Identification of Potential Biomarkers and Immune Infiltration Characteristics in Ulcerative Colitis by Combining Results from Two Machine Learning Algorithms.

ABSTRACT:

Objective

This study was designed to identify potential biomarkers for ulcerative colitis (UC) and analyze the immune infiltration characteristics in UC.

Methods

Datasets containing human UC and normal control tissues (GSE87466, GSE107597, and GSE13367) were downloaded from the GEO database. Then, the GSE87466 and GSE107597 datasets were merged, and the differentially expressed genes (DEGs) between UC and normal control tissues were screened out by the "limma R" package. The LASSO regression model and support vector machine recursive feature elimination (SVM-RFE) were performed to screen out the best biomarkers. The GSE13367 dataset was used as a validation cohort, and the receiver operating characteristic curve (ROC) was used to evaluate the diagnostic performance. Finally,

SUBMITTER: Bu M 

PROVIDER: S-EPMC9359832 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature