In recent years, a notably heterogeneous malignant tumor, squamous cell carcinoma of the head and neck (HNSCC), has received increasing attention, with no significant improvement in its survival rate. The rapid increase in the number of prognostic models associated with HNSCC has been observed due to its accuracy, which offers crucial clinical benefits. The 10 genes were selected from 222 human genes associated with the basement membrane in the analysis of this article. The gene pool was narrowed through different classifications and intersections, followed by univariate Cox regression analysis. Genes with statistical significance underwent further Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, resulting in the final selection of 10 genes. The data and images extracted from the Human Protein Atlas database were utilized to confirm the differential expression of the corresponding genes. Multivariate Cox regression analysis was employed to develop a nomogram, and the nomogram was assessed by additional decision curve analysis (DCA). The Gene Expression Omnibus validation set was used to validate the established model. Finally, between the high- and low-risk score groups, Gene Set Enrichment Analysis, immune correlation analysis, and drug sensitivity analysis were conducted in this paper. ITGA5, SPOCK1, EVA1C, TINAGL1, LAMB4, ADAMTS1, EGFL6, GPC2, BGN, and ITGA2B were successfully developed as basement membrane-associated risk models. The time-dependent receiver operating characteristic (timeROC) curve illustrated that the risk score prediction accuracy outperformed indicators, which were commonly adopted in clinical practice, consisting of age, stage, gender, T-staging, and N-staging. The 3-year risk score timeROC area under the curve value was 0.679. This model demonstrates a reliable ability to assess the prognosis of HNSCC patients. In addition, the specific potential biomarkers associated with the basement membrane were explored in this research.

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