Machine learning developed immune-related exosome signature for prognosis and immunotherapy benefit in bladder cancer.

ABSTRACT:

Background

Bladder cancer is one of the most common malignancies with high invasion and poor clinical outcome. Exosomes exert a vital role in tumor development, drug resistance, and immunotherapy response.

Methods

Based on the datasets from TCGA, GSE13507, GSE31684, GSE32984 and GSE48276, immune-related exosome signature (IES) was developed with an integrative analysis procedure containing 10 machine learning methods. To investigate the performance of IES in predicting the immunotherapy benefit, three immunotherapy datasets (GSE91061, GSE78220 and IMvigor210) and several predicting scores were used.

Results

The RSF + Enet (alpha = 0.2) algorithm-based signature was considered as the optimal IES as it had a highest average C-index of 0.75. The IES presented a powerful

SUBMITTER: Luo X 

PROVIDER: S-EPMC12008088 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature