Evidence map›Paper›PMID 22452920›Full record

ArticleBMC research notes2012

Identifying microRNA/mRNA dysregulations in ovarian cancer.

Gregory D Miles, Michael Seiler, Lorna Rodriguez, Gunaretnam Rajagopal, Gyan Bhanot

Open access · goldAbstract read
In one paragraph

Article in BMC research notes, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.

0numbers the graph read from it
0cells of the map it votes in
53citing papers in PubMed
3.7field-weighted citation impact, top 7% 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

53 citing papers in PubMed, 104 citations in OpenAlex.

  1. Review
  2. [Research Advances of RAD51AP1 in Tumor Progression and Drug Resistance].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2023
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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

5 authors at 3 institutions in 1 country.

Gregory D MilesThe Cancer Institute of New Jersey, New Brunswick, NJ, USA. milesgr@umdnj.edu
Michael Seiler
Lorna Rodriguez
Gunaretnam Rajagopal
Gyan Bhanot
Cancer Institute of Florida · USRutgers Cancer Institute of New JerseyRutgers, The State University of New Jersey · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMicroRNAs are a class of noncoding RNA molecules that co-regulate the expression of multiple genes via mRNA transcript degradation or translation inhibition. Since they often target entire pathways, they may be better drug targets than genes or proteins. MicroRNAs are known to be dysregulated in many tumours and associated with aggressive or poor prognosis phenotypes. Since they regulate mRNA in a tissue specific manner, their functional mRNA targets are poorly understood. In previous work, we developed a method to identify direct mRNA targets of microRNA using patient matched microRNA/mRNA expression data using an anti-correlation signature. This method, applied to clear cell Renal Cell Carcinoma (ccRCC), revealed many new regulatory pathways compromised in ccRCC. In the present paper, we apply this method to identify dysregulated microRNA/mRNA mechanisms in ovarian cancer using data from The Cancer Genome Atlas (TCGA).

methodsTCGA Microarray data was normalized and samples whose class labels (tumour or normal) were ambiguous with respect to consensus ensemble K-Means clustering were removed. Significantly anti-correlated and correlated genes/microRNA differentially expressed between tumour and normal samples were identified. TargetScan was used to identify gene targets of microRNA.

resultsWe identified novel microRNA/mRNA mechanisms in ovarian cancer. For example, the expression level of RAD51AP1 was found to be strongly anti-correlated with the expression of hsa-miR-140-3p, which was significantly down-regulated in the tumour samples. The anti-correlation signature was present separately in the tumour and normal samples, suggesting a direct causal dysregulation of RAD51AP1 by hsa-miR-140-3p in the ovary. Other pairs of potentially biological relevance include: hsa-miR-145/E2F3, hsa-miR-139-5p/TOP2A, and hsa-miR-133a/GCLC. We also identified sets of positively correlated microRNA/mRNA pairs that are most likely result from indirect regulatory mechanisms.

conclusionsOur findings identify novel microRNA/mRNA relationships that can be verified experimentally. We identify both generic microRNA/mRNA regulation mechanisms in the ovary as well as specific microRNA/mRNA controls which are turned on or off in ovarian tumours. Our results suggest that the disease process uses specific mechanisms which may be significant for their utility as early detection biomarkers or in the development of microRNA therapies in treating ovarian cancers. The positively correlated microRNA/mRNA pairs suggest the existence of novel regulatory mechanisms that proceed via intermediate states (indirect regulation) in ovarian tumorigenesis.

Indexed as

Gene Expression ProfilingGene Expression Regulation, NeoplasticAntigens, NeoplasmCluster AnalysisDNA-Binding ProteinsDNA Topoisomerases, Type IIE2F3 Transcription FactorFemaleGlutamate-Cysteine LigaseHumansMicroRNAsOvarian NeoplasmsPoly-ADP-Ribose Binding ProteinsRNA-Binding ProteinsRNA, MessengerAntigens, NeoplasmDNA-Binding ProteinsDNA Topoisomerases, Type IIE2F3 protein, humanE2F3 Transcription FactorGlutamate-Cysteine LigaseMicroRNAsMIRN133 microRNA, humanMIRN139 microRNA, humanMirn140 microRNA, humanMIRN145 microRNA, humanPoly-ADP-Ribose Binding ProteinsRAD51AP1 protein, humanRNA-Binding ProteinsRNA, MessengerTOP2A protein, human

Identifiers

PMID22452920
PMCPMC3342161
OpenAlexW2138152797

What OpenQuestion holds

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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.