Evidence map›Paper›PMID 26100580›Full record

ArticleBMC genomics2015

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

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

Open access · goldAbstract read
In one paragraph

Article in BMC genomics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
3.3field-weighted citation impact, top 8% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

27 citing papers in PubMed, 57 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 4 institutions in 2 countries.

Weirong Cui
Yulan Qian
Xiaoke Zhou
Yuxin Lin
Junfeng Jiang
Jiajia Chen
Zhongming Zhao
Bairong Shen
Soochow University · CNFirst Affiliated Hospital of Soochow University · CNSuzhou University of Science and Technology · CNVanderbilt University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundProstate 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.

resultsNine 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.

conclusionThe 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.

Indexed as

Biomarkers, TumorDatabases, GeneticGenetic Predisposition to DiseaseHumansMalePrincipal Component AnalysisPrognosisProstatic NeoplasmsRNA, Long NoncodingSequence Analysis, RNABiomarkers, TumorRNA, Long Noncoding

Identifiers

PMID26100580
PMCPMC4474418
OpenAlexW1889158580

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.