Evaluating the prognostic potential of telomerase signature in breast cancer through advanced machine learning model.

ABSTRACT:

Background

Breast cancer prognosis remains a significant challenge due to the disease's molecular heterogeneity and complexity. Accurate predictive models are critical for improving patient outcomes and tailoring personalized therapies.

Methods

We developed a Machine Learning-assisted Telomerase Signature (MLTS) by integrating multi-omics data from nine independent breast cancer datasets. Using multiple machine learning algorithms, we identified six telomerase-related genes significantly associated with patient survival. The predictive performance of MLTS was evaluated against 66 existing breast cancer prognostic models across diverse cohorts.

Results

The MLTS demonstrated superior predictive accuracy, stability, and reliability compared to other models. Patients with

SUBMITTER: Guo X 

PROVIDER: S-EPMC11634871 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature