Evidence map›Paper›PMID 29414922›Full record

ReviewInternational journal of molecular sciences2018

Perspective Insight into Future Potential Fusion Gene Transcript Biomarker Candidates in Breast Cancer.

Ryong Nam Kim, Hyeong-Gon Moon, Wonshik Han, Dong-Young Noh

Open access · goldAbstract readReview
In one paragraph

Review in International journal of molecular sciences, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
0.9field-weighted citation impact, top 32% 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

12 citing papers in PubMed, 23 citations in OpenAlex.

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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

4 authors at 2 institutions in 1 country.

Ryong Nam KimCenter for Medical Innovation, Biomedical Research Institute, Seoul National University Hospital, Seoul 03080, Korea. ryongnamkim@gmail.com.
Hyeong-Gon MoonDepartment of Surgery, Seoul National University College of Medicine, Seoul 03080, Korea. moonhgsurg@gmail.com.
Wonshik HanDepartment of Surgery, Seoul National University College of Medicine, Seoul 03080, Korea. han@snu.ac.kr.
Dong-Young NohDepartment of Surgery, Seoul National University College of Medicine, Seoul 03080, Korea. dyno@snu.ac.kr.
Seoul National University · KRSeoul National University Hospital · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Next generation sequencing has accelerated the discovery of a variety of new fusion gene types in clinical breast cancer samples by analyzing cancer genomes and transcriptomes. Although previous studies have focused on a few clinically validated oncogenic fusion genes as diagnostic and therapeutic targets in breast cancer, a perspective consideration has not been given thus far for a plethora of breast cancer fusion genes, which are being newly identified at an overwhelmingly increasing pace. In this perspective review, we discuss diverse fusion gene types recently identified in a variety of breast cancer subtypes, including breast clinical cancer samples in TCGA (The Cancer Genome Atlas) database. This perspective review will confer fresh and promising guidance onto breast cancer surgeons, clinical oncologists, and tumor biologists in determining research directions for seeking and developing novel fusion gene biomarkers for breast cancer diagnostics and therapeutic treatment in upcoming years.

Indexed as

Biomarkers, TumorBreast NeoplasmsDatabases, GeneticFemaleHigh-Throughput Nucleotide SequencingHumansNeoplasm Recurrence, LocalOncogene FusionSequence Analysis, RNABiomarkers, Tumorbiomarkerbreast cancerfusion gene transcript

Identifiers

PMID29414922
PMCPMC5855724
OpenAlexW2793487091

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.