Integrative analysis of ceRNA networks and immune cell infiltration in thyroid cancer for enhanced diagnostic and prognostic insights.

Thyroid cancer, the most common endocrine malignancy, has seen a significant rise in incidence, necessitating improved diagnostic and prognostic methods. Despite advancements in fine-needle aspiration biopsy (FNAB) and molecular mutation detection, these techniques have limitations, particularly in large nodules. This study aims to identify molecular markers and construct a comprehensive ceRNA regulatory network to enhance thyroid cancer diagnosis and prognosis. Using transcriptomic data from TCGA, GTEx, and GEO datasets, we performed differential expression analysis and WGCNA to identify key lncRNAs, miRNAs, and mRNAs involved in thyroid cancer. Gene Ontology and KEGG pathway analyses elucidated the biological functions and pathways of these genes. A ceRNA network was constructed, highlighting the interactions between 32 lncRNAs, 18 miRNAs, and 56 mRNAs. Survival analysis and the Cibersort algorithm further revealed the relationship between ceRNA regulatory networks and immune cell infiltration. A prognostic risk model was developed, incorporating key prognostic genes (PRR15, HCP5, and DUXAP8) and immune cells (resting NK cells, monocytes, M0 macrophages, and activated mast cells). DUXAP8 was positively correlated with activated mast cells and monocytes, while HCP5 was negatively correlated with resting NK cells. This study provides new insights into thyroid cancer pathogenesis, suggesting potential molecular markers for early diagnosis and personalized treatment. Integrating ceRNA regulatory mechanisms with immune cell analysis offers a novel perspective on the tumor microenvironment's role in thyroid cancer progression.