Immunohistochemical analysis of matrix metalloproteinase-9 predicts papillary thyroid carcinoma prognosis

基质金属蛋白酶-9 的免疫组织化学分析可预测甲状腺乳头状癌的预后

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作者:Xingkai Liu, Chang Su, Jing Xu, Dan Zhou, He Yan, Wei Li, Guihui Chen, Nan Zhang, Dahai Xu, Haixia Hu

Abstract

The present study aimed to explore the association between immunohistochemical matrix metalloproteinase-9 (MMP-9) expression and the clinicopathological characteristics of patients with papillary thyroid carcinoma (PTC), and to determine whether it may be used as a diagnostic or prognostic tool for PTC. Immunohistochemical staining of MMP-9 was performed in thyroid tissues obtained from 112 patients with PTC and 42 subjects with benign thyroid nodules (BTNs). The receiver operating characteristic curve was used to evaluate the legitimacy of MMP-9 as a diagnostic tool for PTC, and a predictor for structurally persistent/recurrent disease (SPRD) and disease status. Cox regression was applied to identify the risk factors of disease status and SPRD. The present study revealed that MMP-9 was overexpressed in PTC tissues, compared with in BTN tissues. Furthermore, MMP-9 scores yielded an area under the curve (AUC) of 0.842 (95% CI, 0.776-0.908) for differentially diagnosing PTC from BTN. In addition, the MMP-9 score was greater if patients previously had central lymph node metastasis, lateral lymph node metastasis or an advanced tumor-node-metastasis stage (III+IV). When MMP-9 was employed to predict disease status and SPRD, an AUC of 0.811 (95% CI, 0.706-0.917) and 0.806 (95% CI, 0.620-0.992) was obtained, respectively. A tumor size of >2 cm and an MMP-9 staining score of ≥6 were independent risk factors for predicting disease status, whereas vascular invasion and an MMP-9 staining score of ≥8 were risk factors for predicting SPRD. Furthermore, an MMP-9 staining score of ≥6 and ≥8 indicated shortened disease-free survival and survival without SPRD, respectively. In conclusion, the assessment of MMP-9 expression in thyroid carcinoma samples may represent a potential and supplementary tool for the diagnosis and prognostic prediction of PTC.

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