Zhang Siqin, Chen Shaoyong, Wang Yuchen, Zhan Yuxiang, Li Jiarui, Nong Xiaolin, Gao Biyun
College of Stomatology, Guangxi Medical University, Nanning, China.
Guangxi Key Laboratory of Oral and Maxillofacial Surgery Disease Treatment, Nanning, China.
Front Genet. 2021 Dec 17;12:744304. doi: 10.3389/fgene.2021.744304. eCollection 2021.
Although immunotherapy has recently demonstrated a substantial promise in treating advanced thyroid carcinoma (THCA), it is not appropriate for all THCA patients. As a result, this study aims to identify biomarkers for predicting immunotherapy efficacy and prognosis in THCA patients based on a constructed prognostic model. The transcriptomic and corresponding clinical data of THCA patients were obtained from the Cancer Genome Atlas (TCGA) database. We identified differentially expressed genes (DEGs) between THCA and normal samples and performed an intersection analysis of DEGs with immune-related genes (IRGs) downloaded from the ImmPort database. Functional enrichment analysis was performed on the chosen immune-related DEGs. Subsequently, Cox and LASSO regression analyses were conducted to obtain three hub immune-related DEGs, including PPBP, SEMA6B, and GCGR. Following that, a prognostic risk model was established and validated based on PPBP, SEMA6B, and GCGR genes to predict immunotherapy efficacy and THCA prognosis. Finally, we investigated the association between the constructed risk model and tumor mutational burden (TMB), abundance of tumor-infiltrating immune cells (TICs) as well as immunotherapeutic targets (PDL-1, PD-1, and CTLA4) in THCA. THCA patients in the high-risk score (RS) group showed higher TMB levels and worse prognosis than the low RS group. Patients in the high-RS group had higher proportions of monocytes, M2 macrophages, and activated dendritic cells, whereas those in the low-RS group exhibited higher numbers of M1 macrophages and dendritic resting cells. Our data implied that the constructed THCA prognostic model was sound and we concluded that the THCA patients having high TMB and low PD-L1 expression levels might respond poorly to immunotherapy. Taken together, we constructed a novel prognostic model for THCA patients to predict their prognosis and immunotherapy efficacy, providing a viable option for the future management of THCA patients in the clinic.
尽管免疫疗法最近在治疗晚期甲状腺癌（THCA）方面显示出巨大前景，但并非适用于所有THCA患者。因此，本研究旨在基于构建的预后模型，识别预测THCA患者免疫治疗疗效和预后的生物标志物。从癌症基因组图谱（TCGA）数据库获取THCA患者的转录组学及相应临床数据。我们鉴定了THCA与正常样本之间的差异表达基因（DEG），并对从ImmPort数据库下载的DEG与免疫相关基因（IRG）进行了交集分析。对选定的免疫相关DEG进行功能富集分析。随后，进行Cox和LASSO回归分析，以获得三个核心免疫相关DEG，包括血小板碱性蛋白（PPBP）、信号素6B（SEMA6B）和胰高血糖素受体（GCGR）。在此之后，基于PPBP、SEMA6B和GCGR基因建立并验证了一个预后风险模型，以预测免疫治疗疗效和THCA预后。最后，我们研究了构建的风险模型与THCA中肿瘤突变负荷（TMB）、肿瘤浸润免疫细胞（TIC）丰度以及免疫治疗靶点（程序性死亡受体配体1（PDL-1）、程序性死亡受体1（PD-1）和细胞毒性T淋巴细胞相关蛋白4（CTLA4））之间的关联。高风险评分（RS）组的THCA患者比低RS组显示出更高的TMB水平和更差的预后。高RS组患者的单核细胞、M2巨噬细胞和活化树突状细胞比例更高，而低RS组患者的M1巨噬细胞和静止树突状细胞数量更多。我们的数据表明构建的THCA预后模型是合理的，并且我们得出结论，TMB高且程序性死亡受体配体1（PD-L1）表达水平低的THCA患者可能对免疫治疗反应不佳。综上所述，我们为THCA患者构建了一个新的预后模型，以预测其预后和免疫治疗疗效，为临床THCA患者的未来管理提供了一个可行的选择。
