GINS1 promotes ZEB1-mediated epithelial-mesenchymal transition and tumor metastasis via ß-catenin signaling in hepatocellular carcinoma.

GINS1 regulates DNA replication in the initiation and elongation phases and plays an important role in the progression of various malignant tumors. However, the role of GINS1 in hepatocellular carcinoma (HCC) remains largely unclear. In this study, we investigated the role and underlying mechanisms of GINS1 in contributing to HCC metastasis. We found that GINS1 was significantly upregulated in HCC tissues and cell lines, especially in HCC tissues with vascular invasion and HCC cell lines with highly metastatic properties. Additionally, high expression of GINS1 was positively correlated with the progressive clinical features of HCC patients, including tumor number (multiple), tumor size (>5 cm), advanced tumor stage, vascular invasion and early recurrence, suggesting that GINS1 upregulation was greatly involved in HCC metastasis. Moreover, Kaplan-Meier survival analysis revealed that high GINS1 expression predicted a poor prognosis. Both in vitro and in vivo, silencing of GINS1 inhibited proliferation, migration, invasion and metastasis, while overexpression of GINS1 induced opposite effects. Mechanistically, we found that ZEB1 was a crucial regulator of GINS1-induced epithelial-mesenchymal transition (EMT), and GINS1 promoted EMT and tumor metastasis through ß-catenin signaling. Overall, the present study demonstrated that GINS1 promoted ZEB1-mediated EMT and tumor metastasis via ß-catenin signaling in HCC.

Machine learning techniques based on 18F-FDG PET radiomics features of temporal regions for the classification of temporal lobe epilepsy patients from healthy controls.

Background: This study aimed to investigate the clinical application of 18F-FDG PET radiomics features for temporal lobe epilepsy and to create PET radiomics-based machine learning models for differentiating temporal lobe epilepsy (TLE) patients from healthy controls. Methods: A total of 347 subjects who underwent 18F-FDG PET scans from March 2014 to January 2020 (234 TLE patients: 25.50 ± 8.89 years, 141 male patients and 93 female patients; and 113 controls: 27.59 ± 6.94 years, 48 male individuals and 65 female individuals) were allocated to the training (n = 248) and test (n = 99) sets. All 3D PET images were registered to the Montreal Neurological Institute template. PyRadiomics was used to extract radiomics features from the temporal regions segmented according to the Automated Anatomical Labeling (AAL) atlas. The least absolute shrinkage and selection operator (LASSO) and Boruta algorithms were applied to select the radiomics features significantly associated with TLE. Eleven machine-learning algorithms were used to establish models and to select the best model in the training set. Results: The final radiomics features (n = 7) used for model training were selected through the combinations of the LASSO and the Boruta algorithms with cross-validation. All data were randomly divided into a training set (n = 248) and a testing set (n = 99). Among 11 machine-learning algorithms, the logistic regression (AUC 0.984, F1-Score 0.959) model performed the best in the training set. Then, we deployed the corresponding online website version (https://wane199.shinyapps.io/TLE_Classification/), showing the details of the LR model for convenience. The AUCs of the tuned logistic regression model in the training and test sets were 0.981 and 0.957, respectively. Furthermore, the calibration curves demonstrated satisfactory alignment (visually assessed) for identifying the TLE patients. Conclusion: The radiomics model from temporal regions can be a potential method for distinguishing TLE. Machine learning-based diagnosis of TLE from preoperative FDG PET images could serve as a useful preoperative diagnostic tool.

Laparoscopic Posterior Radical Antegrade Modular Pancreatosplenectomy for Distal Pancreatic Carcinoma.

Distal pancreatic carcinoma is a highly malignant tumor with strong invasiveness, often growing to the edge of the pancreas and penetrating the pancreatic capsule to infiltrate surrounding tissues. In conventional distal pancreatosplenectomy (DPS), tumor cells are prone to spread along the direction of blood and lymphatic reflux due to surgical compression. Additionally, inflammation makes it challenging to achieve R0 resection, leading to a lower patient survival rate. To address these limitations, radical antegrade modular pancreatosplenectomy (RAMPS) was developed, emphasizing deeper excision, including the left anterior renal fascia, the left anterior renal adipose sac, and even the left adrenal gland, to improve the R0 resection rate. With the advancement of minimally invasive surgical techniques, laparoscopic RAMPS (L-RAMPS) is being considered technically safe and feasible in oncology. However, due to technical difficulties and a lack of supporting evidence for clinical application, only a few institutions are currently conducting L-RAMPS. In this context, this article presents detailed techniques for laparoscopic posterior radical antegrade modular pancreatosplenectomy (L-pRAMPS), offering promise for future clinical applications.