Metastatic prostate cancer is one of the most common cancers in men. In the
advanced stages of prostate cancer, tumours can metastasise to other tissues in
the body, which is fatal. In this thesis, we performed a genetic analysis of
prostate cancer tumours at different metastatic sites using data science,
machine learning and topological network analysis methods. We presented a
general procedure for pre-processing gene expression datasets and pre-filtering
significant genes by analytical methods. We then used machine learning models
for further key gene filtering and secondary site tumour classification.
Finally, we performed gene co-expression network analysis and community
detection on samples from different prostate cancer secondary site types. In
this work, 13 of the 14,379 genes were selected as the most metastatic prostate
cancer related genes, achieving approximately 92% accuracy under
cross-validation. In addition, we provide preliminary insights into the
co-expression patterns of genes in gene co-expression networks. Project code is
available at https://github.com/zcablii/Master_cancer_project