Integrative transcriptomic profiling and machine learning reveal hypoxia-associated molecular signatures for precision diagnosis in thyroid eye disease.

ABSTRACT:

Background

Thyroid eye disease (TED) is an autoimmune disorder characterized by persistent inflammation around the periphery and within the orbit, potentially driven by hypoxic conditions. Effective biomarkers and precise predictive models are still lacking for the early diagnosis of TED.

Methods

Bulk RNA sequencing was conducted on peripheral blood samples from TED patients, Graves' hyperthyroidism (GH) patients without ocular involvement, and healthy controls (HC). Differentially expressed genes between TED and HC, hypoxia-related genes and genes identified through weighted gene co-expression network analysis (WGCNA) were intersected to identify candidate biomarkers. Subsequently, nine machine learning algorithms were applied to screen for critical hypoxia-related TED diag

SUBMITTER: Qian W 

PROVIDER: S-EPMC12642099 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature