Literature DB >> 27460542

Low expression lncRNA RPLP0P2 is associated with poor prognosis and decreased cell proliferation and adhesion ability in lung adenocarcinoma.

Jie Chen1, Lijuan Hu2, Jian Chen2, Fang Wu3, Dongwei Hu2, Gang Xu2, Peiwu Zhu2, Yumin Wang2.   

Abstract

We investigated the clinical roles and biological function of long non-coding (lncRNA) RPLP0P2 in lung adenocarcinoma (LAD). The expression level of RPLP0P2 was estimated by quantitative reverse transcription-polymerase chain reaction (qPCR) in 57 pairs of LAD and NT samples and the relation of RPLP0P2 to clinical data of LAD patients was analyzed. We overexpressed RPLP0P2 based on the human LAD cell line A549 by lentivirus‑mediated technology, then oncological behavior change was observed of A549 cells and the change of mRNA level of LRRC10B and RPLP0P2 by qPCR. We found that RPLP0P2 expression was lower while LRRC10B mRNA level was higher in LAD than NT by qPCR. RPLP0P2 expression level was negative correlated to LRRC10B mRNA level (Pearson correlation =‑0.754, P=0.0021). The expression of RPLP0P2 in lymph node metastasis of LAD group was significantly lower than LAD without lymph node metastasis group. Survival analysis showed that survival time of high expression of RPLP0P2 was significantly longer than low RPLP0P2 level in LAD patients. After RPLP0P2 was overexpressed, the proliferation rate, adhesion ability, S phase and G2/M phase cells and LRRC10B mRNA significantly reduced, while apoptosis and G0/G1 phase cells obviously increased, but migration ability and invasion did not significantly change. Our study ascertained that low expression of RPLP0P2 in LAD is associated with poor prognosis and decreased proliferation and adhesion ability of tumor cells. LRRC10B may be a downstream gene regulated by RPLP0P2.

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Year:  2016        PMID: 27460542     DOI: 10.3892/or.2016.4965

Source DB:  PubMed          Journal:  Oncol Rep        ISSN: 1021-335X            Impact factor:   3.906


  8 in total

1.  LncRNA MALAT1/miR-181a-5p affects the proliferation and adhesion of myeloma cells via regulation of Hippo-YAP signaling pathway.

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2.  Genome-Wide Analysis of the FOXA1 Transcriptional Network Identifies Novel Protein-Coding and Long Noncoding RNA Targets in Colorectal Cancer Cells.

Authors:  Sarah B Lazar; Lorinc Pongor; Xiao Ling Li; Ioannis Grammatikakis; Bruna R Muys; Emily A Dangelmaier; Christophe E Redon; Sang-Min Jang; Robert L Walker; Wei Tang; Stefan Ambs; Curtis C Harris; Paul S Meltzer; Mirit I Aladjem; Ashish Lal
Journal:  Mol Cell Biol       Date:  2020-10-13       Impact factor: 4.272

3.  Long noncoding RNA TUG1 is a diagnostic factor in lung adenocarcinoma and suppresses apoptosis via epigenetic silencing of BAX.

Authors:  Huan Liu; Guizhi Zhou; Xin Fu; Haiyan Cui; Guangrui Pu; Yao Xiao; Wei Sun; Xinhua Dong; Libin Zhang; Sijia Cao; Guiqin Li; Xiaowei Wu; Xu Yang
Journal:  Oncotarget       Date:  2017-10-19

4.  Identification of potential cancer-related pseudogenes in lung adenocarcinoma based on ceRNA hypothesis.

Authors:  Yunzhen Wei; Zhiqiang Chang; Cheng Wu; Yinling Zhu; Kun Li; Yan Xu
Journal:  Oncotarget       Date:  2017-08-04

5.  lncRNA FEZF1-AS1 Is Associated With Prognosis in Lung Adenocarcinoma and Promotes Cell Proliferation, Migration, and Invasion.

Authors:  Zhenjun Liu; Pei Zhao; Yuping Han; Song Lu
Journal:  Oncol Res       Date:  2018-03-06       Impact factor: 5.574

6.  Downregulation of lncRNA RPLP0P2 inhibits cell proliferation, invasion and migration, and promotes apoptosis in colorectal cancer.

Authors:  Hang Yuan; Shiliang Tu; Yingyu Ma; Yueming Sun
Journal:  Mol Med Rep       Date:  2021-03-02       Impact factor: 2.952

7.  Linc00426 accelerates lung adenocarcinoma progression by regulating miR-455-5p as a molecular sponge.

Authors:  Hongli Li; Qingjie Mu; Guoxin Zhang; Zhixin Shen; Yuanyuan Zhang; Jun Bai; Liping Zhang; Dandan Zhou; Quan Zheng; Lihong Shi; Wenxia Su; Chonggao Yin; Baogang Zhang
Journal:  Cell Death Dis       Date:  2020-12-11       Impact factor: 8.469

Review 8.  Predicting Pseudogene-miRNA Associations Based on Feature Fusion and Graph Auto-Encoder.

Authors:  Shijia Zhou; Weicheng Sun; Ping Zhang; Li Li
Journal:  Front Genet       Date:  2021-12-13       Impact factor: 4.599

  8 in total

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