Development and validation of a gene model predicting lymph node metastasis and prognosis of oral squamous cell carcinoma based on single‐cell and bulk RNA‐seq analysis

Lymph node metastasis can independently predict oral squamous cell carcinoma patients' survival. This study would investigate the genetic and cellular differences between oral squamous cell carcinoma with positive and negative lymph node metastases.We gathered single-cell RNA sequencing and bulk gene expression data from the Cancer Genome Atlas and Gene Expression Omnibus databases. Sixty lymph node-metastasis-related genes were discovered with refined single-cell RNA sequencing data analysis, and consensus clustering provided three molecular subtypes of oral squamous cell carcinoma. Least absolute shrinkage and selection operator analyses were then utilized to establish a five-gene risk model. CIBERSORT analysis revealed the immune infiltration profile of different risk subgroups.Oral squamous cell carcinoma patients were classified into three subtypes based on the 60 lymph node-metastasis-related key genes identified by single-cell RNA sequencing data. Patients in Subtype 3 showed a tendency for lymph node metastasis and poorer prognosis. Moreover, five biomarkers were selected from the 60 genes to construct a five-gene risk model evaluating the risk of lymph node metastasis. A lower probability of lymph node metastasis and a better prognosis was observed in the low-risk group. The immune infiltration of three different risk groups was explored with CIBERSORT. Besides, further analysis implied different sensitivities of anticancer drugs, including immunotherapy drugs and targeted compounds, in the three risk groups.In view of intratumoral heterogeneity, we found 60 genes associated with lymph node metastasis of oral squamous cell carcinoma. Subsequently, we constructed a five-gene signature that could improve the prediction of lymph node metastasis, clinical outcome, and promote individualized treatment strategies for oral squamous cell carcinoma.