Högskolan i Skövde

Cancer is a disease characterized by abnormal proliferation of cells in the body and ranks as the second leading cause of death worldwide. In order to improve cancer patient care, a major focus of cancer research is to discover biomarkers. A biomarker is a biological molecule found in tissues or body fluids and can be used to predict or assess disease states. The aim of this thesis is to develop bioinformatics tools for discovery and evaluation of novel biomarkers from high-throughput datasets.

MicroRNAs (miRNAs) are short non-coding RNAs that function as negative regulators of gene expression. Dysregulation of miRNAs in cancer is frequently reported, making them interesting as biomarker candidates. GenoScan was developed for genome-wide discovery of miRNA-coding genes, as a first step in the identification of novel mi-RNA biomarkers.

High-throughput technologies such as microarrays allow researchers to measure the expression of thousands of genes or miRNAs simultaneously. The Decision Trunk Classifier (DTC) algorithm has been developed to screen datasets from these experiments for biomarker candidates. When applied to a miRNA expression dataset for endometrial cancer (EC) samples vs. controls, a two-marker model with 98 % accuracy was generated. These miRNAs (hsa-miR-183-5p and hsa-miRPlus-C1070) are promising as biomarkers for EC screening.

The miREC database was developed to store gene and miRNA data from curated expression profiling studies of EC, as well as gene-miRNA regulatory connections. Using gene-miRNA interaction networks from miREC, the roles of miRNAs in cancer hallmark acquisition can be clarified. To further support exploratory analysis of expression data, DTC was extended with partial least squares regression models. The resulting PLS-DTC algorithm can be used to gain deeper insights into the perturbation of biological processes and pathways.