Cargando…

Clear cell renal cell carcinoma associated microRNA expression signatures identified by an integrated bioinformatics analysis

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) represents the most invasive and common adult kidney neoplasm. Mounting evidence suggests that microRNAs (miRNAs) are important regulators of gene expression. But their function in tumourigenesis in this tumour type remains elusive. With the develo...

Descripción completa

Detalles Bibliográficos
Autores principales: Chen, Jiajia, Zhang, Daqing, Zhang, Wenyu, Tang, Yifei, Yan, Wenying, Guo, Lingchuan, Shen, Bairong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3740788/
https://www.ncbi.nlm.nih.gov/pubmed/23841900
http://dx.doi.org/10.1186/1479-5876-11-169
_version_ 1782280177996267520
author Chen, Jiajia
Zhang, Daqing
Zhang, Wenyu
Tang, Yifei
Yan, Wenying
Guo, Lingchuan
Shen, Bairong
author_facet Chen, Jiajia
Zhang, Daqing
Zhang, Wenyu
Tang, Yifei
Yan, Wenying
Guo, Lingchuan
Shen, Bairong
author_sort Chen, Jiajia
collection PubMed
description BACKGROUND: Clear cell renal cell carcinoma (ccRCC) represents the most invasive and common adult kidney neoplasm. Mounting evidence suggests that microRNAs (miRNAs) are important regulators of gene expression. But their function in tumourigenesis in this tumour type remains elusive. With the development of high throughput technologies such as microarrays and NGS, aberrant miRNA expression has been widely observed in ccRCC. Systematic and integrative analysis of multiple microRNA expression datasets may reveal potential mechanisms by which microRNAs contribute to ccRCC pathogenesis. METHODS: We collected 5 public microRNA expression datasets in ccRCC versus non-matching normal renal tissues from GEO database and published literatures. We analyzed these data sets with an integrated bioinformatics framework to identify expression signatures. The framework incorporates a novel statistic method for abnormal gene expression detection and an in-house developed predictor to assess the regulatory activity of microRNAs. We then mapped target genes of DE-miRNAs to different databases, such as GO, KEGG, GeneGo etc, for functional enrichment analysis. RESULTS: Using this framework we identified a consistent panel of eleven deregulated miRNAs shared by five independent datasets that can distinguish normal kidney tissues from ccRCC. After comparison with 3 RNA-seq based microRNA profiling studies, we found that our data correlated well with the results of next generation sequencing. We also discovered 14 novel molecular pathways that are likely to play a role in the tumourigenesis of ccRCC. CONCLUSIONS: The integrative framework described in this paper greatly improves the inter-dataset consistency of microRNA expression signatures. Consensus expression profile should be identified at pathway or network level to address the heterogeneity of cancer. The DE-miRNA signature and novel pathways identified herein could provide potential biomarkers for ccRCC that await further validation.
format Online
Article
Text
id pubmed-3740788
institution National Center for Biotechnology Information
language English
publishDate 2013
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-37407882013-08-13 Clear cell renal cell carcinoma associated microRNA expression signatures identified by an integrated bioinformatics analysis Chen, Jiajia Zhang, Daqing Zhang, Wenyu Tang, Yifei Yan, Wenying Guo, Lingchuan Shen, Bairong J Transl Med Research BACKGROUND: Clear cell renal cell carcinoma (ccRCC) represents the most invasive and common adult kidney neoplasm. Mounting evidence suggests that microRNAs (miRNAs) are important regulators of gene expression. But their function in tumourigenesis in this tumour type remains elusive. With the development of high throughput technologies such as microarrays and NGS, aberrant miRNA expression has been widely observed in ccRCC. Systematic and integrative analysis of multiple microRNA expression datasets may reveal potential mechanisms by which microRNAs contribute to ccRCC pathogenesis. METHODS: We collected 5 public microRNA expression datasets in ccRCC versus non-matching normal renal tissues from GEO database and published literatures. We analyzed these data sets with an integrated bioinformatics framework to identify expression signatures. The framework incorporates a novel statistic method for abnormal gene expression detection and an in-house developed predictor to assess the regulatory activity of microRNAs. We then mapped target genes of DE-miRNAs to different databases, such as GO, KEGG, GeneGo etc, for functional enrichment analysis. RESULTS: Using this framework we identified a consistent panel of eleven deregulated miRNAs shared by five independent datasets that can distinguish normal kidney tissues from ccRCC. After comparison with 3 RNA-seq based microRNA profiling studies, we found that our data correlated well with the results of next generation sequencing. We also discovered 14 novel molecular pathways that are likely to play a role in the tumourigenesis of ccRCC. CONCLUSIONS: The integrative framework described in this paper greatly improves the inter-dataset consistency of microRNA expression signatures. Consensus expression profile should be identified at pathway or network level to address the heterogeneity of cancer. The DE-miRNA signature and novel pathways identified herein could provide potential biomarkers for ccRCC that await further validation. BioMed Central 2013-07-10 /pmc/articles/PMC3740788/ /pubmed/23841900 http://dx.doi.org/10.1186/1479-5876-11-169 Text en Copyright © 2013 Chen et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research
Chen, Jiajia
Zhang, Daqing
Zhang, Wenyu
Tang, Yifei
Yan, Wenying
Guo, Lingchuan
Shen, Bairong
Clear cell renal cell carcinoma associated microRNA expression signatures identified by an integrated bioinformatics analysis
title Clear cell renal cell carcinoma associated microRNA expression signatures identified by an integrated bioinformatics analysis
title_full Clear cell renal cell carcinoma associated microRNA expression signatures identified by an integrated bioinformatics analysis
title_fullStr Clear cell renal cell carcinoma associated microRNA expression signatures identified by an integrated bioinformatics analysis
title_full_unstemmed Clear cell renal cell carcinoma associated microRNA expression signatures identified by an integrated bioinformatics analysis
title_short Clear cell renal cell carcinoma associated microRNA expression signatures identified by an integrated bioinformatics analysis
title_sort clear cell renal cell carcinoma associated microrna expression signatures identified by an integrated bioinformatics analysis
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3740788/
https://www.ncbi.nlm.nih.gov/pubmed/23841900
http://dx.doi.org/10.1186/1479-5876-11-169
work_keys_str_mv AT chenjiajia clearcellrenalcellcarcinomaassociatedmicrornaexpressionsignaturesidentifiedbyanintegratedbioinformaticsanalysis
AT zhangdaqing clearcellrenalcellcarcinomaassociatedmicrornaexpressionsignaturesidentifiedbyanintegratedbioinformaticsanalysis
AT zhangwenyu clearcellrenalcellcarcinomaassociatedmicrornaexpressionsignaturesidentifiedbyanintegratedbioinformaticsanalysis
AT tangyifei clearcellrenalcellcarcinomaassociatedmicrornaexpressionsignaturesidentifiedbyanintegratedbioinformaticsanalysis
AT yanwenying clearcellrenalcellcarcinomaassociatedmicrornaexpressionsignaturesidentifiedbyanintegratedbioinformaticsanalysis
AT guolingchuan clearcellrenalcellcarcinomaassociatedmicrornaexpressionsignaturesidentifiedbyanintegratedbioinformaticsanalysis
AT shenbairong clearcellrenalcellcarcinomaassociatedmicrornaexpressionsignaturesidentifiedbyanintegratedbioinformaticsanalysis