Prophylactic CLND may be performed in PTMC patients with clinically uninvolved central lymph nodes but with high risk factors; multicenter studies with long-term follow-up are recommended to better understand the risk factors and surgical management for central nodes in PTMC.

Long non-coding RNAs (lncRNAs) participate in cancer cell tumorigenesis, cell cycle control, migration, proliferation, apoptosis, metastasis and drug resistance. The BRAF-activated non-coding RNA (BANCR) functions as both an oncogene and a tumor suppressor. Here, we investigated BANCR's role in papillary thyroid carcinoma (PTC) by assessing BANCR levels in PTC and matched normal thyroid epithelial tissues from 92 patients using qRT-PCR. We also used lentiviral vectors to establish PTC cell lines to investigate the effects of BANCR overexpression on cancer cell proliferation, apoptosis, migration and invasion. Our results indicate BANCR levels are lower in PTC tumor tissues than control tissues. Decreased BANCR levels correlate with tumor size, the presence of multifocal lesions and advanced PTC stage. BANCR overexpression reduced PTC cell proliferation and promoted apoptosis, which inhibited metastasis. It also inactivated ERK1/2 and p38, and this effect was enhanced by treatment with the MEK inhibitor U0126. Finally, BANCR overexpression dramatically inhibited tumor growth from PTC cells in xenograft mouse models. These results suggest BANCR inhibits tumorigenesis in PTC and that BANCR levels may be used as a novel prognostic marker.

Epigenetic abnormalities as well as genetic abnormalities may play a vital role in the tumorigenesis of papillary thyroid cancer (PTC). The present study aimed to analyze the function and methylation status of the HOXD10 gene in PTC and aimed to identify relationships between HOXD10 methylation, HOXD10 expression, BRAF mutation and clinicopathological characteristics of PTC. A total of 152 PTC patients were enrolled in the present study. The methylation status of the HOXD10 promoter was analyzed by quantitative methylation-specific polymerase chain reaction (Q-MSP). BRAFV600E mutation status was analyzed by polymerase chain reaction (PCR) followed by DNA sequencing. HOXD10 mRNA expression level was analyzed by real-time polymerase chain reaction (RT-PCR). 5-Aza-2-deoxycytidine (5-Aza) treatment was performed in 4 PTC cell lines to observe the change in HOXD10 expression. Transwell, cell cycle and apoptosis assays were then performed in an HOXD10-overexpressing PTC cell line. Furthermore, we analyzed the associations between HOXD10 methylation, HOXD10 expression, BRAF mutation and clinicopathological characteristics in PTC. Overexpression of HOXD10 suppressed the migration of PTC cells, and promoted cell apoptosis. Q-MSP showed that methylation levels of the HOXD10 promoter were significantly higher in PTC tissues than levels in the adjacent normal thyroid tissues (P=0.02). In addition, expression of HOXD10 was decreased in the PTC cell lines and PTC tissues compared with that noted in the adjacent normal thyroid tissues (P=0.008). However, BRAFV600E mutation was detected in 42.1% of PTC patients enrolled. In addition, the BRAF mutation status was associated with the methylation and expression level of HOXD10 in PTC. We then observed that 5-Aza treatment could revert the expression of HOXD10 in PTC cell lines. Moreover, the hypermethylation of HOXD10 was associated with invasion of the primary tumor and age >45. In conclusion, the HOXD10 gene may act as a tumor suppressor in PTC. The aberrant hypermethylation and decreased expression of the HOXD10 gene were shown in PTC patients, particularly in those with BRAFV600E mutation. The epigenetic suppression of the HOXD10 gene may play a role in the tumorigenesis of PTC, and it is a prospective biomarker for the diagnosis and prognosis of PTC.

The return level estimation is an essential topic in studying spatial extremes for environmental data. Recently, various models for spatial extremes have emerged, which generally yield different estimates for return levels, given the same data. In the meantime, several approaches that obtain confidence intervals (CIs) for return levels have arisen, and the results from different approaches can also largely disagree. These pose natural questions for assessing different return level estimation methods and different CI derivation approaches. In this article, we compare an array of popular models for spatial extremes in return level estimation, as well as three approaches in CI derivation, through extensive Monte Carlo simulations. Our results show that in general, max‐stable models yield return level estimates with similar mean squared error, and the spatial generalized extreme value model also provides comparable estimates. The bootstrap method is recommended for max‐stable models to compute the CI, and the profile likelihood CI works well for spatial generalized extreme value. We also evaluate the methods for return level interpolation at unknown spatial locations and find that kriging of marginal return level estimates performs as well as max‐stable models.