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

摘要

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 point-of-care testing with high specificity and sensitivity.

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