A basement membrane-related signature for prognosis and immunotherapy benefit in bladder cancer based on machine learning.

ABSTRACT:

Background

Bladder cancer has a poor clinical outcome because of its high aggressiveness. Basement membrane plays vital functions in tumor invasion and migration. Invasion and distant metastasis of cancer are facilitated by degradation of the basement membrane and extracellular matrix.

Methods

Ten machine learning methods were utilized to develop the basement membrane-related signature (MRS) using datasets from TCGA, GSE13507, GSE31684, GSE32984 and GSE48276. Three anti-PD1 or anti-CTLA4 datasets and several predicting scores were used to investigate the performance of MRS in predicting the immunotherapy benefits.

Results

A predicting model based on the Enet algorithm (alpha = 0.1) was chosen as the optimal MRS since it had the highest average C-index being 0.72. Acco

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC11464978 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature