Clinical and molecular impact of concurrent thyroid autoimmune disease and thyroid cancer: From the bench to bedside

Germline polymorphisms of the NOD2 pathway may predict the effectiveness of radioiodine in differentiated thyroid cancer treatment

PURPOSE

Differentiated thyroid cancer (DTC) presents a complex clinical challenge, especially in patients with distant metastases and resistance to standard treatments. This study aimed to investigate the influence of specific genes and their germline single nucleotide polymorphisms (SNPs) linked to both inflammatory processes and other neoplasms on the clinical and pathological characteristics of DTC, particularly their potential impact on radioiodine (RAI) treatment efficacy.

METHODS

This retrospective analysis involved a cohort of 646 patients diagnosed with DTC after thyroidectomy. Study covering 1998-2014, updated in 2023, included 567 women and 79 men (median age: 49; range: 7-83). SNP selection targeted functional significance, while mutational status was assessed by pyrosequencing for comprehensive characterization. Patient genetic profiles were assessed for associations with disease characteristics, RAI response, and cancer pathology.

RESULTS

Significant correlations emerged between certain SNPs and DTC features. Notably, the NOD2 c.802 T > C variant (rs2066842) was identified as a marker distinguishing between papillary thyroid cancer (PTC) and follicular thyroid cancer (FTC). Moreover, the c.802 T allele was associated with an enhanced response to RAI treatment, indicating a more substantial decrease in posttreatment stimulated thyroglobulin (sTg) concentrations. The NFKB1A allele c.126A (rs696) exhibited connections with lower FTC stages and a reduced probability of multifocality.

CONCLUSION

This study explored the molecular mechanisms of particular SNPs, highlighting the role of NOD2 in innate immunity and the stress response, and its potential impact on RAI efficacy. This research underscores the clinical promise of SNP analysis and contributes to personalized treatment strategies for DTC, emphasizing the relevance of genetic factors in cancer progression and treatment outcomes.

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

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.