Elucidating the role of Pyroptosis in papillary thyroid cancer: prognostic, immunological, and therapeutic perspectives

Abstract

Abstract Background Pyroptosis, an inflammatory form of programmed cell death, has been implicated in the pathogenesis and progression of several cancers. However, the significance of pyroptosis-related genes (PRGs) in papillary thyroid cancer (PTC) remains unclear. Methods Transcriptome and clinical data of PTC patients were obtained from The Cancer Genome Atlas. The expression patterns of PRGs were identified by consensus clustering. A prognostic model for predicting the thyroid cancer-free interval (TCFi) employed five machine learning methods. Enrichment and immune-related analyses were performed to elucidate the role of pyroptosis. The responses to radioactive iodine (RAI), immune checkpoint inhibitors (ICIs), molecular targeted therapy (MTT), and chemotherapy (CTx) were predicted based on pyroptosis-derived features. Additionally, the expression of prognostic PRGs was validated via six external datasets, 16 cell lines, and 20 pairs of clinical samples. Results PTC patients were classified into three PyroClusters, C1 exhibited BRFA-like tumors with the highest invasiveness and the worst prognosis, C2 presented RAS-like tumors, and C3 was characterized by gene fusion. Nine PRGs (CXCL8, GJA1, H2BC8, IFI27, PRDM1, PYCARD, SEZ6L2, SIGLEC15, TRAF6) were filtered out to construct a PyroScore prognostic model. A derived nomogram demonstrated superior predictive performance than four clinical staging systems. A strong correlation between pyroptosis and tumor immune microenvironment (TIME) remodeling was observed in mechanistic analyses. Patients with a high PyroScore exhibited “hot” tumor immunophenotypes and had a poorer prognosis but could benefit more from ICIs and CTx (such as paclitaxel). Patients with a low PyroScore were more sensitive to RAI and MTT (such as pazopanib and sorafenib). Conclusions PyroScore model can effectively predict TCFi in patients with PTC. Dysregulated expression of PRGs is associated with the TIME modeling. Pyroptosis features have potential significance for developing novel therapeutic strategies for PTC patients.