Context Metabolic reprogramming is a common feature of tumorigenesis. It remains unknown concerning expression pattern of metabolism-associated genes in dedifferentiated thyroid cancer (DDTC). Objective This study aimed to identify a useful signature to indicate dedifferentiation of papillary thyroid cancer (PTC). Design and Setting We utilized one discovery and two validation cohorts to screen out aberrant metabolic genes in DDTC, and further used The Cancer Genome Atlas (TCGA) cohort to search for independent risk factors for the low-differentiated phenotype of PTC as a signature of dedifferentiation. The prediction of the signature for DDTC was validated in the TCGA cohort and the combined Gene Expression Omnibus (GEO) cohort. We also analyzed the correlations of the signature risk score with clinicopathological features of PTC. Gene set enrichment analyses were performed in the TCGA cohort. Results Significant enrichment of metabolic pathways correlated with differentiation status of PTC. A signature of metabolic genes including LPCAT2, ACOT7, HSD17B8, PDE8B and ST3GAL1 was discovered and validated across three cohorts. The signature was not only predictive of DDTC but also significantly associated with BRAF^V600E mutation (p<0.001), T3/T4 stage (p<0.001), extrathyroidal extension (p<0.001), lymph node metastasis (p<0.001) and III/IV stage (p<0.001) in PTC. Downregulations of LPCAT2 expression (p=0.009) and ST3GAL1 expression (p=0.005) increased risks of decreased disease-free survival for patients. Furthermore, the signature was implicated in a number of oncogenic biological pathways. Conclusions Our findings suggest metabolic deregulations mediate dedifferentiation of PTC, and the metabolic gene signature can be used as a biomarker for DDTC.

The incidence of coexisting papillary thyroid cancer (PTC) and Hashimoto’s thyroiditis (HT) is increasing. The impact of HT on PTC prognosis and its possible mechanism remains controversial. Interleukin-17A (IL-17A) has been reported to participate in the pathogenesis of multiple autoimmune diseases and cancers. The aim of this study is to investigate the role of IL-17A in PTC with coexistent HT and evaluate the changes in tumor antigenicity. Expression of IL-17A and major histocompatibility complex (MHC) class I molecules were compared on PTC tissue samples with or without HT. PTC cell lines K1 and TPC-1 were stimulated with IL-17A and analyzed for MHC class I expression afterwards. Cluster of differentiation (CD) 8+T cell activation, production of Interleukin-2 (IL-2) and Interferon-gamma (IFN-γ) as well as the programmed death-1 (PD-1) expression on lymphocytes were assessed by coculture of donor peripheral blood lymphocytes (PBLs) with IL-17A pretreated PTC cells. Elevated IL-17A and MHC class I expression were observed in PTC tissue samples with coexistent HT. Stimulation of PTC cells with IL-17A effectively increased MHC class I expression in vitro. Coculture of PBLs with IL-17A pretreated PTC cells resulted in enhanced T cell activation (%CD25+ of CD3+T cells) and increased IL-2 production along with decreased IFN-γ secretion and PD-1 expression of the lymphocytes. Papillary thyroid cancer with coexisting Hashimoto’s thyroiditis presents elevated MHC class I expression, which may be the result of IL-17A secretion. T cell activation is enhanced in vitro by IL-17A and may provide future utility in PTC immunotherapy.

Background: Papillary thyroid cancer (PTC) with the BRAF ^V600E mutation is associated with a poorer prognosis. BRAF inhibitors may demonstrate limited efficacy due to emerging drug resistance. The Warburg effect may have cancer therapeutic implications. It is not known if the BRAF ^V600E mutation is associated with altered glucose metabolism in PTC. Methods: This study examined the effect of BRAF ^V600E and dynamin-related protein 1 (DRP1) on various cellular processes in PTC cells, including cell proliferation, migration, invasion, mitochondrial fission, glucose metabolism, reactive oxygen species (ROS) generation, and apoptosis. We used RT-qPCR to assess the expression of key glycolytic enzymes in thyroid cancer tissues. Additionally, the regulatory interaction between BRAF ^V600E and DRP1 was investigated through Western blot and immunohistochemical staining. We further evaluated the impact of DRP1 in PTC and the inhibitory effects of dabrafenib and 2-deoxy- d -glucose (2-DG) in vitro and in vivo . Results: We found that the BRAF ^V600E mutation significantly augments aerobic glycolysis while suppressing oxidative phosphorylation in PTC. We identified the BRAF ^V600E /p-ERK/p-DRP1(Ser616) signaling pathway as a critical mediator in PTC progression. First, the BRAF ^V600E /p-ERK/p-DRP1(Ser616) signaling pathway enhances cell proliferation by upregulating hexokinase 2 expression and thereby increasing aerobic glycolysis. Second, it inhibits apoptosis by promoting mitochondrial fission and reducing ROS levels. Moreover, we demonstrated that the combination therapy of 2-DG and dabrafenib markedly impedes the progression of BRAF ^V600E -positive PTC. Conclusion: The BRAF ^V600E /p-ERK/p-DRP1(Ser616) signaling pathway plays a pivotal role in glucose metabolism reprogramming, contributing to the aggressiveness and progression of BRAF ^V600E -positive PTC. Our findings suggest that a combined therapeutic approach using 2-DG and dabrafenib has the potential to improve the outcome of PTC patients with BRAF ^V600E .

Purpose: From 8% to 28% of patients with papillary thyroid carcinoma (PTC) experience recurrence, complicating risk stratification and treatment. We previously identified an inflammatory molecular subtype of PTC associated with poor prognosis. Based on this subtype, we aimed to develop and validate a noninvasive radiomic signature to predict prognosis and treatment response in patients with PTC. Experimental Design: We collected preoperative ultrasound images from two large independent centers (n = 2,506) to develop and validate a deep learning radiomics signature of inflammation (DLRI) for predicting the inflammatory subtype of PTC, including its correlation with prognosis and anti-inflammatory traditional Chinese medicine (TCM) treatment. Training set 1 (n = 64) and internal validation set 2 (n = 1,108) were from Tianjin Medical University Cancer Institute and Hospital. External validation sets 1 (n = 76) and 2 (n = 1,258) were from Fudan University Shanghai Cancer Center. Results: We developed a DLRI to accurately predict PTC’s inflammatory subtype (AUC = 0.97 in training set 1 and AUC = 0.82 in external validation set 1). High-risk DLRI was significantly associated with poor disease-free survival in the first cohort [HR = 16.49, 95% confidence interval (CI), 7.92–34.35, P < 0.001] and second cohort (HR = 5.42, 95% CI, 3.67–8.02, P < 0.001). The DLRI independently predicted disease-free survival, irrespective of clinicopathologic variables (P < 0.001 for all). Furthermore, patients with high-risk DLRI were likely to benefit from anti-inflammatory TCM treatment (HR = 0.19, 95% CI, 0.06–0.55, P = 0.002), whereas those with low-risk DLRI did not. Conclusions: DLRI is a reliable noninvasive tool for evaluating prognosis and guiding anti-inflammatory TCM treatment in patients with PTC. Prospective studies are needed to confirm these findings.

We developed a deep residual network (ResNet) framework to classify thyroid cancer differentiation states by integrating multiomic data and interpretability analysis. Our framework incorporated untargeted metabolomic, whole-exome sequencing, and transcriptomic data from 158 thyroid tumors and 57 matched normal tissues, encompassing well-differentiated, poorly differentiated, and anaplastic thyroid cancers. We further examined single-cell RNA sequencing datasets from the Gene Expression Omnibus (GEO) to map key metabolic reprogramming pathways in dedifferentiated thyroid cancer. We systematically integrated transcriptomic data from follicular epithelial-derived thyroid carcinomas across all GEO cohorts, establishing a pan-pathological classification model based on a 10-gene metabolic signature. To complement this approach, a 10-metabolite model was developed via the ResNet architecture, capitalizing on the direct pathophysiological responsiveness of metabolites to tumor progression states. By employing Shapley additive explanations, we highlighted critical metabolic signatures driving differentiation states. Our findings reveal how metabolic shifts underpin thyroid cancer progression, and based on these findings, we propose an accurate, interpretable model that may facilitate early diagnosis and inform clinical decision-making.