Construction of a glycosylation-related prognostic signature for predicting prognosis, tumor microenvironment, and immune response in soft tissue sarcoma.

ABSTRACT:

Background

Altered glycosylation, one of the most common post-translational protein modifications, plays a critical role in the initiation and progression of soft tissue sarcoma (STS). Dysregulated expression of glycosyltransferases leads to abnormal glycosylation patterns, which may offer valuable insights for prognosis and therapeutic response prediction in STS.

Methods

Transcriptional variants and expression profiles of glycosylation-related genes were analyzed using data from The Cancer Genome Atlas (TCGA). Differential gene expression analysis and non-negative matrix factorization (NMF) were performed to identify STS molecular subtypes. A comprehensive machine learning framework integrating 101 algorithms was applied to construct a glycosyltransferase-based prognostic s

SUBMITTER: Yuan N 

PROVIDER: S-EPMC12436507 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature