Evidence map›Paper›PMID 27410227›Full record

ArticlePloS one2016

A Physical Mechanism and Global Quantification of Breast Cancer.

Chong Yu, Jin Wang

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2016. 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
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

13 citing papers in PubMed, 25 citations in OpenAlex.

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  12. Modeling the response of a tumor-suppressive network to mitogenic and oncogenic signals.Proceedings of the National Academy of Sciences of the United States of America · 2017
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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

2 authors at 2 institutions in 2 countries.

Chong YuState Key Laboratory of Electroanalytical Chemistry/Changchun Institute of Applied Chemistry, Chinese Academy of Sciences/Changchun, Jilin 130022, China.
Jin WangState Key Laboratory of Electroanalytical Chemistry/Changchun Institute of Applied Chemistry, Chinese Academy of Sciences/Changchun, Jilin 130022, China.
Chinese Academy of Sciences · CNState University of New York · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Initiation and progression of cancer depend on many factors. Those on the genetic level are often considered crucial. To gain insight into the physical mechanisms of breast cancer, we construct a gene regulatory network (GRN) which reflects both genetic and environmental aspects of breast cancer. The construction of the GRN is based on available experimental data. Three basins of attraction, representing the normal, premalignant and cancer states respectively, were found on the phenotypic landscape. The progression of breast cancer can be seen as switching transitions between different state basins. We quantified the stabilities and kinetic paths of the three state basins to uncover the biological process of breast cancer formation. The gene expression levels at each state were obtained, which can be tested directly in experiments. Furthermore, by performing global sensitivity analysis on the landscape topography, six key genes (HER2, MDM2, TP53, BRCA1, ATM, CDK2) and four regulations (HER2⊣TP53, CDK2⊣BRCA1, ATM→MDM2, TP53→ATM) were identified as being critical for breast cancer. Interestingly, HER2 and MDM2 are the most popular targets for treating breast cancer. BRCA1 and TP53 are the most important oncogene of breast cancer and tumor suppressor gene, respectively. This further validates the feasibility of our model and the reliability of our prediction results. The regulation ATM→MDM2 has been extensive studied on DNA damage but not on breast cancer. We notice the importance of ATM→MDM2 on breast cancer. Previous studies of breast cancer have often focused on individual genes and the anti-cancer drugs are mainly used to target the individual genes. Our results show that the network-based strategy is more effective on treating breast cancer. The landscape approach serves as a new strategy for analyzing breast cancer on both the genetic and epigenetic levels and can help on designing network based medicine for breast cancer.

Indexed as

Gene Regulatory NetworksBreast NeoplasmsE2F1 Transcription FactorEarly Detection of CancerFemaleGene Expression Regulation, NeoplasticGenes, BRCA1Genes, erbB-2Genes, p53HumansModels, GeneticPhenotypePrecancerous ConditionsE2F1 protein, humanE2F1 Transcription Factor

Identifiers

PMID27410227
PMCPMC4943646
OpenAlexW2463855622

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.