Use of Personalized Biomarkers in Metastatic Colorectal Cancer and the Impact of AI

SIMPLE SUMMARY: Colorectal cancer is one of the most frequent cancers worldwide, with a high incidence and mortality. Although many treatment options are available for metastatic disease, patient survival is still limited. The molecular classification of colorectal cancer proposed in 2015 has helped us to better understand colorectal cancer and realize a more effective implementation of therapeutic sequences. It has also been observed that the existing mutational landscape is closely correlated with the epigenetics of colorectal cancer. The identification of prognostic and predictive biomarkers in this context becomes a necessity closely related to therapeutics, and artificial intelligence can be used to discover new biomarkers. ABSTRACT: Colorectal cancer is a major cause of cancer-related death worldwide and is correlated with genetic and epigenetic alterations in the colonic epithelium. Genetic changes play a major role in the pathophysiology of colorectal cancer through the development of gene mutations, but recent research has shown an important role for epigenetic alterations. In this review, we try to describe the current knowledge about epigenetic alterations, including DNA methylation and histone modifications, as well as the role of non-coding RNAs as epigenetic regulators and the prognostic and predictive biomarkers in metastatic colorectal disease that can allow increases in the effectiveness of treatments. Additionally, the intestinal microbiota’s composition can be an important biomarker for the response to strategies based on the immunotherapy of CRC. The identification of biomarkers in mCRC can be enhanced by developing artificial intelligence programs. We present the actual models that implement AI technology as a bridge connecting ncRNAs with tumors and conducted some experiments to improve the quality of the model used as well as the speed of the model that provides answers to users. In order to carry out this task, we implemented six algorithms: the naive Bayes classifier, the random forest classifier, the decision tree classifier, gradient boosted trees, logistic regression and SVM.