Dr. Luis RuedaSchool of Computer Sciences, University of Windsor
Transcriptomic analysis of prostate cancer using RNA-sequence data
Co-Investigators and Collaborators:
EVIDENCE OF PROGRESSWe have developed a computational model used to detect differential expression of genes (i.e. transcripts) in benign and malignant prostate tumours, as well as in prostate cancer progression. The computational model uses machine learning techniques for classification, which simulates a diagnosis system using only a few transcripts as drivers. The model has been tested on datasets generated using the latest RNA sequencing technologies, obtaining a perfect prediction accuracy. As a result, we have identified a very small subset of transcripts as drivers of prostate cancer and progression, which have been validated using human protein data repositories. Proteins associated with three genes have been categorized as having moderate to strong relationships to prostate cancer and weak relationship to noncancerous tissue. We have identified 44 transcripts correlated to tumour progression, which are currently being investigated using previously reported studies in the literature.
Our aim for the third year is to validate the drivers we identified through biochemical analysis on prostate cancer cell lines. In addition, we are currently deploying a set of integrative computational tools that will be used to simulate the next steps in the molecular biology of the cells, involving potential proteins and protein variants whose functions are involved in cellular processes associated with prostate cancer and progression. We aim to identify a more robust set of drivers of prostate cancer progression that can be used as potential targets for diagnosis, drug development, therapeutic procedures and clinical follow up.