Liu Xiang, Zhu Qiao-Li, He Zi-Yi, Shu Jing-de, Xiang Cheng
Department of Breast and Thyroid Surgery, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, Zhejiang, China.
Department of Thyroid Surgery, The Second Affiliated Hospital of Zhejiang University College of Medicine, Hangzhou, Zhejiang, China.
Front Oncol. 2025 Jun 4;15:1535966. doi: 10.3389/fonc.2025.1535966. eCollection 2025.
We initially found that the thyroid differentiation score (TDS) was associated with the prognosis of papillary thyroid carcinoma (PTC) patients. Therefore, this study aimed to investigate the influencing factors and construct a discriminative model of high-risk dedifferentiation, and to explore the possible mechanisms.
Data were sourced from the TCGA database. The influences of the TDS, tumor mutation burden, and immune score on the progression-free interval (PFI) were assessed by the Kaplan-Meier method and multivariable Cox regression. Then, logistic regression analyses were utilized to explore the factors of dedifferentiation and a nomogram model was conducted. Additionally, differentially expressed genes (DEGs) were identified using sequencing data, while their regulatory pathways were determined by the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Finally, the differential expression of key genes of major pathways was explored.
This study included 391 PTC patients. After analyzing the influences of the three indicators on survival, only TDS showed an association with PFI. Multivariable logistic analysis revealed that the disease duration and PTC subtypes influenced dedifferentiation. The nomogram model based on these two variables showed improved discriminative capability. The study identified 17 overlapping DEGs associated with the dedifferentiation and three primary enrichment pathways, with complement and coagulation cascade pathways being the most significant (<0.001). The central gene was , which showed high expression in high-risk dedifferentiated and tall cell PTC, and the expression level increased as the disease progressed.
This research may contribute to promising identifying high-risk dedifferentiated PTC and also provide a potential therapeutic target.
我们最初发现甲状腺分化评分（TDS）与甲状腺乳头状癌（PTC）患者的预后相关。因此，本研究旨在探讨影响因素并构建高风险去分化的判别模型，并探索其可能的机制。
数据来源于TCGA数据库。采用Kaplan-Meier法和多变量Cox回归评估TDS、肿瘤突变负荷和免疫评分对无进展生存期（PFI）的影响。然后，利用逻辑回归分析探讨去分化的因素并构建列线图模型。此外，使用测序数据鉴定差异表达基因（DEG），并通过京都基因与基因组百科全书（KEGG）分析确定其调控途径。最后，探讨主要途径关键基因的差异表达。
本研究纳入391例PTC患者。在分析这三个指标对生存的影响后，只有TDS与PFI相关。多变量逻辑分析显示病程和PTC亚型影响去分化。基于这两个变量的列线图模型显示出更好的判别能力。该研究确定了17个与去分化相关的重叠DEG和三个主要富集途径，其中补体和凝血级联途径最为显著（<0.001）。中心基因为 ，在高风险去分化和高细胞PTC中高表达，且表达水平随疾病进展而升高。
本研究可能有助于识别高风险去分化PTC，并提供潜在的治疗靶点。
