Gene expression analysis in breast cancer
Mehta, Jai Prakash (2010) Gene expression analysis in breast cancer. PhD thesis, Dublin City University.
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Breast cancer is the most common type of cancer among females, both in incidence and death. As meaningful biological understanding of the disease is confounded by the existence of various molecular groups and sub-groups, the challenge for targeted drug development may lie in understanding the molecular mechanisms of various sub-groups in breast cancer.
An in-house breast cancer gene expression dataset comprising 17 normal and 104 tumour samples was analysed to identify important genes and pathways relevant to various clinical parameters. Our results identified groups of patients with similar expression profiles, the possible biology driving them and the clinical implications. Comparing Normal and Cancer specimens’ gene expression profiles, TP53, along with cell cycle genes, were up-regulated in cancer samples. Embryonic stem cell pathway genes were up-regulated, while fatty acid biosynthesis pathways were down-regulated in tumors vs normal.
The cancer specimens largely clustered with respect to ER status. Meta-analysis was performed on in-house datasets along with five public datasets to identify ER pathway genes. The analysis identified novel genes which had not been previously associated with ER-related pathways in cancer. Nuclear receptor pathways were up-regulated in ER-positive tumors/cell lines. Mining for ESR1-correlated genes across 5897 specimens identified FOXA1, SPDEF, C1ORF34 and GATA3 expression to be highly correlated.
Three sub-clusters were identified among the ER-negative cluster. One represented ERBB2 over-expressing cluster. Additionally two unique groups of patients, with significant differences in survival, previously un-identified by other studies, were identified among the ER-negative cluster; a good prognosis cluster with high expression of Immune response genes; and a bad prognosis cluster with high expression of Ropporin, over-expression of which was also linked to high incidence of relapse in our study. siRNA knockdown of Ropporin (ROPN1 and ROPN1B) in the M14 melanoma cell line impaired cancer cell motility and invasion. Knockdown of ROPN1B in MDA-MB-435s reduced motility. In the first study of its kind our results validated the role of Ropporin in cancer cell motility and invasion.
A list of 162 relapse-associated prognostically-important genes was used to develop a Neural Network back propagation model to predict the clinical outcomes. The model was successful in predicting relapse with 97.8% accuracy and outperformed existing models, indicating a strong possibility of its use as diagnostic model.
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