Machine Learning Integration Framework Constructs a Lactylation-Associated Gene Signature to Improve Prognosis in Bladder Cancer.

ABSTRACT:

Background

Bladder cancer remains a significant challenge in oncology owing to its high recurrence rates and limited treatment options, particularly in cases of resistance to standard therapies.

Aims

Our study aimed to pinpoint a lactylation-associated gene signature capable of predicting prognosis and providing important theoretical support for drug development and precision therapy in bladder cancer patients.

Materials and methods

Leveraging RNA sequencing data from the TCGA and GEO databases, we scrutinized the expression profiles of lactylation-associated genes and pinpointed a signature comprising eight genes strongly linked to prognosis based on a machine learning integrative framework. Our prognostic model, incorporating the expression levels of these lactylati

SUBMITTER: Wang J 

PROVIDER: S-EPMC12782153 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature