Analysis of Cancer-Associated Mutations of POLB Using Machine Learning and Bioinformatics

DNA damage is a critical factor in the onset and progression of cancer. When DNA is damaged, the number of genetic mutations increases, making it necessary to activate DNA repair mechanisms. A crucial factor in the base excision repair process, which helps maintain the stability of the genome, is an enzyme called DNA polymerase <inline-formula><tex-math notation="LaTeX">$\boldsymbol{\beta}$</tex-math><alternatives><mml:math><mml:mi>β</mml:mi></mml:math><inline-graphic xlink:href="maalouf-ieq1-3395777.gif"/></alternatives></inline-formula> (Pol<inline-formula><tex-math notation="LaTeX">$\boldsymbol{\beta}$</tex-math><alternatives><mml:math><mml:mi>β</mml:mi></mml:math><inline-graphic xlink:href="maalouf-ieq2-3395777.gif"/></alternatives></inline-formula>) encoded by the POLB gene. It plays a vital role in the repair of damaged DNA. Additionally, variations known as Single Nucleotide Polymorphisms (SNPs) in the POLB gene can potentially affect the ability to repair DNA. This study uses bioinformatics tools that extract important features from SNPs to construct a feature matrix, which is then used in combination with machine learning algorithms to predict the likelihood of developing cancer associated with a specific mutation. Eight different machine learning algorithms were used to investigate the relationship between POLB gene variations and their potential role in cancer onset. This study not only highlights the complex link between POLB gene SNPs and cancer, but also underscores the effectiveness of machine learning approaches in genomic studies, paving the way for advanced predictive models in genetic and cancer research.