  The Cancer Genome Atlas (TCGA) provides researchers with clinicopathological
data and genomic characterizations of various carcinomas. These data sets
include expression microarrays for genes and microRNAs -- short, non-coding
strands of RNA that downregulate gene expression through RNA interference -- as
well as days_to_death and days_to_last_followup fields for each tumor sample.
Our aim is to develop a software tool that screens TCGA data sets for
genes/miRNAs with functional involvement in specific cancers. Furthermore, our
computational pipeline is intended to produce a set of visualizations, or
profiles, that place our screened outputs in a pathway-centric context. We
accomplish our 'screening' by ranking genes/miRNAs by the correlation of their
expression misregulation with differential patient survival. In other words, if
a gene/miRNA is consistently misregulated in patients with poor survival rates
and, on the other hand, is expressed more 'normally' in patients with longer
survival rates, then it is ranked highly; if its misregulation has no such
correlation with good/bad survival in patients, then its rank is low. Our
pathway profiling pipeline produces several outputs, which allow us to examine
the functional roles played by highly ranked genes discovered by our screening.
Running the OV (ovarian serous cystadenocarcinoma) data set through our
analysis pipeline, we find that several highly ranked pathways and functional
groups of genes (VEGF, Jun, Fos, etc.) have already been shown to play some
part in the development of epithelial ovarian carcinomas. We also observe that
the dysfunction of the Wnt signaling pathway, which regulates cell-fate
specification and progenitor cell differentiation, has a disproportionate
impact on the survival of ovarian cancer patients.
