Evidence map›Paper›PMID 25497279›Full record

ArticleMolecular oncology2015

MiR-score: a novel 6-microRNA signature that predicts survival outcomes in patients with malignant pleural mesothelioma.

Michaela B Kirschner, Yuen Yee Cheng, Nicola J Armstrong, Ruby C Y Lin, Steven C Kao, Anthony Linton, Sonja Klebe, Brian C McCaughan, Nico van Zandwijk, Glen Reid

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Molecular oncology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
45citing papers in PubMed, 1 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

45 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

10 authors.

Michaela B KirschnerAsbestos Diseases Research Institute (ADRI), Sydney, NSW, Australia; Sydney Medical School, The University of Sydney, Sydney, NSW, Australia. Electronic address: michaela.kirschner@sydney.edu.au.
Yuen Yee ChengAsbestos Diseases Research Institute (ADRI), Sydney, NSW, Australia. Electronic address: yuen.cheng@sydney.edu.au.
Nicola J ArmstrongSchool of Mathematics and Statistics, The University of Sydney, Sydney, NSW, Australia. Electronic address: nicola.armstrong@sydney.edu.au.
Ruby C Y LinAsbestos Diseases Research Institute (ADRI), Sydney, NSW, Australia; School of Biotechnology and Biomolecular Sciences, University of New South Wales, NSW, Australia. Electronic address: ruby.lin@sydney.edu.au.
Steven C KaoAsbestos Diseases Research Institute (ADRI), Sydney, NSW, Australia; Department of Medical Oncology, Chris O'Brien Lifehouse, Sydney, NSW, Australia. Electronic address: Steven.Kao@lh.org.au.
Anthony LintonAsbestos Diseases Research Institute (ADRI), Sydney, NSW, Australia; Sydney Medical School, The University of Sydney, Sydney, NSW, Australia; Department of Medical Oncology, Concord Repatriation General Hospital, Sydney, NSW, Australia. Electronic address: anthony.linton@sydney.ed.au.
Sonja KlebeDepartment of Anatomical Pathology, Flinders Medical Centre, Adelaide, SA, Australia. Electronic address: Sonja.Klebe@health.sa.gov.au.
Brian C McCaughanSydney Cardiothoracic Surgeons, RPA Medical Centre, Sydney, NSW, Australia. Electronic address: brianmccaughan@gmail.com.
Nico van ZandwijkAsbestos Diseases Research Institute (ADRI), Sydney, NSW, Australia; Sydney Medical School, The University of Sydney, Sydney, NSW, Australia. Electronic address: nico.vanzandwijk@sydney.edu.au.
Glen ReidAsbestos Diseases Research Institute (ADRI), Sydney, NSW, Australia; Sydney Medical School, The University of Sydney, Sydney, NSW, Australia. Electronic address: glen.reid@sydney.edu.au.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPrognosis of malignant pleural mesothelioma (MPM) is poor, and predicting the outcomes of treatment is difficult. Here we investigate the potential of microRNA expression to estimate prognosis of MPM patients.

methodsCandidate microRNAs from microarray profiling of tumor samples from 8 long (median: 53.7 months) and 8 short (median: 6.4 months) survivors following extrapleural pneumonectomy (EPP) were validated by RT-qPCR in 48 additional EPP samples. Kaplan-Meier log ranking was used to further explore the association between microRNA expression and overall survival (OS). Binary logistic regression was used to construct a microRNA signature (miR-Score) that was able to predict an OS of ≥20 months. Performance of the miR-Score was evaluated by receiver operating characteristic (ROC) curve analysis and validated in a series of 43 tumor samples from patients who underwent palliative surgery [pleurectomy/decortication (P/D)].

resultsThe miR-Score, using expression data of six microRNAs (miR-21-5p, -23a-3p, -30e-5p, -221-3p, -222-3p, and -31-5p), enabled prediction of long survival with an accuracy of 92.3% for EPP and 71.9% for palliative P/D. Hazard ratios for score-negative patients were 4.12 (95% CI: 2.03-8.37) for EPP and 1.93 (95% CI: 1.01-3.69) for P/D. Importantly, adding the miR-Score to a set of clinical selection criteria (histology, age, gender) increased predictive accuracy in the independent validation set from 76.3% for clinical factors only to 87.3%.

conclusionsThis study has identified a novel 6-microRNA signature (miR-Score) that can accurately predict prognosis of MPM patients.

Indexed as

Gene Expression ProfilingGene Expression Regulation, NeoplasticAdultAgedAged, 80 and overCohort StudiesFemaleHumansKaplan-Meier EstimateLung NeoplasmsMaleMesotheliomaMesothelioma, MalignantMicroRNAsMiddle AgedMultivariate AnalysisMicroRNAsBiomarkerMalignant pleural mesotheliomaMicroRNAsPrognosisSurvival

Identifiers

PMID25497279
PMCPMC5528709

What OpenQuestion holds

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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.