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September 5, 2025Frontiers in OncologyOpen Access

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

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Authors

NYNingning YuanJZJinfeng ZhangJYJunhui Yuan

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Overview

Machine learning identifies a 12-gene glycosyltransferase signature in soft tissue sarcoma, suggesting significant immune response implications.

Key Points

  • A 12-gene glycosyltransferase signature effectively distinguishes high- and low-risk soft tissue sarcoma patients.
  • Patients in the high-risk group showed significantly poorer survival outcomes, highlighting critical prognostic differences.
  • Differential gene expression analysis revealed two distinct molecular subtypes of soft tissue sarcoma with unique immunological traits.
  • In vitro experiments confirmed that silencing STT3A significantly suppressed the proliferation and migration of soft tissue sarcoma cells.

Cite This Study

Yuan et al. (2025) studied this question.

synapsesocial.com/papers/68c23a2cb210217d6477f434https://doi.org/10.3389/fonc.2025.1636830
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