Evidence map›Paper›PMID 26459852›Full record

ArticleBMC genomics2015

Integrated ordination of miRNA and mRNA expression profiles.

Giacomo Diaz, Fausto Zamboni, Ashley Tice, Patrizia Farci

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 33 papers.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed
0.8field-weighted citation impact, top 29% 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

33 citing papers in PubMed, 42 citations in OpenAlex.

  1. Article
  2. Competition effects regulating the composition of the microRNA pool.Journal of the Royal Society, Interface · 2025
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  7. Identification and Analysis ofFrontiers in aging neuroscience · 2022
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  8. Review
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  20. Stress induced dynamic adjustment of conserved miR164:NAC module.Plant-environment interactions (Hoboken, N.J.) · 2020
    Article
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 at 4 institutions in 2 countries.

Giacomo DiazDepartment of Biomedical Sciences, University of Cagliari, Cagliari, Italy. gdiaz@unica.it.
Fausto ZamboniLiver Transplantation Center, Brotzu Hospital, Cagliari, Italy. faustozamboni@aob.it.
Ashley TiceHepatic Pathogenesis Section, Laboratory of Infectious Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA. ashleytice@gmail.com.
Patrizia FarciHepatic Pathogenesis Section, Laboratory of Infectious Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA. pfarci@niaid.nih.gov.
Azienda Ospedaliera G. Brotzu · ITNational Institute of Allergy and Infectious Diseases · USNational Institutes of Health · USUniversity of Cagliari · IT

Funding

Molecular Mechanisms of Pathogenesis of Acute and Chronic Liver DiseasesZIAAI000999 · NIAID · NATIONAL INSTITUTE OF ALLERGY AND INFECTIOUS DISEASES · PI FARCI, PATRIZIA · 2009 to 2025
$25.3M
Intramural NIH HHS
6 · The paper itself

Abstract

backgroundSeveral studies have investigated miRNA and mRNA co-expression to identify regulatory networks at the transcriptional level. A typical finding of these studies is the presence of both negative and positive miRNA-mRNA correlations. Negative correlations are consistent with the expected, faster degradation of target mRNAs, whereas positive correlations denote the existence of feed-forward regulations mediated by transcription factors. Both mechanisms have been characterized at the molecular level, although comprehensive methods to represent miRNA-mRNA correlations are lacking. At present, genome-wide studies are able to assess the expression of more than 1000 mature miRNAs and more than 35,000 well-characterized human genes. Even if studies are generally restricted to a small subset of genes differentially expressed in specific diseases or experimental conditions, the number of potential correlations remains very high, and needs robust multivariate methods to be conveniently summarized by a small set of data.

resultsNonparametric Kendall correlations were calculated between miRNAs and mRNAs differentially expressed in livers of patients with acute liver failure (ALF) using normal livers as controls. Spurious correlations due to the histopathological composition of samples were removed by partial correlations. Correlations were then transformed into distances and processed by multidimensional scaling (MDS) to map the miRNA and mRNA relationships. These showed: (a) a prominent displacement of miRNA and mRNA clusters in ALF livers, as compared to control livers, indicative of gene expression dysregulation; (b) a clustering of mRNAs consistent with their functional annotations [CYP450, transcription factors, complement, proliferation, HLA class II, monocytes/macrophages, T cells, T-NK cells and B cells], as well as a clustering of miRNAs with the same seed sequence; and (c) a tendency of miRNAs and mRNAs to populate distinct regions of the MDS plot. MDS also allowed to visualize the network of miRNA-mRNA target pairs.

conclusionsDifferent features of miRNA and mRNA relationships can be represented as thematic maps within the framework of MDS obtained from pairwise correlations. The symmetric distribution of positive and negative correlations between miRNA and mRNA expression suggests that miRNAs are involved in a complex bidirectional molecular network, including, but not limited to, the inhibitory regulation of miRNA targets.

Indexed as

Gene Expression ProfilingGene Expression RegulationComputational BiologyGene Regulatory NetworksHumansLiverLiver Failure, AcuteMicroRNAsNecrosisRNA InterferenceRNA, MessengerMicroRNAsRNA, Messenger

Identifiers

PMID26459852
PMCPMC4603994
OpenAlexW1944858750

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.