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Published on: August 23, 2019
Combination of inflammatory proteins in serum can be used to diagnose papillary thyroid carcinoma with lymph node
Yongqin Pan1, Guanghao Wang2, Delin Chen2
1Department of Thyroid Surgery, The First Affiliated Hospital of Jinan University, No. 613, W. Huangpu Avenue, Tianhe District, Guangzhou, 510630, China. moonlight0518@126.com.
Background:
Accurately distinguishing lymph node metastases (LNM) from papillary thyroid carcinomas (PTC) is crucial in clinical practice. The role of the immune system in PTC-LNM has attracted increasing attention. The aim of the present study was to evaluate the differential expression of 92 immune-related proteins in the serum and identify their potential diagnostic effects in patients with PTC-LNM.
Methods:
The 92 immune-related proteins were analyzed using a proximity extension assay. In addition, logistic regression and least absolute shrinkage and selection operator regression methods were used to develop combined diagnostic markers for thyroid cancer. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic validity. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis were used to analyze the potential regulatory pathways.
Results:
Five proteins, including IL-22RA1, IL-12B, CCL4, CCL3, and IL-1α, were significantly elevated in the serum of patients with LNM. The combined diagnosis of these five proteins demonstrated excellent diagnostic performance in distinguishing patients with PTC-LNM (area under the curve = 0.967, sensitivity = 0.941, and specificity = 0.889). Further analysis revealed that IL12B and IL1A mRNAs were significantly overexpressed in patients with PTC-LNM. This study also showed that the IL12B and IL1A was closely related to the PI3K-AKT, NF-κB, and MAPK signaling pathways.
Conclusion:
The combination of IL-22RA1, IL-12B, CCL4, CCL3, and IL-1α represents a promising diagnostic panel for PTC-LNM. These findings provide a novel set of diagnostic markers for PTC-LNM based on serum inflammatory protein levels.

