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A multivariate regression approach for identification of SNPs importance in prostate cancer

ABSTRACT Nowadays, it is well-known that there are several genetic alterations that can be employed as genetic markers of prostate cancer (PCa). The use of single nucleotide polymorphism (SNP) is one of the most promising areas of research in cancer investigation. The aim of the present research is to study the influence of the pathways with the help of models such as recursive partitioning method, to detect the SNP of relevance, and consequently the detection of PCa. Data are retrieved from MCC-Spain database, selecting cases and controls as a heterogeneous group. Recursive partitioning method decision trees allow to prune off the splits that are supposed to be not of interest. Then, with the selected pathways, multivariate adaptive regression spline models are trained, and its performance is assessed in terms of the Area Under Curve (AUC) of the Receiver Operating Characteristics (ROC) curve. As a result, with performance tests for researchers that work with genetic datasets, a dimensional reduction and tuning of the parameters for the models are determined. In the case of our research, a total of 12 SNPs were found as the most relevant of the above-mentioned database for the PCa detection.