Development of a tertiary lymphoid structure-based prognostic model for breast cancer: integrating single-cell sequencing and machine learning to enhance patient outcomes.

ABSTRACT:

Background

Breast cancer, a highly prevalent global cancer, poses significant challenges, especially in advanced stages. Prognostic models are crucial to enhance patient outcomes. Tertiary lymphoid structures (TLS) within the tumor microenvironment have been associated with better prognostic outcomes.

Methods

We analyzed data from 13 independent breast cancer cohorts, totaling over 9,551 patients. Using single-cell RNA sequencing and machine learning algorithms, we identified critical TLS-associated genes and developed a TLS-based predictive model. This model stratified patients into high and low-risk groups. Genomic alterations, immune infiltration, and cellular interactions within the tumor microenvironment were assessed.

Results

The TLS-based model demonstrated sup

SUBMITTER: Zhang X 

PROVIDER: S-EPMC11897234 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature