免疫疗法
免疫系统
肿瘤科
基底细胞
主成分分析
医学
内科学
免疫学
生物
计算生物学
计算机科学
人工智能
标识
DOI:10.1080/10255842.2023.2179364
摘要
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.
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