A Gene-Based Machine Learning Classifier Associated to the Colorectal Adenoma-Carcinoma Sequence.

ABSTRACT: Colorectal cancer (CRC) carcinogenesis is generally the result of the sequential mutation and deletion of various genes; this is known as the normal mucosa-adenoma-carcinoma sequence. The aim of this study was to develop a predictor-classifier during the "adenoma-carcinoma" sequence using microarray gene expression profiles of primary CRC, adenoma, and normal colon epithelial tissues. Four gene expression profiles from the Gene Expression Omnibus database, containing 465 samples (105 normal, 155 adenoma, and 205 CRC), were preprocessed to identify differentially expressed genes (DEGs) between adenoma tissue and primary CRC. The feature selection procedure, using the sequential Boruta algorithm and Stepwise Regression, determined 56 highly important genes. K-Means methods showed that, using

SUBMITTER: Lacalamita A 

PROVIDER: S-EPMC8698794 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature