甲状腺癌（THCA）是全球常见的一种癌症类型，近年来其发病率呈逐渐上升的趋势。越来越多的证据表明，程序性细胞死亡（PCD）模式对肿瘤进展起着重要影响。然而，编码辅助清理的细胞死亡模式与乳头状甲状腺癌患者的预后之间的关联尚待进一步阐明。本研究旨在探究PCD与甲状腺癌预后之间的关系，并同时基于PCD基因开发预后指数。我们采用了程序性细胞死亡模式构建模型并定义簇。来自TCGA数据库的568名甲状腺癌患者的基因表达谱基因组学数据和临床数据，以及从Gene Expression Omnibus（GEO）数据库获取的单细胞转录组数据GSE184362用于后续分析。本研究采用了六种机器学习算法创建了一个程序性细胞死亡特征（PCDS）。最终，通过SVM开发的模型被选为最优模型，具有最高的C-index。此外，应用非负矩阵分解（NMF）确定了两种THCA分子亚型，每种亚型都具有不同的重要生物过程和药物敏感性。通过对平均和单细胞转录组的综合分析，研究发现PCDS与趋化因子、白细胞介素、干扰素和检查点基因以及肿瘤微环境的重要组分相关。THCA患者中PCDS值升高的患者更容易对传统化疗方案表现出抗药性，但对靶向治疗药物可能表现出更高的敏感性。最后，我们根据多变量Cox和Logistic回归分析建立了一个预测THCA患者总生存的分析模型。该研究揭示了程序性细胞死亡（PCD）模式在THCA中的作用。通过对各种细胞死亡模式进行深入分析，我们开发了一种新的PCD模型，能够准确预测具有THCA的患者的临床预后和药物敏感性。© 2023. 作者，独家授权给Springer-Verlag GmbH Germany，Springer Nature的一部分。

Thyroid carcinoma (THCA) represents a prevalent form of cancer globally, with its incidence demonstrating an upward trend in recent years. Accumulating evidence has indicated that programmed cell death (PCD) patterns exert a vital influence on tumor progression. Nevertheless, the association between PCD and the prognosis of patients with papillary thyroid carcinoma remains to be elucidated. The current study endeavors to examine the link between PCD and the prognosis of thyroid cancer while concurrently developing a prognostic index based on PCD genes.Programmed cell death patterns were employed to construct the model and define clusters. Gene expression profile genomics and clinical data pertaining to 568 patients with thyroid cancer were sourced from the TCGA database. In addition, single-cell transcriptome data GSE184362 were procured from the Gene Expression Omnibus (GEO) database for subsequent analysis.The study harnessed six machine learning algorithms to create a programmed cell death signature (PCDS). Ultimately, the model developed via SVM was chosen as the optimal model, boasting the highest C-index. Moreover, the application of non-negative matrix factorization (NMF) led to the identification of two molecular subtypes of THCA, each characterized by distinct vital biological processes and drug sensitivities. The investigation revealed that PCDS is linked to chemokines, interleukins, interferons, and checkpoint genes, as well as pivotal components of the tumor microenvironment, as determined through a comprehensive analysis of bulk and single-cell transcriptomes. Patients with THCA and elevated PCDS values are more inclined to exhibit resistance to conventional chemotherapy regimens, yet may display heightened responsiveness to targeted therapeutic agents. Finally, we established a nomogram model based on multivariable cox and logistic regression analyses to predict the overall survival of THCA patients.This research sheds new light on the role of programmed cell death (PCD) patterns in THCA. By conducting an in-depth analysis of various cell death patterns, a novel PCD model has been devised, capable of accurately predicting the clinical prognosis and drug sensitivity of patients with THCA.© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.