ArticleLangenbeck's archives of surgery2025
Identification of three subtypes of thyroid cancer based on IFN-γ-related genes to reveal their prognostic characteristics.
Article in Langenbeck's archives of surgery, 2025. 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
backgroundThyroid cancer is one of the deadliest malignancies. Increasing evidence suggests that interferon-γ (IFN-γ) plays an important role in anti-tumor immunity and its treatment. However, the effectiveness of classifying, predicting prognosis, and immunotherapy for thyroid cancer based on IFN-γ-related genes has not been discovered.
methodsWe used the Gene Set Enrichment Analysis (GSEA) database to obtain IFN-γ-related genes and classified thyroid cancer patients from The Cancer Genome Atlas (TCGA). We systematically explored the differences among various thyroid cancer subtypes from multiple perspectives, such as Kaplan-Meier survival analysis, tumor mutation analysis, immune analysis, enrichment analysis, and drug sensitivity analysis. Finally, we screened some potential drugs suitable for each population.
resultsThrough clustering analysis, we obtained three thyroid cancer subtypes with different IFN-γ-related gene expression levels. The survival analysis results showed significant survival differences among these three subtypes. In addition, gene mutation analysis in different subtypes found that BRAF, TTN, and TG were the top three genes with the highest mutation frequency in the three subtypes, which may be related to their prognosis. Cluster 1 and cluster 2 were the two subtypes with the greatest difference in immune cell infiltration levels, and the differentially expressed genes were mainly enriched in immune-related biological processes or signaling pathways such as leukocyte-mediated immunity, regulation of T cell activation, and chemokine signaling pathway. Eighteen compounds such as Cyclopamine, Erlotinib, FH535, Imatinib, and A-770,041 were selected as potential therapeutic drugs in this study, and their sensitivity to different subtypes varied.
conclusionBased on bioinformatics analysis, we discovered a new classification method based on IFN-γ genes, which could divide thyroid cancer patients into three populations with significant characteristics. Different populations had different mutation patterns, immune infiltration levels, and candidate therapeutic drugs.
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