In the UK more than 16,000 people died of colorectal cancer in 2007. More than 200,000 people died of colorectal cancer in the whole of Europe. Better understanding of the molecular mechanisms and signalling pathways of initiation and progression of colorectal cancer would facilitate discovery of new prognostic markers and therapeutic targets. Genome-wide gene expression profiling analysis using microarray technology is one of the most effective approaches to understand these key molecular events. The main purpose of the study was to identify consensus gene expression modules that were common to many microarray datasets together with their biological relevance. The results of this study show that I have identified gene expression modules with distinct biological role that occur regularly across many datasets To prove my findings conclusively , I successfully mapped them with other studies.
The genes from module 1 identified as associated with Focal adhesion pathway, separated the GSE 3494 breast cancer dataset which has 235 samples into three classes on the basis of expression levels, i.e., over expression (class 1) has 94 samples, and under expression (class 2) has 94 samples and unclassified (at p–value < 0.05) 47 samples. The plot shows the class 1 which is the overexpressed class has 29 deaths and the underexpressed class has 17 deaths. The blue curve shows the probability of survival for class 1 which is low and the black curve for probability of survival which is higher at p-value =0.0168.
The findings can be applied in other diseases areas Combine with differential expression between normal tissues and CRC tissues
Identification of Gene Expression Modules in Colorectal Cancer September 08, 2009 Manikhandan A V Mudaliar MSc Thesis Supervisors: Dr. Daniel Crowther and Dr. Keith Vass
Kaplan-Meier Survival Analysis Plot showing probability of tumour relapse over time: predictability using MCODE cluster No.8 (RAS signalling pathway) as classifier on Wang et al. 3 Colorectal Cancer dataset
No of samples = 74 Class Samples Relapse Cluster 1 42 15 Cluster 2 32 16 p-value = 0.3817 Red curve = Cluster 1 (under expressed) Blue curve = Cluster 2 (over expressed)
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