Identification of an lncRNA-miRNA-mRNA interaction mechanism in breast cancer based on bioinformatic analysis
| Autor: | Yongfeng Li, Xuli Meng, Xiping Zhang, Qing Wang, Xianfeng Ding, Hong Chao Tang, Yuhan Zhang, Dajin Wang |
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| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Cancer Research mRNA Gene regulatory network Estrogen receptor Breast Neoplasms Computational biology Biology Biochemistry Transcriptome 03 medical and health sciences breast cancer 0302 clinical medicine RNA interference Databases Genetic microRNA Genetics Humans Gene Regulatory Networks RNA Messenger Molecular Biology Regulation of gene expression long non-coding RNA Gene Expression Profiling Computational Biology Genetic Variation Articles bioinformatics PVT1 Gene Expression Regulation Neoplastic Gene expression profiling MicroRNAs 030104 developmental biology Oncology 030220 oncology & carcinogenesis Nucleic Acid Conformation Molecular Medicine Female RNA Interference RNA Long Noncoding |
| Zdroj: | Molecular Medicine Reports |
| ISSN: | 1791-3004 1791-2997 |
| DOI: | 10.3892/mmr.2017.7304 |
| Popis: | Non-coding RNAs serve important roles in regulating the expression of certain genes and are involved in the principal biological processes of breast cancer. The majority of studies have focused on defining the regulatory functions of long non-coding RNAs (lncRNAs) and microRNAs (miRNAs/miRs), and few studies have investigated how lncRNAs and miRNAs are transcriptionally regulated. In the present study, based on the breast invasive carcinoma dataset from The Cancer Genome Atlas at cBioPortal, and using a bioinformatics computational approach, an lncRNA-miRNA-mRNA network was constructed. The network consisted of 601 nodes and 706 edges, which represented the complex web of regulatory effects between lncRNAs, miRNAs and target genes. The results of the present study demonstrated that miR-510 was the most potent miRNA controller and regulator of numerous target genes. In addition, it was observed that the lncRNAs PVT1, CCAT1 and linc00861 exhibited possible interactions with clinical biomarkers, including receptor tyrosine-protein kinase erbB-2, estrogen receptor and progesterone receptor, demonstrated using RNA-protein interaction prediction software. The network of lncRNA-miRNA-mRNA interactions will facilitate further experimental studies and may be used to refine biomarker predictions for developing novel therapeutic approaches in breast cancer. |
| Databáze: | OpenAIRE |
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