İki boyutlu kümelemeye dayalı analizle göğüs kanseri ifade davranışındaki işlevsel ve zamansal ilişkilerin açığa çıkarılması

The emerging microarray technology has useful applications in cancer research.Reports detailing the expression profiles of various types of cancers have pointed to theutility of this approach in defining cancer classes and subclasses with distinct molecularconfigurations and clinical behaviour. Another crucial application of microarrays tocancer research is the detection and selection of diagnostic marker genes. Thesemolecular markers give valuable additional information about tumour diagnosis,prognosis and therapy development . In the case of breast cancer, estrogen receptor (ER)and progesterone receptor (PR) have been used as prognostic markers in clinicalmanagement of breast cancer patients. Patients with ER negative breast tumors have apoorer prognosis than patients with ER positive tumors. Expression profiling of themammary gland to identify tumor associated genes differentially expressed in breasttumors regarding the presence or absence of ER and PR lead to the identification ofgenes, the function of some of which is unknown. However most of these previousstudies uses clustering methods which focus on the global pattern seen in the expressionprofiles. On the other hand, in this study, we used biclustering, which captures the localexpression changes better to improve the understanding on the molecular mechanismswhich underlie the response to estrogen in breast cancer cells. In addition, functionalannotation enrichment, pathway information, transcriptional factor binding site analysiswere also utilized. The results show that estrogen responsive genes act in groups of genesor the so-called `modules? which follow an order in time. Furthermore the temporal orderis associated with a biologically very meaningful functional order, which is supported bythe transcription factor analysis. The pathway analysis indicates that estrogen isresponsible for eliciting mechanisms required for tumorigenesis. All these results showthat biclustering if very useful in the analysis of time series data and it is capable ofelucidating the underlying functional and temporal relationship between genes bettersince it catches local expression behaviours better than the global ones.Advisor: Yar. Doç. Dr. Özlem Keskin Date: 16/11/2006Co-advisor: Doç. Dr. Attila Gürsoy Date: 16/11/2006Director: Prof. Dr. Süleyman Özekici Date: 16/11/2006