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

## TL;DR

This study identifies immune-related ferroptosis genes in thyroid cancer and builds a prognostic model to improve patient outcomes.

## Contribution

A novel prognostic model based on six immune-related ferroptosis genes for thyroid cancer prognosis and treatment.

## Key findings

- Twelve IRFGs were identified, classifying thyroid cancer into three subtypes with different outcomes.
- A six-gene risk score model was strongly associated with immune cell infiltration and patient survival.
- ScRNA-seq showed these genes are mainly expressed in myeloid cells and validated by qRT–PCR.

## Abstract

Background: 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. Methods: 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. Results: 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 (ACSL5, HSD17B11, CCL5, NCF2, PSME1, and ACTB), 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; p = 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. Conclusions: 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.