Exploring Immune-Related Ferroptosis Genes in Thyroid Cancer: A Comprehensive Analysis

<b>Background</b>: The increasing incidence and poor outcomes of recurrent thyroid cancer highlight the need for innovative therapies. Ferroptosis, a regulated cell death process linked to the tumour microenvironment (TME), offers a promising antitumour strategy. This study explored immune-related ferroptosis genes (IRFGs) in thyroid cancer to uncover novel therapeutic targets. <b>Methods</b>: CIBERSORTx and WGCNA were applied to data from TCGA-THCA to identify hub genes. A prognostic model composed of IRFGs was constructed using LASSO Cox regression. Pearson correlation was employed to analyse the relationships between IRFGs and immune features. Single-cell RNA sequencing (scRNA-seq) revealed gene expression in cell subsets, and qRT-PCR was used for validation. <b>Results</b>: Twelve IRFGs were identified through WGCNA, leading to the classification of thyroid cancer samples into three distinct subtypes. There were significant differences in patient outcomes among these subtypes. A prognostic risk score model was developed based on six key IRFGs (<i>ACSL5</i>, <i>HSD17B11</i>, <i>CCL5</i>, <i>NCF2</i>, <i>PSME1</i>, and <i>ACTB</i>), which were found to be closely associated with immune cell infiltration and immune responses within the TME. The prognostic risk score was identified as a risk factor for thyroid cancer outcomes (HR = 14.737, 95% CI = 1.95-111.65; <i>p</i> = 0.009). ScRNA-seq revealed the predominant expression of these genes in myeloid cells, with differential expression validated using qRT-PCR in thyroid tumour and normal tissues. <b>Conclusions</b>: This study integrates bulk and single-cell RNA sequencing data to identify IRFGs and construct a robust prognostic model, offering new therapeutic targets and improving prognostic evaluation for thyroid cancer patients.