Carbon nanoparticle-guided intraoperative lymph node biopsy predicts the status of lymph nodes posterior to right recurrent laryngeal nerve in cN0 papillary thyroid carcinoma

Bone Marrow Mesenchymal Stem Cells with Long Non-Coding RNA-Growth Arrest Specific 5 (LncRNA-GAS5) Modification Impede the Migration and Invasion Activities of Papillary Thyroid Carcinoma Cells

The impact of bone marrow mesenchymal stem cells (BMSCs) on the behaviors of papillary thyroid carcinoma (PTC) cells and LncRNAs remains poorly understood. This study mainly explores the mechanism of LncRNA-GAS5-modified BMSCs on the behaviors of PTC cells, aiming to further elucidate PTC carcinogenesis and provide evidence for drug development. PTC cell lines were assigned into blank group, BMSCs group (co-culture with BMSCs), GAS5 group (co-culture with LncRNA-GAS5-modified BMSCs) and positive control group (cultured in the presence of 60 μg/mL β-elemene) followed by analysis of LncRNA-GAS5 expression, the number of migrating and invading PTC cells, the quantity of EMT-related markers, MMP-9 and MMP-2. LncRNA-GAS5 level was lowest in the blank group, while highest in the GAS5 group (P <0.05), followed by positive control group and BMSCs group. Moreover, the number of migrated and invaded cells was highest in the blank group, while lowest in GAS5 group (P < 0.05), followed by positive control group and BMSCs group. PTC cells exhibited the highest expression of EMT-related markers (N-cadherin and Vimentin) and MMPs but lowest E-cadherin level in blank group and positive control group. These proteins showed an opposite trend in GAS5 group and BMSCs group. Additionally, a more remarkable difference was recorded in the GAS5 group (P <0.05). LncRNA-GAS5-modified BMSCs can down-regulate Vimentin and N-cadherin while up-regulate E-cadherin, thereby restraining the expression of MMP-9 and MMP-2. In this way, the EMT process can be manipulated, leading to inhibition of PTC cells behaviors by LncRNA-GAS5-modified BMSCs, indicating that LncRNA-GAS5 might be applied as a therapeutic target for PTC.

LASSO-based machine learning models for the prediction of central lymph node metastasis in clinically negative patients with papillary thyroid carcinoma

BACKGROUND

The presence of central lymph node metastasis (CLNM) is crucial for surgical decision-making in clinical N0 (cN0) papillary thyroid carcinoma (PTC) patients. We aimed to develop and validate machine learning (ML) algorithms-based models for predicting the risk of CLNM in cN0 patients.

METHODS

A total of 1099 PTC patients with cN0 central neck from July 2019 to March 2022 at our institution were retrospectively analyzed. All patients were randomly split into the training dataset (70%) and the validation dataset (30%). Eight ML algorithms, including the Logistic Regression, Gradient Boosting Machine, Extreme Gradient Boosting (XGB), Random Forest (RF), Decision Tree, Neural Network, Support Vector Machine and Bayesian Network were used to evaluate the risk of CLNM. The performance of ML models was evaluated by the area under curve (AUC), sensitivity, specificity, and decision curve analysis (DCA).

RESULTS

We firstly used the LASSO Logistic regression method to select the most relevant factors for predicting CLNM. The AUC of XGB was slightly higher than RF (0.907 and 0.902, respectively). According to DCA, RF model significantly outperformed XGB model at most threshold points and was therefore used to develop the predictive model. The diagnostic performance of RF algorithm was dependent on the following nine top-rank variables: size, margin, extrathyroidal extension, sex, echogenic foci, shape, number, lateral lymph node metastasis and chronic lymphocytic thyroiditis.

CONCLUSION

By incorporating clinicopathological and sonographic characteristics, we developed ML-based models, suggesting that this non-invasive method can be applied to facilitate individualized prediction of occult CLNM in cN0 central neck PTC patients.

A Nomogram Based on Clinicopathological and Ultrasound Imaging Characteristics for Predicting Cervical Lymph Node Metastasis in cN0 Unilateral Papillary Thyroid Microcarcinoma

Objective: The aim of this study was to establish a practical nomogram for preoperatively predicting the possibility of cervical lymph node metastasis (CLNM) based on clinicopathological and ultrasound (US) imaging characteristics in patients with clinically node-negative (cN0) unilateral papillary thyroid microcarcinoma (PTMC) in order to determine a personal surgical volume and therapeutic strategy. Methods: A total of 269 consecutive patients diagnosed with cN0 unilateral PTMC by postoperative pathological examination from January 2018 to December 2020 were retrospectively analyzed. All the patients underwent lobectomy or thyroidectomy with routine prophylactic central lymph node dissection (CLND) and were divided into a CLNM group and a non-CLNM group. Using logistic regression, the least absolute shrinkage and selection operator (LASSO) regression analysis was applied to determine the risk factors for CLNM in patients with unilateral cN0 PTMC. A nomogram including risk-factor screening using LASSO regression for predicting the CLNM in patients with cN0 unilateral PTMC was further developed and validated. Results: Risk factors identified by LASSO regression, including age, sex, tumor size, presence of extrathyroidal extension (ETE), tumor diameter/lobe thickness (D/T), tumor location, and coexistent benign lesions, were potential predictors for CLNM in patients with cN0 unilateral PTMC. Meanwhile, age (odds ratio [OR] = 0.261, 95% CI.104-0.605; P = 0.003), sex (men: OR = 3.866; 95% CI 1.758-8.880; P < 0.001), ETE (OR = 3.821; 95% CI 1.168-13.861; P = 0.032), D/T (OR = 72.411; 95% CI 5.483-1212.497; P < 0.001), and coexistent benign lesions (OR = 3.112 95% CI 1.407-7.303; P = 0.007) were shown to be significantly related to CLNM by multivariant logistic regression. A nomogram for predicting CLNM in patients with cN0 unilateral PTMC was established based on the risk factors identified by the LASSO regression analysis. The receiver operating characteristic (ROC) curve for predicting CLNM by nomogram showed that the area under the curve (AUC) was 0.777 and exhibited an excellent consistency. Conclusions: A nomogram based on clinical and US imaging characteristics for predicting the probability of CLNM in patients with cN0 unilateral PTMC was developed, which showed a favorable predictive value and consistency. Further prospective research to observe the oncological outcomes is necessary to determine whether the nomogram could potentially guide a personalized surgical volume and surgical approach.

Development and validation of a novel 14-gene signature for predicting lymph node metastasis in papillary thyroid carcinoma

Background

There is still no reasonably accurate method of preoperatively predicting central lymph node metastasis (LNM), and it is essential to develop an effective evaluation model for predicting LNM in papillary thyroid carcinoma (PTC) patients.

Methods

PTC samples were collected from The Cancer Genome Atlas database. Candidate genes were identified as continuously upregulated or downregulated genes in the process of N0 to N1a and N1a to N1b. The least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct the predictive model for LNM. Multivariate logistic regression analysis was performed to screen the potential factors related to LNM, and a nomogram was established. The risk score of the gene signature model for predicting disease-free survival (DFS) was evaluated by Kaplan-Meier analysis.

Results

A 14-gene signature was developed by LASSO regression for predicting LNM based on 69 differential expression genes (DEGs) that were continuously upregulated or downregulated in the progress of PTC. The receiver operating characteristic (ROC) curves of the 14-gene signature predicting LNM, central LNM and lateral LNM were generated. The area under the ROC (AUC) values were 0.806 [95% confidence interval (CI): 0.7608-0.8815], 0.755 (95% CI: 0.6839-0.8263) and 0.821 (95% CI: 0.7608-0.8815). The nomogram's C-index value, including the 14-gene signature and other potential risk factors, was 0.786 (95% CI: 0.7296-0.8425), and the calibration exhibited fairly good consistency with the perfect prediction. Based on the 14-gene risk score, high-risk PTC patients had a worse DFS.

Conclusions

A novel 14-gene signature was developed for predicting LNM in PTC patients. The risk score also correlated with DFS in PTC patients.