Evidence map›Paper›PMID 17565689›Full record

ArticleBMC genomics2007

Characterization of microRNA expression profiles in normal human tissues.

Yu Liang, Dana Ridzon, Linda Wong, Caifu Chen

Abstract readComparative Study
In one paragraph

Article in BMC genomics, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 543 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
543citing papers in PubMed, 4 pooled it
–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

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

543 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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  15. MicroRNAs and synaptic dysfunction in Parkinson's disease.Molecular therapy. Nucleic acids · 2025
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483 more citing papers are in PubMed but not listed here.

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

4 authors.

Yu LiangMolecular and Cell Biology-R&D, Applied Biosystems, Foster City, CA 94404, USA. Yu.Liang@appliedbiosystems.com
Dana Ridzon
Linda Wong
Caifu Chen

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMeasuring the quantity of miRNAs in tissues of different physiological and pathological conditions is an important first step to investigate the functions of miRNAs. Matched samples from normal state can provide essential baseline references to analyze the variation of miRNA abundance.

resultsWe provided expression data of 345 miRNAs in 40 normal human tissues, which identified universally expressed miRNAs, and several groups of miRNAs expressed exclusively or preferentially in certain tissue types. Many miRNAs with co-regulated expression patterns are located within the same genomic clusters, and candidate transcriptional factors that control the pattern of their expression may be identified by a comparative genomic strategy. Hierarchical clustering of normal tissues by their miRNA expression profiles basically followed the structure, anatomical locations, and physiological functions of the organs, suggesting that functions of a miRNA could be appreciated by linking to the biologies of the tissues in which it is uniquely expressed. Many predicted target genes of miRNAs that had specific reduced expression in brain and peripheral blood mononuclear cells are required for embryonic development of the nervous and hematopoietic systems based on database search.

conclusionWe presented a global view of tissue distribution of miRNAs in relation to their chromosomal locations and genomic structures. We also described evidence from the cis-regulatory elements and the predicted target genes of miRNAs to support their tissue-specific functional roles to regulate the physiologies of the normal tissues in which they are expressed.

Indexed as

Gene Expression ProfilingComputational BiologyHumansMicroRNAsRegression AnalysisMicroRNAs

Identifiers

PMID17565689
PMCPMC1904203

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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.