Evidence map›Paper›PMID 30897768›Full record

ReviewNon-coding RNA2019

Breast Cancer and miR-SNPs: The Importance of miR Germ-Line Genetics.

Poonam Malhotra, Graham H Read, Joanne B Weidhaas

Open access · goldAbstract readReview
In one paragraph

Review in Non-coding RNA, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
1.2field-weighted citation impact, top 25% 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

10 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Germline variants disrupting microRNAs predict long-term genitourinary toxicity after prostate cancer radiation.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2022
    Article
  6. Article
  7. Article
  8. Review
  9. Article
  10. 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

3 authors at 1 institution in 1 country.

Poonam MalhotraDepartment of Radiation Oncology, David Geffen School of Medicine, University of California, Los Angeles, CA 90001, USA. PMalhotra@mednet.ucla.edu.
Graham H ReadDepartment of Radiation Oncology, David Geffen School of Medicine, University of California, Los Angeles, CA 90001, USA. gread@ucla.edu.ORCID 0000-0002-7716-6506
Joanne B WeidhaasDepartment of Radiation Oncology, David Geffen School of Medicine, University of California, Los Angeles, CA 90001, USA. jweidhaas@mednet.ucla.edu.ORCID 0000-0002-5096-3281
University of California, Los Angeles · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent studies in cancer diagnostics have identified microRNAs (miRNAs) as promising cancer biomarkers. Single nucleotide polymorphisms (SNPs) in miRNA binding sites, seed regions, and coding sequences can help predict breast cancer risk, aggressiveness, response to stimuli, and prognosis. This review also documents significant known miR-SNPs in miRNA biogenesis genes and their effects on gene regulation in breast cancer, taking into account the genetic background and ethnicity of the sampled populations. When applicable, miR-SNPs are evaluated in the context of other patient factors, including mutations, hormonal status, and demographics. Given the power of miR-SNPs to predict patient cancer risk, prognosis, and outcomes, further study of miR-SNPs is warranted to improve efforts towards personalized medicine.

Indexed as

3′UTRbreast cancermiR-SNPspolymorphismpredictive biomarkers

Identifiers

PMID30897768
PMCPMC6468861
OpenAlexW2922677657

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.