Evidence map›Paper›PMID 31161221›Full record

ArticleJournal of the National Cancer Institute2020

Temporal Stability and Prognostic Biomarker Potential of the Prostate Cancer Urine miRNA Transcriptome.

Jouhyun Jeon, Ekaterina Olkhov-Mitsel, Honglei Xie, Cindy Q Yao, Fang Zhao, Sahar Jahangiri, Carmelle Cuizon, Seville Scarcello, Renu Jeyapala, John D Watson and 11 more

Open access · bronzeAbstract read
In one paragraph

Article in Journal of the National Cancer Institute, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed
3.3field-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

26 citing papers in PubMed, 65 citations in OpenAlex.

  1. Article
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  4. Article
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  6. Article
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  8. Review
  9. Article
  10. Article
  11. miRNA in Molecular Diagnostics.Bioengineering (Basel, Switzerland) · 2022
    Review
  12. Review
  13. Article
  14. Review
  15. MicroRNAs as Biomarkers for Ionizing Radiation Injury.Frontiers in cell and developmental biology · 2022
    Review
  16. Review
  17. Article
  18. Article
  19. A flexible model-free prediction-based framework for feature ranking.Journal of machine learning research : JMLR · 2021
    Article
  20. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

21 authors at 5 institutions in 3 countries.

Jouhyun JeonOntario Institute for Cancer Research, Toronto, ON, Canada.
Ekaterina Olkhov-MitselLunenfeld-Tannenbaum Research Institute, Sinai Health System, Toronto, ON, Canada.
Honglei XieOntario Institute for Cancer Research, Toronto, ON, Canada.
Cindy Q YaoOntario Institute for Cancer Research, Toronto, ON, Canada.
Fang ZhaoLunenfeld-Tannenbaum Research Institute, Sinai Health System, Toronto, ON, Canada.
Sahar JahangiriSunnybrook Research Institute and Department of Radiation Oncology, Sunnybrook-Odette Cancer Centre, Toronto, ON, Canada.
Carmelle CuizonLunenfeld-Tannenbaum Research Institute, Sinai Health System, Toronto, ON, Canada.
Seville ScarcelloSunnybrook Research Institute and Department of Radiation Oncology, Sunnybrook-Odette Cancer Centre, Toronto, ON, Canada.
Renu JeyapalaLunenfeld-Tannenbaum Research Institute, Sinai Health System, Toronto, ON, Canada.
John D WatsonOntario Institute for Cancer Research, Toronto, ON, Canada.
Michael FraserOntario Institute for Cancer Research, Toronto, ON, Canada.
Jessica RaySunnybrook Research Institute and Department of Radiation Oncology, Sunnybrook-Odette Cancer Centre, Toronto, ON, Canada.
Kristina CommissoSunnybrook Research Institute and Department of Radiation Oncology, Sunnybrook-Odette Cancer Centre, Toronto, ON, Canada.
Andrew LoblawSunnybrook Research Institute and Department of Radiation Oncology, Sunnybrook-Odette Cancer Centre, Toronto, ON, Canada.
Neil E FleshnerPrincess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.
Robert G BristowPrincess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.
Michelle DownesOntario Institute for Cancer Research, Toronto, ON, Canada.
Danny VespriniSunnybrook Research Institute and Department of Radiation Oncology, Sunnybrook-Odette Cancer Centre, Toronto, ON, Canada.
Stanley LiuSunnybrook Research Institute and Department of Radiation Oncology, Sunnybrook-Odette Cancer Centre, Toronto, ON, Canada.
Bharati BapatLunenfeld-Tannenbaum Research Institute, Sinai Health System, Toronto, ON, Canada.
Paul C BoutrosOntario Institute for Cancer Research, Toronto, ON, Canada.
Ontario Institute for Cancer Research · CASunnybrook Health Science Centre · CALunenfeld-Tanenbaum Research Institute · CAUniversity Health Network · CAUniversity of Toronto · CA

Funding

Women's CancersP30CA016042 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Samuel Wheeler French · 1985 to 2026
$134.5M
Development of Protein Biomarkers in Post-DRE Urine for use in Liquid Biopsy of Prostate CancerU01CA214194 · NCI · EASTERN VIRGINIA MEDICAL SCHOOL · PI BOUTROS, PAUL CHRISTOPHER, KISLINGER, THOMAS · 2016 to 2021
$2.5M
CIHRNCI NIH HHS P30 CA016042NCI NIH HHS U01 CA214194
6 · The paper itself

Abstract

backgroundThe development of noninvasive tests for the early detection of aggressive prostate tumors is a major unmet clinical need. miRNAs are promising noninvasive biomarkers: they play essential roles in tumorigenesis, are stable under diverse analytical conditions, and can be detected in body fluids.

methodsWe measured the longitudinal stability of 673 miRNAs by collecting serial urine samples from 10 patients with localized prostate cancer. We then measured temporally stable miRNAs in an independent training cohort (n = 99) and created a biomarker predictive of Gleason grade using machine-learning techniques. Finally, we validated this biomarker in an independent validation cohort (n = 40).

resultsWe found that each individual has a specific urine miRNA fingerprint. These fingerprints are temporally stable and associated with specific biological functions. We identified seven miRNAs that were stable over time within individual patients and integrated them with machine-learning techniques to create a novel biomarker for prostate cancer that overcomes interindividual variability. Our urine biomarker robustly identified high-risk patients and achieved similar accuracy as tissue-based prognostic markers (area under the receiver operating characteristic = 0.72, 95% confidence interval = 0.69 to 0.76 in the training cohort, and area under the receiver operating characteristic curve = 0.74, 95% confidence interval = 0.55 to 0.92 in the validation cohort).

conclusionsThese data highlight the importance of quantifying intra- and intertumoral heterogeneity in biomarker development. This noninvasive biomarker may usefully supplement invasive or expensive radiologic- and tissue-based assays.

Indexed as

Biomarkers, TumorCarcinogenesisCohort StudiesHumansLongitudinal StudiesMaleMicroRNAsNeoplasm GradingPrognosisProstatic NeoplasmsReproducibility of ResultsTranscriptomeBiomarkers, TumorMicroRNAs

Identifiers

PMID31161221
PMCPMC7073919
OpenAlexW2948082370

What OpenQuestion holds

Textmetadata
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.