Prediction of colorectal cancer driver genes from patients’ genome data

Abstract

Colorectal cancer refers to the cancer that occurs in the colon and rectum. It has been established as the third most common cancer and the forth one in causing worldwide mortality. Cancer caused by the mutation of several genes that usually involved in the regulation of cell proliferation, growth and cell death. The mutation that leads to abnormal function of genes, either in enabling the genes to gain or loss of function was termed as driver mutation and the genes with driver mutation ability was termed as driver genes. The identification of driver genes provides insight on mechanistic process of cancer development where this information can be used to further understand their mode of action for causing dysregulation in signaling pathways. In this study, two bioinformatic tools, i.e. CGI and iCAGES were used to predict potential driver genes from the genome of eight colorectal cancer patients with annotated variants datasets. 44 unique driver genes and 21 pathways have been identified; such as p53 signaling, PI3K-AKT, Endocrine resistance, MAPK and cell cycle pathways. The identification of these pathways can lead to the identification of potential drugs targeting these pathways.