Transcriptomic profile based cancer disease prediction and patient survival time differentiation

[摘要] ENGLISH ABSTRACT : Cancer disease is an abnormal growth of cells, which may be caused by mutations in genes which, as a result, alter the way cells function mainly in theway they grow and divide. Cancer cells are regulated by complex interactionsmediated by a group of proteins and miRNAs which are expressed and repressed. With the help of transcriptomic technologies such as RNA–sequencing(RNA–seq), it is now possible to profile thousands of genes at once to createa global picture of the functions of cells. Here, the study employs a statisticalapproach, called Significance Analysis of Microarray (SAM), to identify genesthat are differentially expressed in breast cancer patients. Genes with scoresgreater than a threshold are deemed potentially significant. Genes identified assignificantly different are used for twofold reasons. First, the study uses thesesignificantly identified genes to predict breast cancer using three machine learning algorithms. The machine learning algorithms used are random forests, artificial neural networks and support vector machines. Secondly, clinical detailsof patients and significantly identified genes are combined to build a survivalmodel to predict the probability of survival and risk to the event in breast cancer patients. Using The Cancer Genome Atlas (TCGA) as the primary data for the study, SAM reported 23 genes as significantly different. Further investigations revealed that these 23 significant genes are involved in tumour suppression, angiogenesis, cell growth factor, tumourigenesis, cell proliferation, tumourprogression and tumour necrosis activities. In predicting breast cancer, 10 outof the 23 genes contribute significantly to the model. Finally, it was identifiedthat log–logistic distribution best describes the survival time of breast cancer patients. Moreover, the survival model revealed that expression levels of six genesinfluence the survival probability of a breast cancer patient.

[发布机构] Stellenbosch University