Evidence map›Paper›PMID 34579745›Full record

ArticleBreast cancer research : BCR2021

Robust inflammatory breast cancer gene signature using nonparametric random forest analysis.

Alaa Zare, Lynne-Marie Postovit, John Maringa Githaka

Open access · goldAbstract read
In one paragraph

Article in Breast cancer research : BCR, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
2.1field-weighted citation impact, top 12% 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

13 citing papers in PubMed, 25 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Review
  10. Article
  11. Article
  12. Article
  13. 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 2 institutions in 1 country.

Alaa ZareDepartment of Pediatrics, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada.
Lynne-Marie PostovitDepartment of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada.
John Maringa GithakaDepartment of Biochemistry, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada. maringa@ualberta.ca.ORCID 0000-0003-1974-0415
University of Alberta · CAQueen's University · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Inflammatory breast cancer (IBC) is a rare, aggressive cancer found in all the molecular breast cancer subtypes. Despite extensive previous efforts to screen for transcriptional differences between IBC and non-IBC patients, a robust IBC-specific molecular signature has been elusive. We report a novel IBC-specific gene signature (59 genes; G59) that achieves 100% accuracy in discovery and validation samples (45/45 correct classification) and remarkably only misclassified one sample (60/61 correct classification) in an independent dataset. G59 is independent of ER/HER2 status, molecular subtypes and is specific to untreated IBC samples, with most of the genes being enriched for plasma membrane cellular component proteins, interleukin (IL), and chemokine signaling pathways. Our finding suggests the existence of an IBC-specific molecular signature, paving the way for the identification and validation of targetable genomic drivers of IBC.

Indexed as

Biomarkers, TumorDatabases, GeneticFemaleHumansInflammatory Breast NeoplasmsInterleukinsMachine LearningMembrane ProteinsSignal TransductionStatistics, NonparametricBiomarkers, TumorInterleukinsMembrane ProteinsBreast cancerIBCIBC signatureMachine learningRandom forest

Identifiers

PMID34579745
PMCPMC8477487
OpenAlexW3203815376

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.