An immune cell infiltration landscape classification to predict prognosis and immunotherapy effect in oral squamous cell carcinoma.

Tumor immune cell infiltration (ICI) is associated with the prognosis of oral squamous cell carcinoma (OSCC) patients and the effect of immunotherapy. The combat algorithm was used to merge the data from three databases and the Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm to quantify the amount of infiltrated immune cells. Unsupervised consistent cluster analysis was used to determine ICI subtypes, and differentially expressed genes (DEGs) were determined according to these subtypes. The DEGs were then clustered again to obtain the ICI gene subtypes. The principal component analysis (PCA) and the Boruta algorithm were used to construct the ICI scores. Three different ICI clusters and gene clusters with a prognosis of significant difference were found and the ICI score was constructed. Patients with higher ICI scores have a better prognosis following internal and external verification. Besides, the proportion of patients with effective immunotherapy was higher than those with low scores in two external datasets with immunotherapy. This study shows that the ICI score is an effective prognostic biomarker and a predictor of immunotherapy.