Identification of the immune-related diagnostic biomarkers between Graves' disease and thyroid carcinoma based on comprehensive bioinformatics analysis and machine learning

Increasing evidence suggests that Graves' disease (GD) may increase the risk of thyroid cancer (THCA), but diagnostic biomarkers associated with it remain underexplored. To address this issue, we analyzed the Gene Expression Omnibus (GEO) and TCGA (The Cancer Genome Atlas) databases, identified 21 shared immune-related genes via differential expression analysis and weighted gene coexpression network analysis (WGCNA). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses emphasized immune pathways, and LASSO regression was used to select five core genes (TREM1/S100A11/MRPS16/MET/ACTN1) to construct robust diagnostic models. The CIBERSORT algorithm revealed a significant correlation between the models and immune infiltration of the THCA. Machine learning and protein‒protein interaction (PPI) networks revealed TREM1 as a central gene for predicting the response to immunotherapy. Xenograft tumor models confirmed that TREM1 knockdown suppressed the proliferative capacity of thyroid cancer cells in vivo. Drug sensitivity studies identified VER-155008 as a potential therapeutic compound. Bioinformatics and experimental validation (qRT‒PCR) revealed that the HOTTIP/hsa-miR-204-3p/TREM1 axis serves as a ceRNA to regulate TREM1. Our study identified five core genes, with TREM1 as a central regulator, that demonstrate strong diagnostic potential for both Graves' disease (GD) and thyroid carcinoma (THCA). These findings provide valuable diagnostic biomarkers and therapeutic targets for THCA patients with GD.