The nature of statistical learning theory
The nature of statistical learning theory
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Gene expression profiling offers a great opportunity for understanding the key role of genes in alterations which drive a normal cell to a cancer state. A deep understanding of the mechanisms of tumorigenesis can be reached focusing on deregulation of gene sets or pathways. We measure the amount of deregulation and assess the statistical significance of predefined pathways belonging to MSigDB collection in a colon cancer data set. To measure the relevance of the pathways we use two well-established methods: Gene Set Enrichment Analysis (GSEA) [7] and Gene List Analysis with Prediction Accuracy (GLAPA) [8]. We found that pathways associated to different diseases are strictly connected with colon cancer. Our study highlights the importance of using gene sets genes for understanding the main biological processes and pathways involved in colorectal cancer. Our analysis shows that many of the genes involved in these pathways are strongly associated to colorectal tumorigenesis.