Searching for cancer signatures using data mining techniques

Data mining finds many uses in biotechnology and one of them may be to analyze multi-platform data in order to allow searching for genomic cancer signatures. The importance of the topic arises as nowadays cancer is noted one of the leading causes of deaths in highly developed countries. The goal of this work was to search for colorectal cancer signatures, consisting of somatic mutations, somatic gene copy number alterations (SCNAs) as well as abnormal expression levels. After acquiring mutation, SCNA and expression data from cBioPortal, frequent itemset mining was performed using basket analysis and apriori algorithm. We also performed survival analysis of colorectal cancer patients using the discovered signatures as differentiating factor for Kaplan-Meier curve comparison. Frequent itemset mining returned modifications of genes that can be regarded as potential colorectal cancer signatures or signatures of carcinogenic processes in general. While methods used in the project consisted of use of simple or even basic tools, the results suggest that searching for cancer signatures amidst multi-platform data may be worth developing and improving.