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Transcriptome Pattern Recognition in Pre-Cancer Cells


Project Description

Background:
All cellular processes are tightly regulated by a complex network of interacting biomolecules. Given that mRNA transcription precedes protein translation, change in gene expression levels often precedes visible pathological manifestation. Hence, transcriptome instability in the form of gene expression alterations serves as key signals for subsequent disease initiation and manifestation. The ability to recognise and measure cancer-associated transcriptome instability could enable better understanding of cancer initiation and smarter way to predict cancer risk 1,2 in otherwise asymptomatic patients. Outcome of this study could be translated into a clinically useful tool for risk prediction before disease manifestation.

Aims:
The PhD project aims to investigate and identify transcriptome patterns using artificial intelligence regulated by key candidate genes.

Research training facilities and environment:
The project will involve interdisciplinary approaches involving cell and molecular techniques for gene transcription data generation, bioinformatics meta-analysis of published transcriptome data for biomarkers discovery and mathematical artificial intelligence (AI) methods for pattern recognition.

The PhD student will be exposed to clinical environment at our new Institute of Dentistry and perform his/her research in a £45 million state-of-the-art laboratory, the Blizard Institute, which hosts eight multidisciplinary research centres. This, together with interaction with the School of Mathematical Sciences at Mile End campuses enables novel and exciting cross-disciplines research.

Person specification:
A graduate with at least an upper second class honours degree or a distinction in an experimental MSc degree is required for this PhD project. The candidate should have strong interests and preferably with some experience in molecular pathology, clinical chemistry, bioinformatics and big-data statistical analysis.

How to apply:
For more information regarding the project, please contact Dr Muy-Teck Teh ()

Applications should be submitted through the Queen Mary application system. Please indicate the project title and supervisor in the ‘Research Degree Programmes - Additional Questions’ section of the application.

Alongside the application form, please send the following supporting documents:
• Curriculum Vitae (CV)
• Copies of your degree certificates with transcripts
• Proof of English language ability for overseas applicants from non-English speaking countries
• A one-side A4 statement of purpose. This should set out your previous academic or other experience relevant to the proposed research; why you wish to undertake this research at QMUL; your previous research or professional training and what further training you think you will need to complete a PhD; and what ethical issues you will need to consider in undertaking this research.
• Two references. At least one reference must be from an academic referee who is in a position to comment on the standard of your academic work and suitability for postgraduate level study. Where appropriate, a second referee can provide comment on your professional experience.

Please contact Charlotte Royle () with any queries about the application process.

Funding Notes

We will consider applications from prospective students with a source of funding to cover tuition fees and bench fees for three years full-time or 6 years part-time. Both self-funded and sponsored students will be considered.

UK and EU nationality self-funded students might be eligible for both the cost of tuition fees and a yearly stipend over the course of the PhD programme from the Student Finance England: View Website

References

1 Ma, H. et al. Independent evaluation of a FOXM1-based quantitative malignancy diagnostic system (qMIDS) on head and neck squamous cell carcinomas. Oncotarget 7, 54555-54563, doi:10.18632/oncotarget.10512 (2016).
2 Teh, M. T. et al. Exploiting FOXM1-orchestrated molecular network for early squamous cell carcinoma diagnosis and prognosis. Int. J. Cancer 132, 2095-2106, doi:10.1002/ijc.27886 (2013).

How good is research at Queen Mary University of London in Allied Health Professions, Dentistry, Nursing and Pharmacy?

FTE Category A staff submitted: 32.20

Research output data provided by the Research Excellence Framework (REF)

Click here to see the results for all UK universities

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