Developing a machine learning-based diagnostic strategy to detect early onset of double negative prostate cancer by integrating SEMA3C-associated genomic variations and blood biopsies
The main goal of this research project is to study if a protein named SEMA3C can be a biomarker for early detection of an aggressive and lethal form of prostate cancer, named Double Negative Prostate Cancer (DNPC). To test if SEMA3C is a contributing factor in the progression of DNPC, we will compare SEMA3C level in tissues from patient and healthy individuals. Then, we will study if SEMA3C level changes in parallel to genetic variations, happening in tumors with cancer growth. If successful, the results can help us figure out SEMA3C’s correlation to contributing genetic factors that are involved in DNPC growth and resistance to drugs. This new diagnostic strategy will improve patient care and management and may improve survival of men with one of the worst forms of prostate cancer.