OBJECTIVE: To identify the signatures of miRNAs differentially expressed in HER2(+) versus HER2(-) breast cancers that accurately predict the HER2 status of breast cancer, and to provide further insight into breast cancer therapy. METHODS: By the methods of literature search, aberrant expressed miRNAs were collected. By target prediction algorithm of TargetScan and PicTar and the data enrichment analysis, target gene sets of miRNAs differentially expressed in HER2(+) versus HER2(-) breast cancers were built. Then, using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) database, the function modules of Gene Ontology categories and Kyoto Encyclopedia of Genes and Genomes (KEGG) and BIOCARTA pathway, biological functions and signaling pathways that are probably regulated by miRNAs, were analyzed. RESULTS: We got five sets of miRNAs expressed in different HER2 status of breast cancers finally. The five sets of data contain 22; 32; 3; 38; and 62 miRNAs, respectively. After miRNAs target prediction and data enrichment, 5,734; 22,409; 1,142; 22,293; and 43,460 target genes of five miRNA sets were collected. Gene ontology analysis found these genes may be involved in transcription, protein transport, angiogenesis, and apoptosis. Moreover, certain KEGG and BIOCARTA signaling pathways related toHER2 status were found. CONCLUSION: Using TargetScan and PicTar for data enrichment, and DAVID database, Gene Ontology categories, KEGG and BIOCARTA pathway for analysis of miRNAs different expression, we conducted a new method for biological interpretation of miRNA profiling data in HER2(+) versus HER2(-) breast cancers. It may improve understanding the regulatory roles of miRNAs in different molecular subtypes of breast cancers. Therefore, it is beneficial to improve the accuracy of experimental efforts to breast cancer and potential therapeutic targets.
OBJECTIVE: To identify the signatures of miRNAs differentially expressed in HER2(+) versus HER2(-) breast cancers that accurately predict the HER2 status of breast cancer, and to provide further insight into breast cancer therapy. METHODS: By the methods of literature search, aberrant expressed miRNAs were collected. By target prediction algorithm of TargetScan and PicTar and the data enrichment analysis, target gene sets of miRNAs differentially expressed in HER2(+) versus HER2(-) breast cancers were built. Then, using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) database, the function modules of Gene Ontology categories and Kyoto Encyclopedia of Genes and Genomes (KEGG) and BIOCARTA pathway, biological functions and signaling pathways that are probably regulated by miRNAs, were analyzed. RESULTS: We got five sets of miRNAs expressed in different HER2 status of breast cancers finally. The five sets of data contain 22; 32; 3; 38; and 62 miRNAs, respectively. After miRNAs target prediction and data enrichment, 5,734; 22,409; 1,142; 22,293; and 43,460 target genes of five miRNA sets were collected. Gene ontology analysis found these genes may be involved in transcription, protein transport, angiogenesis, and apoptosis. Moreover, certain KEGG and BIOCARTA signaling pathways related toHER2 status were found. CONCLUSION: Using TargetScan and PicTar for data enrichment, and DAVID database, Gene Ontology categories, KEGG and BIOCARTA pathway for analysis of miRNAs different expression, we conducted a new method for biological interpretation of miRNA profiling data in HER2(+) versus HER2(-) breast cancers. It may improve understanding the regulatory roles of miRNAs in different molecular subtypes of breast cancers. Therefore, it is beneficial to improve the accuracy of experimental efforts to breast cancer and potential therapeutic targets.
Authors: A M Chinnaiyan; U Prasad; S Shankar; D A Hamstra; M Shanaiah; T L Chenevert; B D Ross; A Rehemtulla Journal: Proc Natl Acad Sci U S A Date: 2000-02-15 Impact factor: 11.205
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