Title:Construction of an Expression Classifier Based on an Immune-related
Ten-gene Panel for Rapid Diagnosis of Papillary Thyroid Carcinoma Risks
Volume: 17
Issue: 10
Author(s): Jingxue Sun, Jingjing Li, Yaguang Zhang, Jun Han, Jiaxing Wei, Yanmeizhi Wu, Bing Liu, Hongyu Han and Hong Qiao*
Affiliation:
- Department of Endocrinology and Metabolism, the Second Affiliated Hospital of Harbin Medical University, Harbin,
150001, China
Keywords:
Papillary thyroid carcinoma, immune-related gene panel, diagnostic model, risk classification, ceRNA regulatory network, SLC16A2, DEPDC1B.
Abstract:
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.
