RESUMEN
AIMS: Cancer-associated fibroblasts (CAFs) have been shown to promote the metastasis of head and neck squamous cell carcinoma (HNSCC), but the underlying mechanisms remain unclear. The purpose of this study is to identify gene in CAFs that are associated with metastasis and to preliminarily validate its impact on the metastasis of HNSCC. MATERIALS AND METHODS: Scissor analysis was utilized to process single-cell and bulk RNA sequencing datasets, identifying genes associated with the metastasis of HNSCC through differential gene expression analysis. A risk model was constructed using LASSO regression analysis. Quantitative real time-PCR and Western blot were employed to measure mRNA and protein expressions, respectively. Multiplex immunohistochemistry (mIHC) was used to assess protein expression in CAFs. siRNA was utilized to achieve gene knockdown. CCK-8 and Transwell assays were employed to evaluate the biological characteristics of HNSCC cells. KEY FINDINGS: Compare to the nonmetastatic primary CAFs (nmCAFs), tissue inhibitors of metalloproteinase-1 (TIMP1) was founded to be overexpressed in the cells and tissues of metastatic primary CAFs (mCAFs). Knocking down TIMP1 in CAFs can signifucantly inhibit the proliferation, invasion, and migration of HNSCC cells. SIGNIFICANCE: CAFs facilitate HNSCC cell metastasis by upregulating TIMP1, highlighting TIMP1 as a potential therapeutic target in HNSCC metastasis management.
RESUMEN
Background: The purpose of this study was to identify the prognostic value of cuproptosis and copper metabolism-related genes, to clarify their molecular and immunological characteristics, and to elucidate their benefits in head and neck squamous cell carcinoma (HNSCC). Methods: The details of human cuproptosis and copper metabolism-related genes were searched and filtered from the msigdb database and the latest literature. To identify prognostic genes associated with cuproptosis and copper metabolism, we used least absolute shrinkage and selection operator regression, and this coefficient was used to set up a prognostic risk score model. HNSCC samples were divided into two groups according to the median risk. Afterwards, the function and immune characteristics of these genes in HNSCC were analyzed. Results: The 14-gene signature was constructed to classify HNSCC patients into low-risk and high-risk groups according to the risk level. In the The Cancer Genome Atlas (TCGA) cohort, the overall survival (OS) rate of the high-risk group was lower than that of the low-risk group (P < 0.0001). The area under the curve of the time-dependent Receiver Operator Characteristic (ROC) curve assessed the good performance of the genetic signature in predicting OS and showed similar performance in the external validation cohort. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment assays and Protein-Protein Interaction (PPI) protein networks have been used to explore signaling pathways and potential mechanisms that were markedly active in patients with HNSCC. Furthermore, the 14 cuproptosis and copper metabolism-related genes were significantly correlated with the immune microenvironment, suggesting that these genes may be linked with the immune regulation and development of HNSCC. Conclusions: Our results emphasize the significance of cuproptosis and copper metabolism as a predictive biomarker for HNSCC, and its expression levels seem to be correlated with immune- related features; thus, they may be a possible biomarker for HNSCC prognosis.
