Screening telomere-related genes to predict prognosis, immunotherapy response, and drug sensitivity in esophageal cancer using a machine learning approach.

ABSTRACT:

Background

Esophageal cancer (EC) is a common malignancy with poor prognosis. Telomeres, composed of repetitive DNA sequences and shelterin complexes, play important roles in tumor biology. However, the prognostic value of telomere-related genes (TRGs) in EC remains unclear.

Methods

TRGs were obtained from TelNet, and transcriptomic and clinical data were collected from The Cancer Genome Atlas (TCGA). Prognostic TRGs were identified using multivariate Cox regression analysis, Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest (RF), and Support Vector Machine (SVM) algorithms to construct a risk model. Model performance was evaluated by Kaplan-Meier(K-M) and Receiver Operating Characteristic (ROC) analyses, and a nomogram integrating clinical variables was

SUBMITTER: Li S 

PROVIDER: S-EPMC12676889 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature