Evidence map›Paper›PMID 33801595›Full record

ArticleCancers2021

Prognostic Evidence of the miRNA-Based Ovarian Cancer Signature MiROvaR in Independent Datasets.

Loris De Cecco, Marina Bagnoli, Paolo Chiodini, Sandro Pignata, Delia Mezzanzanica

Open access · goldAbstract read
In one paragraph

Article in Cancers, 2021. 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
0.6field-weighted citation impact, top 41% 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

4 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Article
  3. The Role of miRNA in Ovarian Cancer: an Overview.Reproductive sciences (Thousand Oaks, Calif.) · 2022
    Review
  4. 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

5 authors at 3 institutions in 1 country.

Loris De CeccoIntegrated Biology Platform, Department of Applied Research and Technology Development, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy.ORCID 0000-0002-7066-473X
Marina BagnoliMolecular Therapies Unit, Department of Research, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy.
Paolo ChiodiniMedical Statistics Unit, University of Campania "Luigi Vanvitelli", 80138 Naples, Italy.
Sandro PignataUrogynaecological Medical Oncology Unit, Istituto Nazionale Tumori-IRCCS-"Fondazione G. Pascale", 80131 Naples, Italy.
Delia MezzanzanicaMolecular Therapies Unit, Department of Research, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy.ORCID 0000-0002-9664-6871
Fondazione IRCCS Istituto Nazionale dei Tumori · ITIstituto Nazionale Tumori IRCCS "Fondazione G. Pascale" · ITUniversity of Campania "Luigi Vanvitelli" · IT

Funding

Associazione Italiana per la Ricerca sul Cancro IG 17475; IG 19243; IG 5776Fondazione Cariplo 2013-0865Ministero della Salute RF 2016-02363995; 5 x 1000 Funds - 2013
6 · The paper itself

Abstract

Epithelial ovarian cancer (EOC) remains the second most common cause of gynecological cancer deaths. To improve patients' outcomes, we still need reliable biomarkers of early relapse, of which external independent validation is a crucial process. Our previously established prognostic signature, MiROvaR, including 35 microRNAs (miRNA) able to stratify EOC patients for their risk of relapse, was challenged on a new independent cohort of 197 EOC patients included in the Pelvic Mass Study whose miRNA profile was made publically available, thus resulting in the only accessible database aside from the EOC TCGA collection. Following accurate data matrix adjustment to account for the use of different miRNA platforms, MiROvaR confirmed its ability to discriminate early relapsing patients. The model's original cutoff separated 156 (79.2%) high- and 41 (20.8%) low-risk patients with median progression free survival (PFS) of 16.3 months and not yet reached (NYR), respectively (hazard ratio (HR): 2.42-95% Confidence Interval (CI) 1.49-3.93; Log-rank

Indexed as

early relapseepithelial ovarian cancerindependent validationmicroRNAmolecular predictor

Identifiers

PMID33801595
PMCPMC8037414
OpenAlexW3152076883

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.