Identification an innovative classification and nomogram for predicting the prognosis of thyroid carcinoma patients and providing therapeutic schedules.

Abstract

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.