ArticlePloS one2016
Serum uPAR as Biomarker in Breast Cancer Recurrence: A Mathematical Model.
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 15 papers.
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Who cites it
15 citing papers in PubMed, 26 citations in OpenAlex.
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- uPAR: An Essential Factor for Tumor Development.Journal of Cancer · 2021Review
- Identification of ZG16B as a prognostic biomarker in breast cancer.Open medicine (Warsaw, Poland) · 2021Article
- Data Driven Mathematical Model of Colon Cancer Progression.Journal of clinical medicine · 2020Article
- How to schedule VEGF and PD-1 inhibitors in combination cancer therapy?BMC systems biology · 2019Article
- Modeling combination therapy for breast cancer with BET and immune checkpoint inhibitors.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
- Improving cancer detection through combinations of cancer and immune biomarkers: a modelling approach.Journal of translational medicine · 2018Article
- Targeting pediatric sarcoma with a bispecific ligand immunotoxin targeting urokinase and epidermal growth factor receptors.Oncotarget · 2018Article
- Dual-targeting Wnt and uPA receptors using peptide conjugated ultra-small nanoparticle drug carriers inhibited cancer stem-cell phenotype in chemo-resistant breast cancer.Biomaterials · 2018Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
There are currently over 2.5 million breast cancer survivors in the United States and, according to the American Cancer Society, 10 to 20 percent of these women will develop recurrent breast cancer. Early detection of recurrence can avoid unnecessary radical treatment. However, self-examination or mammography screening may not discover a recurring cancer if the number of surviving cancer cells is small, while biopsy is too invasive and cannot be frequently repeated. It is therefore important to identify non-invasive biomarkers that can detect early recurrence. The present paper develops a mathematical model of cancer recurrence. The model, based on a system of partial differential equations, focuses on tissue biomarkers that include the plasminogen system. Among them, only uPAR is known to have significant correlation to its concentration in serum and could therefore be a good candidate for serum biomarker. The model includes uPAR and other associated cytokines and cells. It is assumed that the residual cancer cells that survived primary cancer therapy are concentrated in the same location within a region with a very small diameter. Model simulations establish a quantitative relation between the diameter of the growing cancer and the total uPAR mass in the cancer. This relation is used to identify uPAR as a potential serum biomarker for breast cancer recurrence.
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