Cancer is a condition that is demanding more research with new cases being reported each year. In this thesis the aim was to investigate the behaviour of a embryonic stem cell factor and its partners in various types of cancers. The embryonic stem cell factor under study in this thesis is responsible for the maintenance of pleuripotency in stem cells and its interaction partners maintain the self-renewal ability of the embryonic stem cells. With the discovery of cancer stem cells and detections of stem cell like gene signatures from cancers, it becomes important to address the issue to identify the responsible genes. The embryonic stem cell factor of our interest when knocked down in cell line studies showed downregula- tion of stem cell pleuripotency factors therefore, we believe it may be playing a key role in cancer tissues where it is expressed.

We use gene expression analysis of microarray data of cancer patient samples along with the available survival information to test whether the gene and its partners have any ef- fect on survival. We use correlation measures to establish that partners of the embryonic stem cell factor of our interest might be co-expressed in patient samples. In particular, we were able to identify colon cancer and seminoma samples that express our gene of interest at high levels. We used T-test and ROTS (Reproducibility Optimized Test Statistic) on these datasets to detect which genes are differentially expressed.

The project also presents a different approach to microarray data analysis where the focus is not on the disease or condition but a set of genes are central theme of the study, and the research is done to find the cancer or datasets where the gene set is perturbed. This is de- sired under situations similar to the premise of this project, that if an embryonic stem cell factor is expressed in adult tissues it is a sign of problems.

The project suggests that the embryonic stem cell factor under question alone cannot be held responsible for poor survival of the cancer patients, instead it seems that it is a pro- survival factor after all. But further analyses are being done in this area to uncover more in- formation and also to find factors that can explain the poor survival of the samples for the cancer datasets under study.