Nanoclay Drug-Delivery System Loading Potassium Iodide Promotes Endocytosis and Targeted Therapy in Anaplastic Thyroid Cancer.

The rapid proliferative biological behavior of primary foci of anaplastic thyroid cancer (ATC) makes it a lethal tumor. According to the specific iodine uptake capacity of thyroid cells and enhanced endocytosis of ATC cells, we designed a kind of nanoclay drug-loading system and showed a promising treatment strategy for ATC. Introducing potassium iodide (KI) improves the homoaggregation of clay nanoparticles and then affects the distribution of nanoparticles in vivo, which makes KI@DOX-KaolinMeOH enriched almost exclusively in thyroid tissue. Simultaneously, the improvement of dispersibility of KI@DOX-KaolinMeOH changes the target uptake of ATC cells by improving the endocytosis and nanoparticle-induced autophagy, which regulate the production of autolysosomes and autophagy-enhanced chemotherapy, eventually contributing to a tumor inhibition rate of more than 90% in the primary foci of ATC. Therefore, this facile strategy to improve the homoaggregation of nanoclay by introducing KI has the potential to become an advanced drug delivery vehicle in ATC treatment.

A Prognostic Model of Differentiated Thyroid Cancer Based on Up-Regulated Glycolysis-Related Genes.

Objective: This study aims to identify reliable prognostic biomarkers for differentiated thyroid cancer (DTC) based on glycolysis-related genes (GRGs), and to construct a glycolysis-related gene model for predicting the prognosis of DTC patients. Methods: We retrospectively analyzed the transcriptomic profiles and clinical parameters of 838 thyroid cancer patients from 6 public datasets. Single factor Cox proportional risk regression analysis and Least Absolute Shrinkage and Selection Operator (LASSO) were applied to screen genes related to prognosis based on 2528 GRGs. Then, an optimal prognostic model was developed as well as evaluated by Kaplan-Meier and ROC curves. In addition, the underlying molecular mechanisms in different risk subgroups were also explored via The Cancer Genome Atlas (TCGA) Pan-Cancer study. Results: The glycolysis risk score (GRS) outperformed conventional clinicopathological features for recurrence-free survival prediction. The GRS model identified four candidate genes (ADM, MKI67, CD44 and TYMS), and an accurate predictive model of relapse in DTC patients was established that was highly correlated with prognosis (AUC of 0.767). In vitro assays revealed that high expression of those genes increased DTC cancer cell viability and invasion. Functional enrichment analysis indicated that these signature GRGs are involved in remodelling the tumour microenvironment, which has been demonstrated in pan-cancers. Finally, we generated an integrated decision tree and nomogram based on the GRS model and clinicopathological features to optimize risk stratification (AUC of the composite model was 0.815). Conclusions: The GRG signature-based predictive model may help clinicians provide a prognosis for DTC patients with a high risk of recurrence after surgery and provide further personalized treatment to decrease the chance of relapse.

Insights into the Association Between QSER1 and M2 Macrophages and Remarkable Malignancy Characteristics in Hepatocellular Carcinoma.

PURPOSE: Glutamine and serine rich 1 (QSER1), as a DNA methylation modulator, play a crucial role in transforming tumor cells. Previous studies have shown that QSER1 plays a role in regulating the progression of various malignancies and that QSER1 dysfunction is connected with precancerous lesions of hepatocellular carcinoma (HCC) as well as HCC prognosis. However, little is known about the detailed contribution of QSER1 in HCC. PATIENTS AND METHODS: Various statistical methods such as Kaplan-Meier method, AUC analysis, GSEA, and immune-infiltration analysis were used to evaluate the relationship between QSER1 expression and clinical features, prognostic factors, and potential functional mechanisms of QSER1. RESULTS: QSER1 expression was negatively correlated with clinicopathological features (clinical stage, pathological grade, TP53 mutation, lymph node metastasis) and clinical outcome (overall survival versus recurrence). Functional enrichment analysis further suggested that QSER1 is involved in multiple pathways related to DNA replication and tumor immunity. TIMER analysis indicated that high QSER1 expression was significantly associated with higher macrophage infiltration and poorer macrophage-related outcomes. In particular, QSER1 was significantly more associated with M2 macrophages than M1 macrophages. CONCLUSION: Overall, elevated QSER1 is a potential prognostic marker for HCC and is associated with immune infiltration in HCC.

A Prediction Model for Contralateral Central Neck Lymph Node Metastases in Unilateral Papillary Thyroid Cancer.

The health problems caused by the frequent relapse of papillary thyroid carcinoma (PTC) remain a worldwide concern since the morbidity rate of PTC ranks the highest among thyroid cancers. Residues from contralateral central lymph node metastases (con-CLNM) are the key reason for persistence or recurrence of unilateral papillary thyroid carcinoma (uni-PTC); however, the ability to assess the status of con-CLNM in uni-PTC patients is limited. To clarify the risk factors of con-CLNM, a total of 250 patients with uni-PTC who underwent total thyroidectomy and bilateral central lymph node dissection were recruited in this study. We compared the clinical, sonographic, and pathological characteristics of patients with con-CLNM to those without con-CLNM and established a nomogram for con-CLNM in uni-PTC. We found that male sex, without Hashimoto's thyroiditis, present capsular invasion, with ipsilateral lateral lymph node metastases, and the ratio of ipsilateral central lymph node metastases ≥0.16 were independent con-CLNM predictors of uni-PTC (ORs: 2.797, 0.430, 2.538, 2.202, and 26.588; 95% CIs: 1.182-6.617, 0.211-0.876, 1.223-5.267, 1.064-4.557, and 7.596-93.069, respectively). Additionally, a preoperative nomogram for the prediction of con-CLNM based on these risk factors showed good discrimination (C-index 0.881; 95% CI: 0.840-0.923; sensitivity 85.3%; specificity 76.0%) and good agreement via the calibration plot. Our study provided a way to quantitatively and accurately predict whether con-CLNM occurred in patients with uni-PTC, which may guide surgeons to evaluate the nodal status and perform tailored therapeutic central lymph node dissection.