Nomogram model based on preoperative serum thyroglobulin and clinical characteristics of papillary thyroid carcinoma to predict cervical lymph node metastasis.

Objective: Preoperative evaluation of cervical lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) has been one of the serious clinical challenges. The present study aims at understanding the relationship between preoperative serum thyroglobulin (PS-Tg) and LNM and intends to establish nomogram models to predict cervical LNM. Methods: The data of 1,324 PTC patients were retrospectively collected and randomly divided into training cohort (n = 993) and validation cohort (n = 331). Univariate and multivariate logistic regression analyses were performed to determine the risk factors of central lymph node metastasis (CLNM) and lateral lymph node metastasis (LLNM). The nomogram models were constructed and further evaluated by 1,000 resampling bootstrap analyses. The receiver operating characteristic curve (ROC curve), calibration curve, and decision curve analysis (DCA) of the nomogram models were carried out for the training, validation, and external validation cohorts. Results: Analyses revealed that age, male, maximum tumor size >1 cm, PS-Tg ≥31.650 ng/ml, extrathyroidal extension (ETE), and multifocality were the significant risk factors for CLNM in PTC patients. Similarly, such factors as maximum tumor size >1 cm, PS-Tg ≥30.175 ng/ml, CLNM positive, ETE, and multifocality were significantly related to LLNM. Two nomogram models predicting the risk of CLNM and LLNM were established with a favorable C-index of 0.801 and 0.911, respectively. Both nomogram models demonstrated good calibration and clinical benefits in the training and validation cohorts. Conclusion: PS-Tg level is an independent risk factor for both CLNM and LLNM. The nomogram based on PS-Tg and other clinical characteristics are effective for predicting cervical LNM in PTC patients.

A Clinical Predictive Model of Central Lymph Node Metastases in Papillary Thyroid Carcinoma.

Background: Thyroid carcinoma is one of the most common endocrine tumors, and papillary thyroid carcinoma (PTC) is the most common pathological type. Current studies have reported that PTC has a strong propensity for central lymph node metastases (CLNMs). Whether to prophylactically dissect the central lymph nodes in PTC remains controversial. This study aimed to explore the risk factors and develop a predictive model of CLNM in PTC. Methods: A total of 2,554 patients were enrolled in this study. The basic information, laboratory examination, characteristics of cervical ultrasound, genetic test, and pathological diagnosis were collected. The collected data were analyzed by univariate logistic analysis and multivariate logistic analysis. The risk factors were evaluated, and the predictive model was constructed of CLNM. Results: The multivariate logistic analysis showed that Age (p < 0.001), Gender (p < 0.001), Multifocality (p < 0.001), BRAF (p = 0.027), and Tumor size (p < 0.001) were associated with CLNM. The receiver operating characteristic curve (ROC curve) showed high efficiency with an area under the ROC (AUC) of 0.781 in the training group. The calibration curve and the calibration of the model were evaluated. The decision curve analysis (DCA) for the nomogram showed that the nomogram can provide benefits in this study. Conclusion: The predictive model of CLNM constructed and visualized based on the evaluated risk factors was confirmed to be a practical and convenient tool for clinicians to predict the CLNM in PTC.

HDAC6-specific inhibitor alleviates hashimoto's thyroiditis through inhibition of Th17 cell differentiation.

OBJECTIVE: Hashimoto's thyroiditis (HT) is one of the commonest autoimmune disorders. This study was performed to investigate the potential effect of histone deacetylase 6-specific inhibitor (HDAC6i) on Th17 cell differentiation in animal model and the underlying mechanism. METHODS: Experimental autoimmune thyroiditis (EAT) mouse model was established by subcutaneously immunization of porcine thyroglobulin (pTg) and adjuvant, and the HDACi Tubastatin A (TSA) or HDAC6i (ACY-1215) was intraperitoneally injected into mice in the following. The histological examination and immune analysis in EAT mice were carried out. Next, the CD4+ T cells were isolated from peripheral blood mononuclear cells (PBMCs) of EAT mice followed by Th17 cell differentiation assay. The associated factor levels, and the protein interaction between HDAC6 and PKM2 were examined. Subsequently, the effect of STAT3 activation on Th17 cell differentiation was explored. RESULTS: ACY-1215 or TSA treatment reduced lymphocytic infiltration and alleviated thyroid tissue injury in EAT mice. Correspondingly, either ACY-1215 or TSA treatment reduced the levels of anti-thyroglobulin (Tg), anti-thyroid peroxidase (TPO), IL-17A, and IFN-γ in the serum, decreased Th17 cell differentiation, but enhanced TGF-ß level and promoted Treg cell differentiation. In vitro, after induction of Th17 cell differentiation from CD4+ T cells, HDAC6 activity and Th17 cell differentiation were significantly decreased when treated with ACY-1215 or TSA. HDAC6 could interact with PKM2, and HDAC6 overexpression promoted the phosphorylation of STAT3 and PKM2 nuclear translocation. Furthermore, the STAT3 activator treatment reversed the effects of ACY-1215 or TSA treatment. CONCLUSION: HDAC6i suppresses Th17 cell differentiation and attenuates HT via PKM2/STAT3 axis.

Nomograms Predict Survival in Patients with Anaplastic Thyroid Carcinoma.

BACKGROUND Anaplastic thyroid carcinoma (ATC) is a very rare, highly lethal malignant cancer. Our aim in this study was to develop nomograms that predict survival in ATC patients. MATERIAL AND METHODS ATC incidence and mortality were assessed via joinpoint regression analysis of 567 ATC patients selected from the Surveillance, Epidemiology, and End Results 18 Registries Research database. Predictive models were established via univariate and multivariate Cox regression analysis of potential risk factors and used to produce nomograms. Performance of the nomograms in terms of discrimination ability and calibration was evaluated by determining the concordance index (C-index) and by generating calibration plots, respectively. RESULTS The incidence and mortality rates for ATC increased from 2000 to 2015 according to the collected data (p<0.05). Two nomograms were constructed based on 2 predictive models: nomogram 1 considered age, tumor size, and metastasis (all before surgery), and nomogram 2 considered age, tumor size, metastasis, surgery, and extrathyroidal extension (all after surgery). Both nomogram 1 (C-index, 0.6803; 95% confidence interval, 0.6517-0.7089) and nomogram 2 (C-index, 0.7064; 95% confidence interval, 0.6783-0.7345) had good discrimination ability. The validated C-index values were 0.6783 and 0.7029 for nomogram 1 and 2, respectively. The observed values were in agreement with the calibration curves. CONCLUSIONS Nomogram 1 can assist in preoperative prediction of survival time in ATC patients, whereas nomogram 2 can provide additional outcome-related information.

Upregulation of the long non-coding RNA FOXD2-AS1 is correlated with tumor progression and metastasis in papillary thyroid cancer.

BACKGROUND: Mounting evidence has shown that long non-coding RNAs (lncRNAs) play critical regulation roles in the progression of various cancers. However, the biological role and clinical value of lncRNA FOXD2-AS1 in papillary thyroid cancer (PTC) remain to be elucidated. METHODS: The expression of FOXD2-AS1 in PTC tissues and cell lines was evaluated by RT-qPCR and in situ hybridization. The association between FOXD2-AS1 expression levels and clinicopathologic features was analyzed through tissue microarray. The biological function of FOXD2-AS1 in PTC cells was determined both in vitro through CCK-8, EdU staining, colony formation and cell invasion assays and in vivo through a xenograft tumor model. Functional and pathway enrichment analysis were also conducted to analyze the molecular mechanism. RESULTS: FOXD2-AS1 was significantly upregulated in PTC tissues, and high FOXD2-AS1 expression was positively associated with malignant potential factors in PTC patients. In addition, high level of FOXD2-AS1 expression was an unfavorable independent prognostic biomarker for patients with PTC. Moreover, we found that knockdown of FOXD2-AS1 could effectively inhibit PTC cell proliferation and invasion in vitro and suppress tumor growth of PTC in vivo. Bioinformatics analysis indicated that activation of cell cycle and apoptosis pathways might be involved in the oncogenic function of FOXD2-AS1 in PTC. Moreover, we demonstrated that FOXD2-AS1 directly interacted with miR-185-5p as miRNA sponge and overexpression of FOXD2-AS1 partially reversed the suppressive effect of miR-185-5p in TPC cells. CONCLUSION: Our findings suggest FOXD2-AS1 functions as an oncogene and promotes the tumor progression and metastasis in PTC, which might serve as a promising prognostic biomarker and potential therapeutic target for PTC patients.