Evidence map›Paper›PMID 30188759›Full record

ReviewCancer biology & therapy2019

A new look at molecular biology of breast cancer.

Chi Ma, Manoj Nepal, Jin-Hee Kim, Ping Fan, Peiwen Fei

Open access · hybridAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Cancer biology & therapy, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.6field-weighted citation impact, top 16% 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

2 citing papers in PubMed, 26 citations in OpenAlex.

  1. Article
  2. Review
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 2 institutions in 1 country.

Chi Maa University of Hawaii Cancer Center.
Manoj Nepala University of Hawaii Cancer Center.
Jin-Hee Kima University of Hawaii Cancer Center.
Ping Fana University of Hawaii Cancer Center.
Peiwen Feia University of Hawaii Cancer Center.
Cancer Center of Hawaii · USUniversity of Hawaii System · US

Funding

Molecular Insights into the Fanconi Anemia Tumor Suppressor Signaling PathwayR01CA188251 · NCI · UNIVERSITY OF HAWAII AT MANOA · PI FEI, PEIWEN · 2014 to 2018
$1.6M
Roles of the Fanconi Anemia Pathway in Bladder TumorigenesisR01CA136532 · NCI · UNIVERSITY OF MINNESOTA · PI FEI, PEIWEN · 2009 to 2013
$1.5M
NCI NIH HHS R01 CA136532NCI NIH HHS R01 CA188251
6 · The paper itself

Abstract

In the past 25 years, incidence rates of breast cancer have risen about 30% in westernized countries. Mutations in BRCA1 and BRCA2 are the most prominent cause of breast cancer. However, these cancer susceptibility genes (BRCAs) only account for a few percent of women suffering breast tumor. With our understanding that BRCAs are Fanconi Anemia (FA) genes, investigations into the FA signaling network should provide a previously unrecognized key to unlock in-depth insights into both etiology and treatment of breast cancer. Here, we discuss utilization of the FA signaling as a unique genetic model system to expand our knowledge about the molecular biology of breast cancer and potential applications of the gained knowledge to enable preventive and therapeutic approaches for breast cancer patient care.

Indexed as

Gene Expression Regulation, NeoplasticGenetic Predisposition to DiseaseBreast NeoplasmsCarcinogenesisChromosomal InstabilityFanconi Anemia Complementation Group ProteinsFemaleHumansModels, GeneticMutationSignal TransductionFanconi Anemia Complementation Group Proteinsbiomarker and cancer preventionBRCAsbreast cancerdiagnosis & treatmentFanconi anemia (FA)genetic consultationthe FA signaling pathway or network

Identifiers

PMID30188759
PMCPMC6343705
OpenAlexW2891019025

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.