Title |
Regulatory network of circRNA–miRNA–mRNA contributes to the histological classification and disease progression in gastric cancer
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Published in |
Journal of Translational Medicine, August 2018
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DOI | 10.1186/s12967-018-1582-8 |
Pubmed ID | |
Authors |
Jia Cheng, Huiqin Zhuo, Mao Xu, Linpei Wang, Hao Xu, Jigui Peng, Jingjing Hou, Lingyun Lin, Jianchun Cai |
Abstract |
Little has been known about the role of non-coding RNA regulatory network in the patterns of growth and invasiveness of gastric cancer (GC) development. MicroRNAs (miRNAs) microarray was used to screen differential miRNA expression profiles in Ming's classification. The significant differential expressions of representative miRNAs and their interacting circular RNA (circRNA) were confirmed in GC cell line and 63 pairs of GC samples. Then, a circRNA/miRNA network was constructed by bioinformatics approaches to identify molecular pathways. Finally, we explored the clinical value of the common targets in the pathway by using receiver operating characteristic curve and survival analysis. Significantly differential expressed miRNAs were found in two pathological types of GC. Both of miR-124 and miR-29b were consistently down-regulated in GC. CircHIPK3 could play a negative regulatory role on miR-124/miR-29b expression and associated with T stage and Ming's classification in GC. The bioinformatics analyses showed that targets expression of circHIPK3-miR-124/miR-29b axes in cancer-related pathways was able to predict the status of GC and associated with individual survival time. The targets of circHIPK3-miR-124/miR-29b axes involved in the progression of GC. CircHIPK3 could take part in the proliferation process of GC cell and may be potential biomarker in histological classification of GC. |
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Unknown | 1 | 100% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 24 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Master | 5 | 21% |
Student > Bachelor | 4 | 17% |
Researcher | 3 | 13% |
Student > Ph. D. Student | 2 | 8% |
Lecturer | 1 | 4% |
Other | 1 | 4% |
Unknown | 8 | 33% |
Readers by discipline | Count | As % |
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Biochemistry, Genetics and Molecular Biology | 5 | 21% |
Pharmacology, Toxicology and Pharmaceutical Science | 2 | 8% |
Agricultural and Biological Sciences | 2 | 8% |
Medicine and Dentistry | 2 | 8% |
Engineering | 2 | 8% |
Other | 3 | 13% |
Unknown | 8 | 33% |