Literature DB >> 26489476

Systematic Identification of MicroRNAs That Impact on Proliferation of Prostate Cancer Cells and Display Changed Expression in Tumor Tissue.

Anna Aakula1, Pekka Kohonen2, Suvi-Katri Leivonen3, Rami Mäkelä4, Petteri Hintsanen5, John Patrick Mpindi6, Elena Martens-Uzunova7, Tero Aittokallio5, Guido Jenster7, Merja Perälä8, Olli Kallioniemi6, Päivi Östling6.   

Abstract

BACKGROUND: Systematic approaches to functionally identify key players in microRNA (miRNA)-target networks regulating prostate cancer (PCa) proliferation are still missing.
OBJECTIVE: To comprehensively map miRNA regulation of genes relevant for PCa proliferation through phenotypic screening and tumor expression data. DESIGN, SETTING, AND PARTICIPANTS: Gain-of-function screening with 1129 miRNA molecules was performed in five PCa cell lines, measuring proliferation, viability, and apoptosis. These results were integrated with changes in miRNA expression from two cohorts of human PCa (188 tumors in total). For resulting miRNAs, the predicted targets were collected and analyzed for patterns with gene set enrichment analysis, and for their association with biochemical recurrence free survival. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Rank product statistical analysis was used to evaluate miRNA effects in phenotypic screening and for expression differences in the prostate tumor cohorts. Expression data were analyzed using the significance analysis of microarrays (SAM) method and the patient material was subjected to Kaplan-Meier statistics. RESULTS AND LIMITATIONS: Functional screening identified 25 miRNAs increasing and 48 miRNAs decreasing cell viability. Data integration resulted in 14 miRNAs, with aberrant expression and effect on proliferation. These miRNAs are predicted to regulate >3700 genes, of which 28 were found up-regulated and 127 down-regulated in PCa compared with benign tissue. Seven genes, FLNC, MSRB3, PARVA, PCDH7, PRNP, RAB34, and SORBS1, showed an inverse association to their predicted miRNA, and were identified to significantly correlate with biochemical recurrence free survival in PCa patients.
CONCLUSIONS: A systematic in vitro screening approach combined with in vivo expression and gene set enrichment analysis provide unbiased means for revealing novel miRNA-target links, possibly driving the oncogenic processes in PCa. PATIENT
SUMMARY: This study identified novel regulatory molecules, which impact on PCa proliferation and are aberrantly expressed in clinical tumors. Thus, our study reveals regulatory nodes with potential for therapy.
Copyright © 2015 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Functional high-throughput screening; MicroRNA; Prostate cancer; Reverse-phase protein array

Mesh:

Substances:

Year:  2015        PMID: 26489476     DOI: 10.1016/j.eururo.2015.09.019

Source DB:  PubMed          Journal:  Eur Urol        ISSN: 0302-2838            Impact factor:   20.096


  22 in total

1.  Targeting Brain-Adaptive Cancer Stem Cells Prohibits Brain Metastatic Colonization of Triple-Negative Breast Cancer.

Authors:  Ding Ren; Xiaoping Zhu; Ren Kong; Zhen Zhao; Jianting Sheng; Jiang Wang; Xiaoyun Xu; Jiyong Liu; Kemi Cui; Xiang H-F Zhang; Hong Zhao; Stephen T C Wong
Journal:  Cancer Res       Date:  2018-03-22       Impact factor: 12.701

2.  MicroRNA-211 expression is down-regulated and associated with poor prognosis in human glioma.

Authors:  Jun Zhang; Jianguang Lv; Feng Zhang; Hongmin Che; Qiwei Liao; Wobin Huang; Shaopeng Li; Yuqian Li
Journal:  J Neurooncol       Date:  2017-05-27       Impact factor: 4.130

3.  Development and Validation of a Prognostic Classifier Based on Lipid Metabolism-Related Genes for Breast Cancer.

Authors:  Nan Wang; Yuanting Gu; Lin Li; Jiangrui Chi; Xinwei Liu; Youyi Xiong; Chaochao Zhong
Journal:  J Inflamm Res       Date:  2022-06-14

4.  MiR-424 and miR-27a increase TRAIL sensitivity of acute myeloid leukemia by targeting PLAG1.

Authors:  Yan-Ping Sun; Fei Lu; Xiao-Yu Han; Min Ji; Ying Zhou; A-Min Zhang; Hong-Chun Wang; Dao-Xin Ma; Chun-Yan Ji
Journal:  Oncotarget       Date:  2016-05-03

5.  MiR-4638-5p inhibits castration resistance of prostate cancer through repressing Kidins220 expression and PI3K/AKT pathway activity.

Authors:  Yang Wang; Ning Shao; Xueying Mao; Minmin Zhu; Weifei Fan; Zhixiang Shen; Rong Xiao; Chuncai Wang; Wenping Bao; Xinyu Xu; Chun Yang; Jian Dong; Deshui Yu; Yan Wu; Caixia Zhu; Liting Wen; Xiaojie Lu; Yong-Jie Lu; Ninghan Feng
Journal:  Oncotarget       Date:  2016-07-26

6.  Evaluation of a short RNA within Prostate Cancer Gene 3 in the predictive role for future cancer using non-malignant prostate biopsies.

Authors:  Karl H Pang; Derek J Rosario; Susan L Morgan; James W F Catto
Journal:  PLoS One       Date:  2017-04-05       Impact factor: 3.240

Review 7.  MicroRNAs as Guardians of the Prostate: Those Who Stand before Cancer. What Do We Really Know about the Role of microRNAs in Prostate Biology?

Authors:  Thomas Andl; Kavya Ganapathy; Alexia Bossan; Ratna Chakrabarti
Journal:  Int J Mol Sci       Date:  2020-07-07       Impact factor: 5.923

8.  Identification of miR-30b-3p and miR-30d-5p as direct regulators of androgen receptor signaling in prostate cancer by complementary functional microRNA library screening.

Authors:  Binod Kumar; Salar Khaleghzadegan; Brian Mears; Koji Hatano; Tarana A Kudrolli; Wasim H Chowdhury; David B Yeater; Charles M Ewing; Jun Luo; William B Isaacs; Luigi Marchionni; Shawn E Lupold
Journal:  Oncotarget       Date:  2016-11-08

9.  An integrated view of the role of miR-130b/301b miRNA cluster in prostate cancer.

Authors:  Rafael Sebastián Fort; Cecilia Mathó; Carolina Oliveira-Rizzo; Beatriz Garat; José Roberto Sotelo-Silveira; María Ana Duhagon
Journal:  Exp Hematol Oncol       Date:  2018-05-02

10.  MicroRNA-652 suppresses malignant phenotypes in glioblastoma multiforme via FOXK1-mediated AKT/mTOR signaling pathway.

Authors:  Huimei Yang; Zhenzhen Song; Xia Wu; Yilei Wu; Chengxia Liu
Journal:  Onco Targets Ther       Date:  2019-07-10       Impact factor: 4.147

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