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Mehaisi, D, 2024. In silico modelling of the TP53 pathway in cancers using artificial neural network based systems biology approaches. PhD, Nottingham Trent University.
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Abstract
Cancer, a major health issue and one of the most common causes of death worldwide, arises through a multi-stage process that involves several genetic alterations (pathological, immunological, and physiological). Researchers are continually seeking to explore such alterations at the molecular level to gain knowledge that can be used for disease management and prevention, resulting in several large-scale transcriptomic technologies to estimate whole genome expression profiles for cancer. However, such analytical approaches generate massive volumes of data, which need careful processing to extract meaningful information using statistical and computational approaches. Some of these approaches have been dedicated to studying cancer through interrogation of pathway models based on molecular data and based on mining of the literature corpus to obtain deep insights which could help in drug discovery and the achievement of personalized medicine for cancer. These methods tend to address the dimensionality and complexity issues associated with large-scale technologies by presenting the data using signalling network models and pathway knowledge graphs. However, the possibility of identifying novel interactions and disease drivers remains limited, as most of these approaches are based on knowledge obtained from the literature through manual curation.
ANN-based integrative data mining approaches have been successful in cancer research, coping with noise and dimensionality associated with high throughput data, allowing for the identification of novel interactions and drivers related to diseases. These drivers can be used as a panel for the classification of certain conditions or as targets for new therapeutic interventions.
This project applies ANN approaches for pathway data mining through a series of analyses leading to the identification of key interactions associated with the TP53 pathway in cancer. The first analysis indicates the novel drivers associated with the TP53 pathway in colorectal cancer. The second analysis suggests common and unique predictors associated with the TP53 pathway in the Mutant- and Wild-type status of the TP53 gene using three cohorts: colon and rectum cancer (COADREAD), pancreatic cancer (PAAD), and stomach cancer (STAD) from cases in The Cancer Genome Atlas (TCGA). This analysis also identified a panel of differential drivers associated with theTP53 pathway in the Missense mutation status of the TP53 gene for the investigated cohorts. The study integrates the findings and compares the ANN driver results with the existing pathway analysis tool, MetaCore. The final analysis revealed a panel of differential drivers associated with the TP53 pathway in the Wild-type state of the TP53 gene for the studied cohorts.
| Item Type: | Thesis |
|---|---|
| Creators: | Mehaisi, D. |
| Contributors: | Name Role NTU ID ORCID |
| Date: | March 2024 |
| Rights: | This work is the intellectual property of the author. You may copy up to 5% of this work for private study, or personal, non-commercial research. Any re-use of the information contained within this document should be fully referenced, quoting the author, title, university, degree level and pagination. Queries or requests for any other use, or if a more substantial copy is required, should be directed in the owner(s) of the Intellectual Property Rights. |
| Divisions: | Schools > School of Science and Technology |
| Record created by: | Jeremy Silvester |
| Date Added: | 27 Jun 2024 10:14 |
| Last Modified: | 27 Jun 2024 10:14 |
| URI: | https://irep.ntu.ac.uk/id/eprint/51636 |
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