Screening tumor stage-specific candidate neoantigens in thyroid adenocarcinoma using integrated exome and transcriptome sequencing - Jia, Meng; Liang, Jiawen; Li, Zhuyao; Qin, Ye; Li, Qianqian; Wang, Jianwei; Lu, Xiubo (Frontiers in Immunology, 2023)
Incidence of thyroid carcinoma (THCA), the most common endocrine tumor, is continuously increasing worldwide. Although the overall prognosis of THCA is good, patients with distant metastases exhibit a mortality rate of 5-20%. Recently, immunotherapy has been increasingly used to treat various malignant tumors, such as gastric, lung, and prostate cancers, with antigens encoded by tumor-specific mutated genes. To improve the diagnosis and overall prognosis of patients with thyroid cancer, we screened specific candidate neoantigen genes in early-and late-stage THCA by analyzing the transcriptome and somatic cell mutations in this study. The top five earlystage neoantigen-related genes (NRGs) were G protein-coupled receptor 4 [GPR4], chondroitin sulfate proteoglycan 4 [CSPG4], teneurin transmembrane protein 1 [TENM1], protein S 1 [PROS1], and thymidine kinase 1 [TK1], whereas the top five late-stage NRGs were cadherin 6 [CDH6], semaphorin 6B [SEMA6B], dysferlin [DYSF], xenotropic and polytropic retrovirus receptor 1 [XPR1], and ABR activator of RhoGEF and GTPase [ABR]. Subsequently, we used machine learning models to verify their ability to screen NRGs and analyze the correlations among NRGs, immune cell types, and immune checkpoint regulators. Use of candidate antigen genes resulted in a better diagnostic model (area under the curve [AUC] value of the early-stage group [0.979] was higher than that of the late-stage group [0.959]). Then, a prognostic model was constructed to predict NRG survival, and the 1-, 3-and 5-year AUC values were 0.83, 0.87, and 0.86, respectively, which were closely related to different immune cell types. Comparison of the expression trends and mutation frequencies of NRGs in multiple tumors revealed their potential for the development of broad spectrum therapeutic drugs. In conclusion, candidate NRGs identified in this study can potentially be used as therapeutic targets and diagnostic biomarkers for the development of novel broad spectrum therapeutic agents.