Gastric adenocarcinoma is a molecularly and histologically heterogeneous neoplasm with a predictively disastrous outcome if undiagnosed at early stages. Amidst global declines of gastric cancer rates, it remains the 5th most common malignancy with the 4th worst outcome of all cancers. Predictive and prognostic clinical biomarkers are scarce in gastric cancer due to its immense heterogeneity. Identifying novel biomarkers for gastric cancer is an emerging field. Previously in our lab, a 20 gene mRNA signature was developed which successfully stratified gastric cancer patients into poor and good prognosis. In this thesis, we attempted to shorten this list to a five gene signature which will successfully stratify patients into similar clusters as the 20 genes. The 5 genes being, HEYL, CALD1, ACTA2, TAGLN and TPM2. Moreover, we validated the efficacy of our 5-gene signature to stratify patients based on their prognoses in silico.
The second half of the thesis focuses on identifying a set of gastric tissue specific reference genes by analyzing high-throughput gene expression data. Most commonly used reference genes are known to have varying expression in cancer tissue which often leads to an issue with reproducibility in cancer research. We aimed to design an algorithm which identifies a list of stable transcripts within a particular tissue type. We identified 3 genes EWSR1, SF1 and HNRNPK which showed stable expression throughout gastric tissue, both in normal and cancer in silico. Ex vivo validation experiments using gastric tumor and adjacent normal RNA show promise for the efficacy of our genes compared to GAPDH and B2M.