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Discovery and characterization of long intergenic non-coding RNAs (lincRNA) module biomarkers in prostate cancer: an integrative analysis of RNA-Seq data

Overview of attention for article published in BMC Genomics, June 2015
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Title
Discovery and characterization of long intergenic non-coding RNAs (lincRNA) module biomarkers in prostate cancer: an integrative analysis of RNA-Seq data
Published in
BMC Genomics, June 2015
DOI 10.1186/1471-2164-16-s7-s3
Pubmed ID
Authors

Weirong Cui, Yulan Qian, Xiaoke Zhou, Yuxin Lin, Junfeng Jiang, Jiajia Chen, Zhongming Zhao, Bairong Shen

Abstract

Prostate cancer (PCa) is a leading cause of cancer-related death of men worldwide. There is an urgent need to develop novel biomarkers for PCa prognosis and diagnosis in the post prostate-specific antigen era. Long intergenic noncoding RNAs (lincRNAs) play essential roles in many physiological processes and can serve as alternative biomarkers for prostate cancer, but there has been no systematic investigation of lincRNAs in PCa yet. Nine lincRNA co-expression modules were identified from PCa RNA-Seq data. The association between the principle component of each module and the PCa phenotype was examined by calculating the Pearson's correlation coefficients. Three modules (M1, M3, and M5) were found associated with PCa. Two modules (M3 and M5) were significantly enriched with lincRNAs, and one of them, M3, may be used as a lincRNA module-biomarker for PCa diagnosis. This module includes seven essential lincRNAs: TCONS_l2_00001418, TCONS_l2_00008237, TCONS_l2_00011130, TCONS_l2_00013175, TCONS_l2_00022611, TCONS_l2_00022670 and linc-PXN-1. The clustering analysis and microRNA enrichment analysis further confirmed our findings. The correlation between lincRNAs and protein-coding genes is helpful for further exploration of functional mechanisms of lincRNAs in PCa. This study provides some important insights into the roles of lincRNAs in PCa and suggests a few lincRNAs as candidate biomarkers for PCa diagnosis and prognosis.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 1 3%
Unknown 33 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 26%
Student > Ph. D. Student 6 18%
Student > Master 4 12%
Student > Bachelor 3 9%
Student > Doctoral Student 2 6%
Other 5 15%
Unknown 5 15%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 8 24%
Agricultural and Biological Sciences 8 24%
Computer Science 5 15%
Medicine and Dentistry 4 12%
Neuroscience 2 6%
Other 0 0%
Unknown 7 21%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 June 2015.
All research outputs
#18,417,643
of 22,815,414 outputs
Outputs from BMC Genomics
#8,181
of 10,653 outputs
Outputs of similar age
#192,482
of 266,807 outputs
Outputs of similar age from BMC Genomics
#198
of 233 outputs
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