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[Expression and bioinformatics analysis of long-chain non-coding RNA PVT1 in tumors].
Wang, M; Gao, W; Bai, Y F; Lu, D H; Teng, L H.
Affiliation
  • Wang M; Department of Pathology, Capital Medical University, Beijing 100069, China.
Zhonghua Bing Li Xue Za Zhi ; 46(7): 485-490, 2017 Jul 08.
Article in Zh | MEDLINE | ID: mdl-28728223
ABSTRACT

Objective:

To study the expression and mechanism of long-chain non-coding RNA PVT1 in tumor by bioinformatics analysis and experimental verification, and to provide new ideas for the study of the pathogenesis of tumors.

Methods:

The expression of PVT1 in 14 common tumors was downloaded from starBase v2.0 public database, which also was verified by PVT1 RNA-in situ hybridization.The upstream transcription factors, the downstream target microRNA(miRNA) for PVT1 and the target genes for the target miRNAs were predicted and analyzed by using bioinformatics based on the database of UCSC Genome Browser, HMDD v2.0, miRTar Base, JASPAR databases.

Results:

StarBase database analysis and RNA in situ hybridization showed that PVT1 was highly expressed in kidney clear cell carcinoma and colon and rectal adenocarcinoma. PVT1 was regulated by the upstream transcription factors CREB1, Atf1, SP1, KLF5, STAT3, while it could control the expression of the downstream target miR-16. bcl-2, VEGFA, CCNE1, CCND1 and SHOC2 showed an interaction with the transcription factor of PVT1, which formed a feedback regulatory pathway.

Conclusions:

PVT1 is highly expressed in kidney clear cell carcinoma and colon and rectal adenocarcinoma.The predictive analysis of bioinformatics demonstrates that transcription factor/PVT1/miR-16/target gene signal axon may be an important molecular mechanism, which provide a valuable clue for further functional mechanism research of long-chain non-coding RNA.
Subject(s)
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Computational Biology / RNA, Long Noncoding / Neoplasms Type of study: Prognostic_studies Limits: Humans Language: Zh Journal: Zhonghua Bing Li Xue Za Zhi Year: 2017 Document type: Article Affiliation country: China

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Computational Biology / RNA, Long Noncoding / Neoplasms Type of study: Prognostic_studies Limits: Humans Language: Zh Journal: Zhonghua Bing Li Xue Za Zhi Year: 2017 Document type: Article Affiliation country: China