Identification a unique disulfidptosis classification regarding prognosis and immune landscapes in thyroid carcinoma and providing therapeutic strategies

Abstract

Abstract Background Thyroid carcinoma (THCA) is a common type of cancer worldwide, and its incidence has been increasing in recent years. Disulfidoptosis, a recently defined form of metabolic-related regulated cell death (RCD), has been shown to play a sophisticated role in antitumor immunity. However, its mechanisms and functions are still poorly understood and the association between disulfidoptosis and the prognosis of patients with papillary thyroid carcinoma remains to be elucidated. This study aims to investigate the connection between disulfidoptosis and the prognosis of thyroid cancer, while also developing a prognostic index based on disulfidoptosis genes.Materials and methods We utilized 24 genes associated with disulfidoptosis to create the classification and model. To gather data, we sourced gene expression profiles, somatic mutation information, copy number variation data, and corresponding clinical data from the TCGA database for patients with thyroid cancer. Additionally, we obtained single-cell transcriptome data GSE184362 from the Gene Expression Omnibus (GEO) database for further analysis.Results In this study, we utilized 24 genes associated with disulfidoptosis to identify two distinct groups with different biological processes using non-negative matrix factorization (NMF). Our findings showed that Cluster 1 is associated with chemokines, interleukins, interferons, checkpoint genes, and other important components of the immune microenvironment. Moreover, cluster 1 patients with high IPS scores may be more sensitive to immunotherapy. We also provide drug therapeutic strategies for each cluster patients based on the IC50 of each drug. The Enet model was chosen as the optimal model with the highest C-index and showed that patients with high risk had a worse prognosis and weak cell-to-cell interactions in THCA. Finally, we established a nomogram model based on multivariable cox and logistic regression analyses to predict the overall survival of THCA patients.Conclusion This research provides new insight into the impact of disulfidoptosis on THCA. Through a thorough examination of disulfidoptosis, a new classification system has been developed that can effectively predict the clinical prognosis and drug sensitivity of THCA patients.