Identification and validation of a novel tumor driver gene signature for diagnosis and prognosis of head and neck squamous cell carcinoma

Abstract

Objective: Head and neck squamous cell carcinoma (HNSCC) is a common heterogeneous cancer with complex carcinogenic factors. However, the current TNM staging criteria to judge its severity to formulate treatment plans and evaluate the prognosis are particularly weak. Therefore, a robust diagnostic model capable of accurately diagnosing and predicting HNSCC should be established.Methods: Gene expression and clinical data were retrieved from The Cancer Genome Atlas and Gene Expression Omnibus databases. Key prognostic genes associated with HNSCC were screened with the weighted gene co-expression network analysis and least absolute shrinkage and selection operator (LASSO) Cox regression model analysis. We used the timeROC and survival R packages to conduct time-dependent receiver operating characteristic curve analyses and calculated the area under the curve at different time points of model prediction. Patients in the training and validation groups were divided into high- and low-risk subgroups, and Kaplan-Meier (K-M) survival curves were plotted for all subgroups. Subsequently, LASSO and support vector machine algorithms were used to screen genes to construct diagnostic model. Furthermore, we used the Wilcoxon signed-rank test to compare the half-maximal inhibitory concentrations of common chemotherapy drugs among patients in different risk groups. Finally, the expression levels of eight genes were measured using quantitative real-time polymerase chain reaction and immunohistochemistry.Results: Ten genes (SSB, PFKP, NAT10, PCDH9, SHANK2, PAX8, CELSR3, DCLRE1C, MAP2K7, and ODF4) with prognostic potential were identified, and a risk score was derived accordingly. Patients were divided into high- and low-risk groups based on the median risk score. The K-M survival curves confirmed that patients with high scores had significantly worse overall survival. Receiver operating characteristic curves proved that the prognostic signature had good sensitivity and specificity for predicting the prognosis of patients with HNSCC. Univariate and multivariate Cox regression analyses confirmed that the gene signature was an independent prognostic risk factor for HNSCC. Diagnostic model was built by identifying eight genes (SSB, PFKP, NAT10, PCDH9, CELSR3, DCLRE1C, MAP2K7, and ODF4). The high-risk group showed higher sensitivity to various common chemotherapeutic drugs. DCLRE1C expression was higher in normal tissues than in HNSCC tissues.Conclusion: Our study identified the important role of tumor-driver genes in HNSCC and their potential clinical diagnostic and prognostic values to facilitate individualized management of patients with HNSCC.

NAT10 promotes the tumorigenesis and progression of laryngeal squamous cell carcinoma through ac4C modification of FOXM1 mRNA

“… 29 Importantly, the potential diagnostic and prognostic role of NAT10 has been revealed in LSCC. 30 , 31 To analyze the functional effects of NAT10 on LSCC cells, we established sh-NAT10 and the negative control sh-NC plasmids to stably interfere with NAT10 expression for loss-of-function assays. Through CCK-8 detection, we identified that the proliferation of two LSCC cells was dramatically weakened by the knockdown of NAT10.…”