ArticleFrontiers in genetics2026
Comprehensive analyses of biological function and tumor microenvironment with cuproptosis regulators and construction of a cuproptosis-related scoring system in thyroid cancer based on bioinformatics and experimental validation.
Article in Frontiers in genetics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: The role of copper-induced cell death, termed cuproptosis, has been demonstrated recently. Nonetheless, the potential biological function of cuproptosis regulators in thyroid cancer (THCA) remains unknown. Method: We analyzed the expression levels and prognostic values of cuproptosis regulators in THCA. We used weighted gene co-expression network analysis (WGCNA) and single-sample gene set enrichment analysis (ssGSEA) to perform biological function analysis. We used a sliding windows sequential forward feature selection (SWSFS) method to construct a cuproptosis-related score (RS) to predict progression- and disease-free survival probability. We validated the expression level of cuproptosis-related genes and explored the biological function of FDX1. Results: FDX1 was a protective factor for THCA and possessed a higher expression level in thyroid cancer. WGCNA and ssGSEA analysis showed that several pathways, such as protein secretion, oxidative phosphorylation, MYC, MTORC1, DNA repair, and adipogenesis, were highly positively correlated with cuproptosis regulators. In contrast, some immune-related pathways, such as interferon response and inflammatory pathways, were negatively correlated. We selected intersection genes by correlation analysis between the expression level of FDX1 and the prediction inhibitory concentration (IC50). Stratified analysis and nomogram were also employed to verify the validity and accessibility of the signature. Correlation analysis suggested that FDX1 expression was associated with immune cell infiltration patterns in THCA based on Conclusion: Overall, our study identified a novel cuproptosis-based predictive model, and we demonstrated that cuproptosis is a promising therapeutic method for THCA, which enhances our understanding of the cuproptosis-related genes and provides valuable insights into the clinical treatment and molecular mechanisms of THCA.
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