Department of Nuclear Medicine, The Second Affiliated Hospital of Chengdu Medical College, China National Nuclear Corporation 416 Hospital, Chengdu, China.
School of Bioscience and Technology, Chengdu Medical College, Chengdu, China.
Front Endocrinol (Lausanne). 2022 Jun 8;13:895428. doi: 10.3389/fendo.2022.895428. eCollection 2022.
BRAF mutation is a representative oncogenic mutation, with a frequency of 60% in papillary thyroid carcinoma (PTC), but the reasons for the poor prognosis and more aggressive course of BRAF-mutated PTC are controversial. Tumor immune microenvironment (TIME) is an essential factor permitting the development and progression of malignancy, but whether TIME participates in the prognosis of BRAF-mutated PTC has not yet been reported. The primary goal of the present study was to provide a comprehensive TIME-related prognostic model to increase the predictive accuracy of progression-free survival (PFS) in patients with BRAF-mutated PTC. In this study, we analyzed the mRNA-seq data and corresponding clinical data of PTC patients obtained from the TCGA database. By calculating the TIME scores (immune score, stromal score and ESTIMATE score), the BRAF mutation group (n=237) was dichotomized into the high- and low-score groups. By functional analysis of differentially expressed genes (DEGs) in different high/low score groups, we identified 2 key TIME-related genes, and , which affected PFS in BRAF-mutated PTC. A risk scoring system was developed by multivariate Cox analysis based on the abovementioned 2 TIME-related genes. Then, the BRAF-mutated cohort was divided into the high- and low-risk groups using the median risk score as a cutoff. A high risk score correlated positively with a higher expression level but negatively with PFS in BRAF-mutated PTC. Ultimately, a nomogram was constructed by combining risk score with clinical parameter (Tumor stage), and the areas under the ROC curve (AUCs) of the nomogram for predicting 1-, 3- and 5-year PFS were then calculated and found to be 0.694, 0.707 and 0.738, respectively, indicating the improved accuracy and clinical utility of the nomogram versus the risk score model in the BRAF-mutated PTC cohort. Moreover, we determined the associations between prognostic genes or risk score and immune cell infiltration by two-way ANOVA. In the high-risk score, high HTR3A expression, and high NIPAL4 expression groups, higher infiltration of immune cells was found. Collectively, these findings confirm that the nomogram is effective in predicting the outcome of BRAF-mutated PTC and will add a spatial dimension to the developing risk stratification system.
BRAF 突变是一种代表性的致癌突变，在甲状腺乳头状癌（PTC）中的频率为 60%，但 BRAF 突变型 PTC 预后不良和侵袭性更强的原因仍存在争议。肿瘤免疫微环境（TIME）是允许恶性发展和进展的重要因素，但 TIME 是否参与 BRAF 突变型 PTC 的预后尚未报道。本研究的主要目的是提供一个全面的与 TIME 相关的预后模型，以提高 BRAF 突变型 PTC 患者无进展生存期（PFS）的预测准确性。在这项研究中，我们分析了 TCGA 数据库中获得的 PTC 患者的 mRNA-seq 数据和相应的临床数据。通过计算 TIME 评分（免疫评分、基质评分和 ESTIMATE 评分），将 BRAF 突变组（n=237）分为高分组和低分组。通过对不同高低评分组中差异表达基因（DEGs）的功能分析，我们鉴定出 2 个关键的与 TIME 相关的基因 和 ，它们影响 BRAF 突变型 PTC 的 PFS。基于上述 2 个与 TIME 相关的基因，采用多变量 Cox 分析建立风险评分系统。然后，使用中位数风险评分作为截止值将 BRAF 突变队列分为高风险组和低风险组。高风险评分与更高的 表达水平呈正相关，但与 BRAF 突变型 PTC 的 PFS 呈负相关。最终，通过结合风险评分和临床参数（肿瘤分期）构建了一个列线图，计算并发现该列线图预测 1、3 和 5 年 PFS 的 AUC 分别为 0.694、0.707 和 0.738，表明与风险评分模型相比，该列线图在 BRAF 突变型 PTC 队列中具有更高的准确性和临床实用性。此外，我们通过双向 ANOVA 确定了预后基因或风险评分与免疫细胞浸润之间的关系。在高风险评分、高 HTR3A 表达和高 NIPAL4 表达组中，发现免疫细胞浸润较高。总之，这些发现证实了该列线图在预测 BRAF 突变型 PTC 结局方面是有效的，并将为正在发展的风险分层系统增加空间维度。
