Evidence map›Paper›PMID 26484677›Full record

ArticlePloS one2015

Integrated Analysis Reveals together miR-182, miR-200c and miR-221 Can Help in the Diagnosis of Prostate Cancer.

Yinmin Gu, Danqing Lei, Xia Qin, Panyu Chen, Yi Ming Zou, Yanling Hu

Abstract read
In one paragraph

Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
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

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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

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Review
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

6 authors.

Yinmin GuExperimental Center of Medical Sciences, Guangxi Medical University, Nanning, Guangxi, China.
Danqing LeiExperimental Center of Medical Sciences, Guangxi Medical University, Nanning, Guangxi, China.
Xia QinDermatological Department, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
Panyu ChenExperimental Center of Medical Sciences, Guangxi Medical University, Nanning, Guangxi, China.
Yi Ming ZouDepartment of Mathematical Sciences, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, United States of America.
Yanling HuExperimental Center of Medical Sciences, Guangxi Medical University, Nanning, Guangxi, China; Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Research has shown that microRNAs are promising biomarkers that can be used to promote a more accurate diagnosis of cancer. In this study, we developed an integrated multi-step selection process to analyze available high-throughput datasets to obtain information on microRNAs as cancer biomarkers. Applying this approach to the microRNA expression profiles of prostate cancer and the datasets in The Cancer Genome Atlas Data Portal, we identified miRNA-182, miRNA-200c and miRNA-221 as possible biomarkers for prostate cancer. The associations between the expressions of these three microRNAs with clinical parameters as well as their diagnostic capability were studied. Several online databases were used to predict the target genes of these three microRNAs, and the results were confirmed by significant statistical correlations. Comparing with the other 18 types of cancers listed in The Cancer Genome Atlas Data Portal, we found that the combination of both miRNA-182 and miRNA-200c being up-regulated and miRNA-221 being down-regulated only happens in prostate cancer. This provides a unique biological characteristic for prostate cancer that can potentially be used for diagnosis based on tissue testing. In addition, our study also revealed that these three microRNAs are associated with the pathological status of prostate cancer.

Indexed as

Biomarkers, TumorDatabases, GeneticGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMaleMicroRNAsProstatic NeoplasmsBiomarkers, TumorMicroRNAsMIR221, humanMirn182 microRNA, humanMIRN200 microRNA, human

Identifiers

PMID26484677
PMCPMC4618846

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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.