A novel machine learning model based on ubiquitin-related gene pairs and clinical features to predict prognosis and treatment effect in colon adenocarcinoma.

ABSTRACT:

Background

Ubiquitin and ubiquitin-like (UB/UBL) conjugations are essential post-translational modifications that contribute to cancer onset and advancement. In colon adenocarcinoma (COAD), nonetheless, the biological role, as well as the clinical value of ubiquitin-related genes (URGs), is unclear. The current study sought to design and verify a ubiquitin-related gene pairs (URGPs)-related prognostic signature for predicting COAD prognoses.

Methods

Using univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression, URGP's predictive signature was discovered. Signatures differentiated high-risk and low-risk patients. ROC and Kaplan-Meier assessed URGPs' signature. Gene set enrichment analysis (GSEA) examined biological nomogram enrichm

SUBMITTER: Liang L 

PROVIDER: S-EPMC9863211 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature