miR-22 negatively regulating NOD-like receptor protein 3 gene in the proliferation, invasion, and migration of malignant melanoma cells.

Introduction: Malignant melanoma (MM) is a highly aggressive skin tumour. Aim: To investigate whether miR-22 is involved in the proliferation, invasion, and migration of melanoma cells (MCs) by negatively regulating NOD-like receptor protein 3 (NLRP3) gene. Material and methods: Human MCs (WM239a) and human epidermal melanocytes (HEM) were used as study material. The expression levels of miR-22 and NLRP3 were detected by qRT-PCR. The expression of NLRP3 protein was determined by Western blot (WB) analysis. The effects of miR-22 and NLRP3 on the proliferation, invasion, and migration of MCs were evaluated by cell counting kit-8 (CCK-8), Transwell cell invasion assay, and scratch assay. Results: The expression of miR-22 was clearly lower in WM239a than in HEM. Up-regulation of miR-22 expression in WM239a clearly raised the expression of miR-22, Caspase-1, and E-cadherin and the apoptotic rate of WM239a; however, the levels of interleukin-1ß (IL-1ß) and NLRP3, cell proliferation activity, invasion and migration ability were clearly decreased. The negative regulation of NLRP3 by miR-22 may play a major role in activities of MM. Conclusions: Further studies will help to reveal the molecular details of this regulatory mechanism and provide new therapeutic strategies.

Machine learning models predict lymph node metastasis in patients with stage T1-T2 esophageal squamous cell carcinoma.

Background: For patients with stage T1-T2 esophageal squamous cell carcinoma (ESCC), accurately predicting lymph node metastasis (LNM) remains challenging. We aimed to investigate the performance of machine learning (ML) models for predicting LNM in patients with stage T1-T2 ESCC. Methods: Patients with T1-T2 ESCC at three centers between January 2014 and December 2019 were included in this retrospective study and divided into training and external test sets. All patients underwent esophagectomy and were pathologically examined to determine the LNM status. Thirty-six ML models were developed using six modeling algorithms and six feature selection techniques. The optimal model was determined by the bootstrap method. An external test set was used to further assess the model's generalizability and effectiveness. To evaluate prediction performance, the area under the receiver operating characteristic curve (AUC) was applied. Results: Of the 1097 included patients, 294 (26.8%) had LNM. The ML models based on clinical features showed good predictive performance for LNM status, with a median bootstrapped AUC of 0.659 (range: 0.592, 0.715). The optimal model using the naive Bayes algorithm with feature selection by determination coefficient had the highest AUC of 0.715 (95% CI: 0.671, 0.763). In the external test set, the optimal ML model achieved an AUC of 0.752 (95% CI: 0.674, 0.829), which was superior to that of T stage (0.624, 95% CI: 0.547, 0.701). Conclusions: ML models provide good LNM prediction value for stage T1-T2 ESCC patients, and the naive Bayes algorithm with feature selection by determination coefficient performed best.

The minimum number of examined lymph nodes for accurate nodal staging and optimal survival of stage T1-2 esophageal squamous cell carcinoma: A retrospective multicenter cohort with SEER database validation.

BACKGROUND: The extent of lymphadenectomy during esophagectomy remains controversial for patients with T1-2 ESCC. The aim of this study was to identify the minimum number of examined lymph node (ELN) for accurate nodal staging and overall survival (OS) of patients with T1-2 esophageal squamous cell carcinoma (ESCC). MATERIALS AND METHODS: Patients with T1-2 ESCC from three institutes between January 2011 and December 2020 were retrospectively reviewed. The associations of ELN count with nodal migration and OS were evaluated using multivariable models, and visualized by using locally weighted scatterplot smoothing (LOWESS). Chow test was used to determine the structural breakpoints of ELN count. External validation in the SEER database was performed. RESULTS: In total, 1537 patients were included. Increased ELNs was associated with an increased likelihood of having positive nodal disease and incremental OS. The minimum numbers of ELNs for accurate nodal staging and optimal survival were 14 and 18 with validation in the SEER database (n = 519), respectively. The prognostic prediction ability of N stage was improved in the group with ≥14 ELNs compared with those with fewer ELNs (iAUC, 0.70 (95%CI 0.66-0.74) versus 0.61(95%CI 0.57-0.65)). The higher prognostic value was found for patients with ≥18 ELNs than those with <18 ELNs (iAUC, 0.78 (95%CI 0.74-0.82) versus 0.73 (95%CI 0.7-0.77)). CONCLUSION: The minimum numbers of ELNs for accurate nodal staging and optimal survival of stage T1-2 ESCC patients were 14 and 18, respectively.

Clinical nomogram for lymph node metastasis in pathological T1 esophageal squamous cell carcinoma: a multicenter retrospective study.

BACKGROUND: Endoscopic resection is increasingly used to treat pathological T1 (pT1) esophageal cancer (EC) patients. However, the procedures are limited by lymph node metastasis (LNM) and remain controversial. We aimed to construct a nomogram to predict the risk of LNM in patients with pT1 esophageal squamous cell carcinoma (ESCC). METHODS: A total of 243 patients with pT1 ESCC who underwent esophagectomy and lymph node dissection at two different institutes between February 2013 and June 2019 were analyzed retrospectively. Patients were categorized into the negative group and the positive group according to whether there was LNM. Risk factors for LNM were evaluated by univariate and multivariate analyses. The nomogram was used to estimate the individual risk of LNM. RESULTS: Forty-six (18.9%) of the 243 patients with pT1 ESCC exhibited LNM. The LNM rate in patients with stage T1a disease was 5.7% (5/88), and the rate in patients with stage T1b disease was 26.5% (41/155). Multivariable logistic regression analysis showed that tumor differentiation [odds ratio (OR) =1.942, 95% confidence interval (CI): 1.067-3.536, P=0.030], the T1 sub-stage (OR =4.750, 95% CI: 1.658-13.611, P=0.004), the preoperative alanine aminotransferase/aspartate aminotransferase ratio (LSR) (OR =5.371, 95% CI: 1.676-17.210, P=0.005), and the high-density lipoprotein cholesterol (HDL-C) level (OR =5.894, 95% CI: 1.917-18.124, P=0.002) were independent risk factors for LNM. The nomogram had relatively high accuracy, with an area under the receiver operating characteristic curve (AUC) of 0.803 (95% CI: 0.732-0.873). The calibration curve showed that the predicted probability of LNM was in good agreement with the actual probability. CONCLUSIONS: Clinicopathological and hematological parameters of tumor differentiation, the T1 sub-stage, the preoperative LSR, and the HDL-C level may predict the risk of LNM in T1 ESCC. The risk of LNM can be predicted by the nomogram.

Circ_0058063 upregulates GLUT1 expression and promotes glucose-uptake in esophageal squamous-cell carcinomas.

BACKGROUND: CirRNA Circ_0058063 has been proven as an oncogene in bladder cancer, while its involvement in esophageal squamous-cell carcinomas (ESCC) is unknown. This study aimed to investigate the role of Circ_0058063 in ESCC. METHODS: Paired ESCC and non-tumor tissues were collected from ESCC patients and gene expression was analyzed by quantitative reverse transcription polymerase chain reaction (RT-qPCR). Gene interactions were analyzed by overexpression experiment. Glucose uptake was analyzed by glucose uptake assay. Cell proliferation was analyzed by cell proliferation assay. RESULTS: We found that Circ_0058063 was upregulated in ESCC and positively correlated with GLUT1 mRNA. It is known that GLUT1 plays critical roles in glucose transportation and glucose supports the Warburg Effect as the major metabolic precursor. In ESCC cells, Circ_0058063 and GLUT1 overexpression both promoted glucose uptake. In ECSS cells, Circ_0058063 overexpression resulted in the upregulated, while Circ_0058063 knockdown resulted in downregulated GLUT1. In cell proliferation assay, Circ_0058063 and GLUT1 overexpression resulted in the increased, while Circ_0058063 knockdown resulted in the decreased rate of ESCC cell proliferation. Moreover, GLUT1 overexpression reduced the effects of Circ_0058063 knockdown. CONCLUSIONS: Circ_0058063 upregulates GLUT1 expression and promotes glucose-uptake in ESCC to promote cell proliferation.