Construction of an Expression Classifier Based on an Immune-related Ten-gene Panel for Rapid Diagnosis of Papillary Thyroid Carcinoma Risks

Background: Molecular alterations have been recognized as valuable diagnostic biomarkers for papillary thyroid carcinoma (PTC). Objectives: This study aimed to identify immune-related gene signatures associated with PTC progression using a computational pipeline and to develop an expression-based panel for rapid PTC risk classification. Methods: RNA-seq data and clinical information for PTC samples were downloaded from The Cancer Genome Atlas, followed by an analysis of differentially expressed (DE) RNAs among high-risk PTC, low-risk PTC, and normal groups. Immune cell infiltration and protein–protein interaction analyses were performed to obtain DE RNAs related to immunity. Then, a competing endogenous RNA (ceRNA) network was constructed to identify hub genes for the construction of a diagnostic model, which was evaluated by a receiver operator characteristic curve. A manually curated independent sample cohort was constructed to validate the model. Results: By analyzing the immune cell infiltration, we found that the infiltration of plasma cells and CD8+ T cells was more abundant in the high-risk groups, and 68 DE mRNAs were found to be significantly correlated with these immune cells. Then a ceRNA network containing 10 immune-related genes was established. The ten-gene panel (including DEPDC1B, ELF3, VWA1, CXCL12, SLC16A2, C1QC, IPCEF1, ITM2A, UST, and ST6GAL1) was used to construct a diagnostic model with specificity (66.3%), sensitivity (83.3%), and area under the curve (0.762) for PTC classification. DEPDC1B and SLC16A2 were experimentally validated to be differentially expressed between high-risk and low-risk patients. Conclusion: The 10 immune-related gene panels can be used to evaluate the risk of PTC during pointof- care testing with high specificity and sensitivity.