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Purpose:  The pathogenesis of thymoma (THYM) remains unclear, and there is no uniform measurement standard for the complexity of THYM derived from different thymic epithelial cells. Consequently, it is necessary to develop novel biomarkers of prognosis estimation for patients with THYM.   Methods:  Consensus clustering and single-sample gene-set enrichment analysis were used to divide THYM samples into different immunotypes. Differentially expressed genes (DEGs) between those immunotypes were used to do the Kyoto Encyclopedia of Genes and Genomes analysis, Gene Ontology annotations, and protein-protein interaction network. Furthermore, the survival-related DEGs were used to construct prognostic model with lasso regression. The model was verified by survival analysis, receiver operating characteristic curve, and principal component analysis. Furthermore, the correlation coefficients of stemness index and riskscore, tumor mutation burden (TMB) and riskscore, drug sensitivity and gene expression were calculated with Spearman method.   Results:  THYM samples were divided into immunotype A and immunotype B. A total of 707 DEGs were enriched in various cancer-related or immune-related pathways. An 11-genes signature prognostic model ( CELF5, ODZ1, CD1C, DRP2, PTCRA, TSHR, HKDC1, KCTD19, RFX8, UGT3A2,  and  PRKCG ) was constructed from 177 survival-related DEGs. The prognostic model was significantly related to overall survival, clinical features, immune cells, TMB, and stemness index. The expression of some genes were significantly related to drug sensitivity.   Conclusion:  For the first time, a prognostic model of 11 genes was identified based on the immune microenvironment in patients with THYM, which may be helpful for diagnosis and prediction. The associated factors (immune microenvironment, mutation status, and stemness) may be useful for exploring the mechanisms of THYM.

Prognostic Model of Eleven Genes Based on the Immune Microenvironment in Patients With Thymoma