Immunotyping of thyroid cancer for clinical outcomes and implications.

Abstract

Background: Tumor immune microenvironment (TIME) plays a crucial role in cancer development. However, the prognostic significance of immune-related genes (IRGs) in thyroid cancer (THCA) is unclear.

Methods: The Cancer Genome Atlas (TCGA)-THCA dataset was downloaded. The CIBERSORT algorithm was used to determine immune cell infiltration and a Weighted Gene Co-expression Network Analysis (WGCNA) was executed to obtain immune cell-related genes. Univariate Cox analysis was performed to screen prognostic genes and THCA samples were categorized into different immune cell-related clusters. The correlations between clusters and THCA prognosis and clinical characteristics were explored. Differentially expressed genes (DEGs) between THCA and controls from TCGA-THCA were identified. Macrophage and lymphocyte abundances, IFN-γ, wound healing, and TGF-beta levels were determined using the single set gene set enrichment analysis (GSEA), and THCA samples were categorized into different immune-related clusters, and corresponding genes were obtained from WGCNA. DEGs, IRGs, and immune-related clusters genes were subjected to overlap analysis to obtain differentially expressed IRGs (DE-IRGs), and these were subjected to least absolute shrinkage and selection operator (LASSO) and multivariate Cox analyses to identify prognosis-related genes. THCA samples were divided into high/low-risk groups based on the median risk score. Furthermore, the prognostic model's utility in predicting immunotherapy response was analyzed. The potential therapeutic drugs were obtained. The expression of the corresponding genes in 10 pairs of clinical specimens was evaluated and those of proteins were analyzed by immunofluorescence assay.

Results: TCGA-THCA samples were categorized into two immune cell-related clusters based on 141 prognostic immune cell-related genes. Significant differences in survival and clinical characteristics such as T Stage between clusters. In total, 16,648 DEGs between THCA and control samples were extracted. THCA samples were categorized into two immune-related clusters and were found to affect the prognosis and TIME of THCA. By using LASSO and multivariate Cox analyses for 88 DE-IRGs, three prognostic IRGs, namely FLNC, IL18, and MMP17 were identified. The TIDE score of the low-risk group was significantly lower than that of the other one, indicating that these samples were more responsive to immunotherapy. The 50% inhibitory concentration (IC50) of camptothecin, methotrexate, rapamycin, and others were notably different between the risk groups.

Conclusion: Based on bioinformatics analysis, we constructed an immune-related prognosis model for THCA, which is expected to provide new ideas for studies related to the prognosis and treatment of THCA.