Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1061086 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR27420448_1.fastq.gz | Fastqsanger.gz | |||
| SRR27420448_2.fastq.gz | Fastqsanger.gz |
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Project description:The paper describes a model on the detection of cancer based on cancer and immune biomarkers.
Created by COPASI 4.25 (Build 207)
This model is described in the article:
Improving cancer detection through combinations of cancer and immune biomarkers: a modelling approach
Raluca Eftimie and and Esraa Hassanein
J Transl Med (2018) 16:73
Abstract:
Background: Early cancer diagnosis is one of the most important challenges of cancer research, since in many can- cers it can lead to cure for patients with early stage diseases. For epithelial ovarian cancer (which is the leading cause of death among gynaecologic malignancies) the classical detection approach is based on measurements of CA-125 biomarker. However, the poor sensitivity and specificity of this biomarker impacts the detection of early-stage cancers.
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Results: We show that this combined biomarkers approach could lead indeed to earlier cancer detection. However, the immune response (which influences the level of secreted IL-7 biomarker) plays an important role in improving and/or delaying cancer detection. Moreover, the detection level of IL-7 immune biomarker could be in a range that would not allow to distinguish between a healthy state and a cancerous state. In this case, the construction of solu- tion diagrams in the space generated by the IL-7 and CA-125 biomarkers could allow us predict the long-term evolu- tion of cancer biomarkers, thus allowing us to make predictions on cancer detection times.
Conclusions: Combining cancer and immune biomarkers could improve cancer detection times, and any predic- tions that could be made (at least through the use of CA-125/IL-7 biomarkers) are patient specific.
Keywords: Ovarian cancer, Mathematical model, CA-125 biomarker, IL-7 biomarker, Cancer detection times
This model is hosted on BioModels Database and identified by: MODEL1907050002.
To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide.
Please refer to CC0 Public Domain Dedication for more information.
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