Dataset Information

Predicting associations between microRNAs and target genes in breast cancer by bioinformatics analyses.

ABSTRACT: Breast cancer is the leading type of cancer among females. However, the association between microRNAs (miRNAs) and target genes in breast tumorigenesis is poorly studied. The original data set GSE26659 was downloaded from the Gene Expression Omnibus, and then the differentially expressed miRNAs among 77 breast cancer patients and 17 controls were identified using the Limma package in R software. Furthermore, breast cancer-related differentially expressed miRNAs were selected from a human miRNA disease database and their target genes were selected from five miRNA databases. Then, functional analysis was performed for the target genes followed by construction of a miRNA-target gene network. A total of 34 differentially expressed miRNAs were identified, including 13 breast cancer-related miRNAs. Moreover, the target genes of the 13 miRNAs were significantly enriched in regulation of transcription (P=7.43E-09) and pathways related to cancer (P=3.33E-11). Finally, eight upregulated miRNAs (including hsa-miR-425) and five downregulated miRNAs (including hsa-miR-143, hsa-miR-145 and hsa-miR-125b) were identified in the miRNA-target gene network. In conclusion, using bioinformatics approaches, we demonstrate that the changes in regulation of transcription and cancer pathways may play significant roles in the process of breast cancerogenesis. Differentially expressed miRNAs and their target genes may be new targets for breast cancer therapy.

SUBMITTER: Zheng T

PROVIDER: S-EPMC4950656 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature

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Predicting associations between microRNAs and target genes in breast cancer by bioinformatics analyses.

Zheng Tianying T Zhang Xing X Wang Yonggang Y Yu Xiucui X

Oncology letters 20160615 2

Breast cancer is the leading type of cancer among females. However, the association between microRNAs (miRNAs) and target genes in breast tumorigenesis is poorly studied. The original data set GSE26659 was downloaded from the Gene Expression Omnibus, and then the differentially expressed miRNAs among 77 breast cancer patients and 17 controls were identified using the Limma package in R software. Furthermore, breast cancer-related differentially expressed miRNAs were selected from a human miRNA d ...[more]

PMID: 27446395

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Project description:Importance:Germline pathogenic variants in BRCA1 and BRCA2 predispose to an increased lifetime risk of breast cancer. However, the relevance of germline variants in other genes from multigene hereditary cancer testing panels is not well defined. Objective:To determine the risks of breast cancer associated with germline variants in cancer predisposition genes. Design, Setting, and Participants:A study population of 65?057 patients with breast cancer receiving germline genetic testing of cancer predisposition genes with hereditary cancer multigene panels. Associations between pathogenic variants in non-BRCA1 and non-BRCA2 predisposition genes and breast cancer risk were estimated in a case-control analysis of patients with breast cancer and Exome Aggregation Consortium reference controls. The women underwent testing between March 15, 2012, and June 30, 2016. Main Outcomes and Measures:Breast cancer risk conferred by pathogenic variants in non-BRCA1 and non-BRCA2 predisposition genes. Results:The mean (SD) age at diagnosis for the 65 057 women included in the analysis was 48.5 (11.1) years. The frequency of pathogenic variants in 21 panel genes identified in 41?611 consecutively tested white women with breast cancer was estimated at 10.2%. After exclusion of BRCA1, BRCA2, and syndromic breast cancer genes (CDH1, PTEN, and TP53), observed pathogenic variants in 5 of 16 genes were associated with high or moderately increased risks of breast cancer: ATM (OR, 2.78; 95% CI, 2.22-3.62), BARD1 (OR, 2.16; 95% CI, 1.31-3.63), CHEK2 (OR, 1.48; 95% CI, 1.31-1.67), PALB2 (OR, 7.46; 95% CI, 5.12-11.19), and RAD51D (OR, 3.07; 95% CI, 1.21-7.88). Conversely, variants in the BRIP1 and RAD51C ovarian cancer risk genes; the MRE11A, RAD50, and NBN MRN complex genes; the MLH1 and PMS2 mismatch repair genes; and NF1 were not associated with increased risks of breast cancer. Conclusions and Relevance:This study establishes several panel genes as high- and moderate-risk breast cancer genes and provides estimates of breast cancer risk associated with pathogenic variants in these genes among individuals qualifying for clinical genetic testing.

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.

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Predicting associations between microRNAs and target genes in breast cancer by bioinformatics analyses.

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Predicting associations between microRNAs and target genes in breast cancer by bioinformatics analyses.

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