with multiple clusters of proteins. Results were subsequently validated in two independent breast cancer data sets. A number of the microRNA-protein associations were functionally validated in a breast cancer cell line. CONCLUSIONS: A comprehensive map is derived for the co-expression in breast cancer of microRNAs and 105 proteins with known roles in cancer, after filtering out the in-cis effect of mRNA expression. The analysis suggests that group action by several microRNAs to deregulate the expression of proteins is a common modus operandi in breast cancer.
2015-02-02 | GSE58210 | GEO
Project description:Integrated analysis reveals microRNA networks coordinately expressed with key proteins in breast cancer
| PRJNA253578 | ENA
Project description:Breast cancer (BC) is the leading malignancy in women worldwide, yet relatively little is known about the genes and signaling pathways involved in BC tumorigenesis and progression. The present study aimed to elucidate potential key candidate genes and pathways in BC. Five gene expression profile data sets (GSE22035, GSE3744, GSE5764, GSE21422 and GSE26910) were downloaded from the Gene Expression Omnibus (GEO) database, which included data from 113 tumorous and 38 adjacent non?tumorous tissue samples. Differentially expressed genes (DEGs) were identified using t?tests in the limma R package. These DEGs were subsequently investigated by pathway enrichment analysis and a protein?protein interaction (PPI) network was constructed. The most significant module from the PPI network was selected for pathway enrichment analysis. In total, 227 DEGs were identified, of which 82 were upregulated and 145 were downregulated. Pathway enrichment analysis results revealed that the upregulated DEGs were mainly enriched in 'cell division', the 'proteinaceous extracellular matrix (ECM)', 'ECM structural constituents' and 'ECM?receptor interaction', whereas downregulated genes were mainly enriched in 'response to drugs', 'extracellular space', 'transcriptional activator activity' and the 'peroxisome proliferator?activated receptor signaling pathway'. The PPI network contained 174 nodes and 1,257 edges. DNA topoisomerase 2?a, baculoviral inhibitor of apoptosis repeat?containing protein 5, cyclin?dependent kinase 1, G2/mitotic?specific cyclin?B1 and kinetochore protein NDC80 homolog were identified as the top 5 hub genes. Furthermore, the genes in the most significant module were predominantly involved in 'mitotic nuclear division', 'mid?body', 'protein binding' and 'cell cycle'. In conclusion, the DEGs, relative pathways and hub genes identified in the present study may aid in understanding of the molecular mechanisms underlying BC progression and provide potential molecular targets and biomarkers for BC.
| S-EPMC5983982 | biostudies-literature
Project description:BackgroundTreatment strategies for various subtypes of breast cancer (BC) are different based on their distinct molecular characteristics. Therefore, it is very important to identify key differentially expressed genes (DEGs) between ER-positive/HER2-negative BC and ER-negative/HER2-negative BC.MethodsGene expression profiles of GSE22093 and GSE23988 were obtained from the Gene Expression Omnibus database. There were 74 ER-positive/HER2-negative BC tissues and 85 ER-negative/HER2-negative BC tissues in the two profile datasets. DEGs between ER-positive/HER2-negative tissues and ER-negative/HER2-negative BC tissues were identified by the GEO2R tool. The common DEGs among the two datasets were detected with Venn software online. Next, we made use of the Database for Annotation, Visualization and Integrated Discovery to analyze enriched Kyoto Encyclopedia of Gene and Genome (KEGG) pathways and gene ontology terms. Then, the protein-protein interactions (PPIs) of these DEGs were visualized by Cytoscape with the Search Tool for the Retrieval of Interacting Genes. Of the proteins in the PPI network, Molecular Complex Detection plug-in analysis identified nine core upregulated genes and one core downregulated gene. UALCAN and Gene Expression Profiling Interactive Analysis were applied to determine the expression of these 10 genes in BC. Furthermore, for the analysis of overall survival among those genes, the Kaplan-Meier method was implemented.ResultsNinety-three common DEGs (63 upregulated and 30 downregulated) were identified. KEGG pathway enrichment analysis showed that upregulated DEGs were particularly enriched in the progesterone-mediated oocyte maturation pathway. In addition, PGR might be a prognostic biomarker for ER-positive/HER2-negative BC. CCND1 is a poor prognostic biomarker for ER-positive/HER2-negative BC and ER-negative/HER2-negative BC. Moreover, TFF1, AGR2 and EGFR might be predictive biomarkers of node metastasis in ER-positive/HER2-negative BC and ER-negative/HER2-negative BC.ConclusionsCCND1, AGR2, PGR, TFF1 and EGFR are the key DEGs between ER-positive/HER2-negative BC and ER-negative/HER2-negative BC. Further studies are required to confirm the functions of the identified genes.
| S-EPMC7347802 | biostudies-literature
Project description: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.
| S-EPMC4473354 | biostudies-literature
Project description:BackgroundTreatment of patients with Sjögren's syndrome (SjS) is a clinical challenge with high unmet needs. Gene expression profiling and integrative network-based approaches to complex disease can offer an insight on molecular characteristics in the context of clinical setting.MethodsAn integrated dataset was created from salivary gland samples of 30 SjS patients. Pathway-driven enrichment profiles made by gene set enrichment analysis were categorized using hierarchical clustering. Differentially expressed genes (DEGs) were subjected to functional network analysis, where the elements of the core subnetwork were used for key driver analysis.ResultsWe identified 310 upregulated DEGs, including nine known genetic risk factors and two potential biomarkers. The core subnetwork was enriched with the processes associated with B cell hyperactivity. Pathway-based subgrouping revealed two clusters with distinct molecular signatures for the relevant pathways and cell subsets. Cluster 2, with low-grade inflammation, showed a better response to rituximab therapy than cluster 1, with high-grade inflammation. Fourteen key driver genes appeared to be essential signaling mediators downstream of the B cell receptor (BCR) signaling pathway and to have a positive relationship with histopathology scores.ConclusionIntegrative network-based approaches provide deep insights into the modules and pathways causally related to SjS and allow identification of key targets for disease. Intervention adjusted to the molecular traits of the disease would allow the achievement of better outcomes, and the BCR signaling pathway and its leading players are promising therapeutic targets.
| S-EPMC6921432 | biostudies-literature
Project description:BACKGROUND: Cancers are commonly characterised by hypoxia and also by global reductions in the levels of mature microRNAs. We have examined the hypothesis that hypoxia might mediate this reduction through repressive effects on microRNA biogenesis proteins. METHODS: Breast cancer cell lines were exposed to hypoxia and manipulations of hypoxia inducible factor (HIF) and HIF hydroxylase activity. The effects of hypoxia on the mRNA and protein levels of enzymes involved in microRNA biogenesis (Dicer, Drosha, TARPB2, DCGR8, XPO5) was determined by RT PCR and immunoblotting. The effect of hypoxia on microRNAs was determined with microarray studies, RT PCR and reporter assays. RESULTS: In breast cancer lines there was significant reduction of Dicer mRNA and protein levels in cells exposed to hypoxia. This effect was independent of HIF but dependent on the HIF hydroxylase PHD2 and was partly mediated by feedback effects via microRNAs. Furthermore, several other proteins with critical roles in microRNA biogenesis (Drosha, TARBP2 and DCGR8) also showed significant and co-ordinated repression under hypoxic conditions. Despite these substantial alterations no, or modest, changes were observed in mature microRNA production. CONCLUSION: These observations provide further and important interfaces between oxygen availability and gene expression and a potential mechanistic explanation for the reduced levels of microRNAs observed in some cancers. They provide further support for the existence of feedback mechanisms in the regulation of the microRNA biogenesis pathway and the relative stability of microRNAs.
| S-EPMC4223767 | biostudies-literature