Epithelial-mesenchymal transition (EMT), principally involving an E-cadherin to N-cadherin shift, linked to tumor invasion or metastasis, and therapeutic resistance in various human cancer. A growing body of recent evidence has supported the hypothesis that EMT play a crucial role in the invasive phenotype of gliomas. To evaluate the prognostic connotation of EMT traits in glioma, expression of E-cadherin and N-cadherin was explored in a large series of glioma patients in relation to patient survival rate. Expressions of E- and N-cadherin were examined using immunohistochemical analysis in 92 glioma cases diagnosed at our hospital. These markers expressions were also explored in 21 cases of fresh frozen glioma samples and in glioma cell lines by Western blot analysis. Expression of E-cadherin was observed in eight cases (8.7%) with weak staining intensity in the majority of the immunoreactive cases (7/8). Expression of N-cadherin was identified in 81 cases (88.0%) with high expression in 64 cases (69.5%). Fresh frozen tissue samples and glioma cell lines showed similar results by Western blot analysis. There was no significant difference in either overall survival (OS) or progression-free survival (PFS) according to E-cadherin expression (P > 0.05). Although the OS rates were not affected by N-cadherin expression levels (P = 0.138), PFS increased in the low N-cadherin expression group with marginal significance (P = 0.058). The survival gains based on N-cadherin expression levels were significantly augmented in a larger series of publicly available REMBRANDT data (P < 0.001). E- and N-cadherin, as representative EMT markers, have limited prognostic value in glioma. Nonetheless, the EMT process in gliomas may be compounded by enhanced N-cadherin expression supported by unfavorable prognostic outcomes.

Spondin-2 (SPON2) is involved in cancer progression and metastasis of many tumors; however, its role and underlying mechanism in gastric cancer are still obscure. In this study, we investigated the role of SPON2 and related signaling pathway in gastric cancer progression and metastasis. SPON2 expression levels were found to be upregulated in gastric cancer cell lines and patient tissues compared to normal gastric epithelial cells and normal controls. Furthermore, SPON2 silencing was observed to decrease cell proliferation and motility and reduce tumor growth in xenograft mice. Conversely, SPON2 overexpression was found to increase cell proliferation and motility. Subsequently, we focused on regulatory mechanism of SPON2 in gastric cancer. cDNA microarray and in vitro study showed that Notch signaling is significantly correlated to SPON2 expression. Therefore, we confirmed how Notch signaling pathway regulate SPON2 expression using Notch signaling-related transcription factor interaction and reporter gene assay. Additionally, activation of Notch signaling was observed to increase cell proliferation, migration, and invasion through SPON2 expression. Our study demonstrated that Notch signaling-mediated SPON2 upregulation is associated with aggressive progression of gastric cancer. In conclusion, we suggest upregulated SPON2 via Notch signaling as a potential target gene to inhibit gastric cancer progression.

Lymphovascular invasion (LVI) is one of the most important prognostic factors in gastric cancer as it indicates a higher likelihood of lymph node metastasis and poorer overall outcome for the patient. Despite its importance, the detection of LVI(+) in histopathology specimens of gastric cancer can be a challenging task for pathologists as invasion can be subtle and difficult to discern. Herein, we propose a deep learning-based LVI(+) detection method using H&E-stained whole-slide images. The ConViT model showed the best performance in terms of both AUROC and AURPC among the classification models (AUROC: 0.9796; AUPRC: 0.9648). The AUROC and AUPRC of YOLOX computed based on the augmented patch-level confidence score were slightly lower (AUROC: −0.0094; AUPRC: −0.0225) than those of the ConViT classification model. With weighted averaging of the patch-level confidence scores, the ensemble model exhibited the best AUROC, AUPRC, and F1 scores of 0.9880, 0.9769, and 0.9280, respectively. The proposed model is expected to contribute to precision medicine by potentially saving examination-related time and labor and reducing disagreements among pathologists.

Diffuse large B‐cell lymphoma (DLBCL) is a heterogeneous and prevalent subtype of aggressive non‐Hodgkin lymphoma that poses diagnostic and prognostic challenges, particularly in predicting drug responsiveness. In this study, we used digital pathology and deep learning to predict responses to immunochemotherapy in patients with DLBCL. We retrospectively collected 251 slide images from 216 DLBCL patients treated with rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R‐CHOP), with their immunochemotherapy response labels. The digital pathology images were processed using contrastive learning for feature extraction. A multi‐modal prediction model was developed by integrating clinical data and pathology image features. Knowledge distillation was employed to mitigate overfitting on gigapixel histopathology images to create a model that predicts responses based solely on pathology images. Based on the importance derived from the attention mechanism of the model, we extracted histological features that were considered key textures associated with drug responsiveness. The multi‐modal prediction model achieved an impressive area under the ROC curve of 0.856, demonstrating significant associations with clinical variables such as Ann Arbor stage, International Prognostic Index, and bulky disease. Survival analyses indicated their effectiveness in predicting relapse‐free survival. External validation using TCGA datasets supported the model's ability to predict survival differences. Additionally, pathology‐based predictions show promise as independent prognostic indicators. Histopathological analysis identified centroblastic and immunoblastic features to be associated with treatment response, aligning with previous morphological classifications and highlighting the objectivity and reproducibility of artificial intelligence‐based diagnosis. This study introduces a novel approach that combines digital pathology and clinical data to predict the response to immunochemotherapy in patients with DLBCL. This model shows great promise as a diagnostic and prognostic tool for clinical management of DLBCL. Further research and genomic data integration hold the potential to enhance its impact on clinical practice, ultimately improving patient outcomes.

The gut microbiota has pivotal roles in metabolic homeostasis and modulation of the intestinal environment. Notably, the administration ofLactobacillusspp. ameliorates diet-induced obesity in humans and mice. However, the mechanisms through whichLactobacillusspp. control host metabolic homeostasis remain unclear. Accordingly, in this study, we evaluated the physiological roles ofLactobacillus fermentumin controlling metabolic homeostasis in diet-induced obesity. Our results demonstrated thatL. fermentum-potentiated oxidative phosphorylation in adipose tissue, resulting in increased energy expenditure to protect against diet-induced obesity. Indeed, oral administration ofL. fermentumLM1016 markedly ameliorated glucose clearance and fatty liver in high-fat diet-fed mice. Moreover, administration ofL. fermentumLM1016 markedly decreased inflammation and increased oxidative phosphorylation in gonadal white adipose tissue, as demonstrated by transcriptome analysis. Finally, metabolome analysis showed that metabolites derived fromL. fermentumLM1016-attenuated adipocyte differentiation and inflammation in 3T3-L1 preadipocytes. These pronounced metabolic improvements suggested that the application ofL. fermentumLM1016 could have clinical applications for the treatment of metabolic syndromes, such as diet-induced obesity.

Aims: This study assessed the expression and clinical relevance of cancer-asssociated fibroblast (CAF)-related biomarkers in brain metastasis (BM). Moreover, molecular characterization of patient-derived primary CAFs and normal fibroblasts (NFs) was performed.