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

Area of Science:

• Oncology
• Immunology
• Genetics

Background:

• Recurrent thyroid cancer presents significant challenges due to increasing incidence and poor prognoses.
• Ferroptosis, a form of regulated cell death influenced by the tumor microenvironment (TME), offers a potential anti-cancer strategy.
• Identifying novel therapeutic targets is crucial for improving thyroid cancer treatment outcomes.

Purpose of the Study:

• To explore immune-related ferroptosis genes (IRFGs) in thyroid cancer.
• To identify novel therapeutic targets and develop a prognostic model for thyroid cancer.
• To understand the role of IRFGs in the tumor microenvironment and immune responses.

Main Methods:

• Utilized CIBERSORTx and Weighted Gene Co-expression Network Analysis (WGCNA) on TCGA-THCA data to identify hub genes.
• Constructed a prognostic model using LASSO Cox regression and analyzed IRFG relationships with immune features via Pearson correlation.
• Employed single-cell RNA sequencing (scRNA-seq) for gene expression analysis in cell subsets and qRT-PCR for validation.

Main Results:

• Identified twelve IRFGs, classifying thyroid cancer into three distinct subtypes with significant outcome differences.
• Developed a prognostic risk score model based on six key IRFGs (ACSL5, HSD17B11, CCL5, NCF2, PSME1, ACTB), linked to immune cell infiltration and TME responses.
• The prognostic risk score was a significant risk factor for thyroid cancer outcomes (HR = 14.737, p = 0.009); scRNA-seq indicated predominant myeloid cell expression, validated by qRT-PCR.

Conclusions:

• Integrated bulk and single-cell RNA sequencing data to identify critical IRFGs in thyroid cancer.
• Developed a robust prognostic model based on IRFGs, enhancing prognostic evaluation for thyroid cancer patients.
• These findings offer novel therapeutic targets and strategies for managing thyroid cancer.