ArticlePloS one2016
A Physical Mechanism and Global Quantification of Breast Cancer.
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.
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.
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.
Who cites it
13 citing papers in PubMed, 25 citations in OpenAlex.
- Beyond metaphor: quantitative reconstruction of Waddington landscape and exploration of cellular behavior.Briefings in bioinformatics · 2025Review
- The mathematical exploration for the mechanism of lung adenocarcinoma formation and progression.Briefings in bioinformatics · 2024Article
- A multiscale model of the role of microenvironmental factors in cell segregation and heterogeneity in breast cancer development.PLoS computational biology · 2023Article
- Data mining and mathematical models in cancer prognosis and prediction.Medical review (2021) · 2022Review
- Perspectives on the landscape and flux theory for describing emergent behaviors of the biological systems.Journal of biological physics · 2022Review
- Novel Curcumin Derivative-Decorated Ultralong-Circulating Paclitaxel Nanoparticles: A Novel Delivery System with Superior Anticancer Efficacy and Safety.International journal of nanomedicine · 2022Article
- Quantification of the Landscape for Revealing the Underlying Mechanism of Intestinal-Type Gastric Cancer.Frontiers in oncology · 2022Article
- Data-Driven Modeling of Breast Cancer Tumors Using Boolean Networks.Frontiers in big data · 2021Article
- Uncovering the Underlying Mechanisms of Cancer Metabolism through the Landscapes and Probability Flux Quantifications.iScience · 2020Article
- Quantification of the Underlying Mechanisms and Relationships Among Cancer, Metastasis, and Differentiation and Development.Frontiers in genetics · 2019Article
- Mesenchymal stem cells over-expressing cxcl12 enhance the radioresistance of the small intestine.Cell death & disease · 2018Article
- 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 · 2017Article
- Multiplex Gene Expression Profiling of 16 Target Genes in Neoplastic and Non-Neoplastic Canine Mammary Tissues Using Branched-DNA Assay.International journal of molecular sciences · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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
Identifiers
What OpenQuestion holds
Registered trials
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.