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Comprehensive analysis of matrix metalloproteinases and their inhibitors in head and neck squamous cell carcinoma

dataset
posted on 2021-12-09, 06:00 authored by Mingyuan Zou, Chen Zhang, Yan Sun, Huina Wu, Feng Xiao, Wei Gao, Fengfeng Zhao, Xiaobo Fan, Guoqiu Wu

Objective: This study aimed to explore the association of matrix metalloproteinases (MMPs) and tissue inhibitors of metalloproteinases (TIMPs) with cancer progression and prognosis in head and neck squamous cell carcinoma (HNSCC).

Methods: Differentially expressed genes (DEGs) were identified by LIMMA package using R software. The correlation between the expression levels of MMPs and TIMPs in HNSCC cancer samples and adjacent normal tissue samples was performed using Pearson correlation analysis. The Kruskal-Wallis test (H-test) was used to determine the association between the expression level of MMPs/TIMPs and HNSCC clinical stage. The survival result was expressed as a KM curve, and the log-rank test was used for statistical analysis. Lasso regression and multivariate Cox regression analyses were used to examine whether the gene signature based on MMPs and TIMPs was an independent prognostic factor in patients with HNSCC.

Results: Among the top 10 most up-regulated genes in HNSCC cancer tissues when compared with normal tissues, six genes belonged to the MMPs. Spearman correlation analysis revealed that only MMP11 and MMP23B were positively correlated with tumor stage. Survival analysis showed that patients with a high expression of MMP14, MMP20, TIMP1, and TIMP4 had a worse prognosis than low expression patients. Additionally, a novel five-gene (MMP3, MMP17, MMP19, MMP24, and TIMP1) signature was constructed and significantly associated with prognosis as an independent prognostic signature.

Conclusions: Our data show that the accuracy of a single gene of MMP or TIMP as predictors of progression and prognosis of HNSCC is limited, although some studies have proposed that MMPs act as driving factors for cancer progression. The prediction performance of the five-gene signature prediction model was much better than that of the gene signatures based on every single gene in prognosis prediction.

Funding

This work was supported by grants from the Fundamental Research Funds for the Central Universities [2242020K40130 and 2242020K10020], and the National Science and Technology Major Project [No. 2020ZX09201015], National Natural Science Foundation of China [Nos. 81773624, 81603016, and 81900453], National Natural Science Foundation of Jiangsu Province [Nos. BE2017746 and BK20160706], and Medical Science and Technology Development Foundation of Nanjing Department of Health [Nos. YKK18267, YKK18255, and YKK19162].

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