The apelin‑apelin receptor signaling pathway in fibroblasts is involved in tumor growth via p53 expression of cancer cells

Cancer-associated fibroblasts (CAFs) are pivotal in tumor progression. TP53-deficiency in cancer cells is associated with robust stromal activation. The apelin-apelin receptor (APJ) system has been implicated in suppressing fibroblast-to-myofibroblast transition in non-neoplastic organ fibrosis. The present study aimed to elucidate the oncogenic role of the apelin-APJ system in tumor fibroblasts. APJ expression and the effect of APJ suppression in fibroblasts were investigated for p53 status in cancer cells using human cell lines (TP53-wild colon cancer, HCT116, and Caco-2; TP53-mutant colon cancer, SW480, and DLD-1; and colon fibroblasts, CCD-18Co), resected human tissue samples of colorectal cancers, and immune-deficient nude mouse xenograft models. The role of exosomes collected by ultracentrifugation were also analyzed as mediators of p53 expression in cancer cells and APJ expression in fibroblasts. APJ expression in fibroblasts co-cultured with p53-suppressed colon cancer cells (HCT116sh p53 cells) was significantly lower than in control colon cancer cells (HCT116sh control cells). APJ-suppressed fibroblasts treated with an antagonist or small interfering RNA showed myofibroblast-like properties, including increased proliferation and migratory abilities, via accelerated phosphorylation of Sma- and Mad-related protein 2/3 (Smad2/3). In addition, xenografts of HCT116 cells with APJ-suppressed fibroblasts showed accelerated tumor growth. By contrast, apelin suppressed the upregulation of phosphorylated Smad2/3 in fibroblasts. MicroRNA 5703 enriched in exosomes derived from HCT116sh p53 cells inhibited APJ expression, and inhibition of miR-5703 diminished APJ suppression in fibroblasts caused by cancer cells. APJ suppression from a specific microRNA in cancer cell-derived exosomes induced CAF-like properties in fibroblasts. Thus, the APJ system in fibroblasts in the tumor microenvironment may be a promising therapeutic target.Saiki H., Hayashi Y., Yoshii S., et al. The apelin‑apelin receptor signaling pathway in fibroblasts is involved in tumor growth via p53 expression of cancer cells. International Journal of Oncology 63, 139 (2023); https://doi.org/10.3892/ijo.2023.5587

A novel artificial intelligence-based endoscopic ultrasonography diagnostic system for diagnosing the invasion depth of early gastric cancer

The version of record of this article, first published in Journal of Gastroenterology, is available online at Publisher’s website: https://doi.org/10.1007/s00535-024-02102-1.Background: We developed an artificial intelligence (AI)-based endoscopic ultrasonography (EUS) system for diagnosing the invasion depth of early gastric cancer (EGC), and we evaluated the performance of this system. Methods: A total of 8280 EUS images from 559 EGC cases were collected from 11 institutions. Within this dataset, 3451 images (285 cases) from one institution were used as a development dataset. The AI model consisted of segmentation and classification steps, followed by the CycleGAN method to bridge differences in EUS images captured by different equipment. AI model performance was evaluated using an internal validation dataset collected from the same institution as the development dataset (1726 images, 135 cases). External validation was conducted using images collected from the other 10 institutions (3103 images, 139 cases). Results: The area under the curve (AUC) of the AI model in the internal validation dataset was 0.870 (95% CI: 0.796–0.944). Regarding diagnostic performance, the accuracy/sensitivity/specificity values of the AI model, experts (n = 6), and nonexperts (n = 8) were 82.2/63.4/90.4%, 81.9/66.3/88.7%, and 68.3/60.9/71.5%, respectively. The AUC of the AI model in the external validation dataset was 0.815 (95% CI: 0.743–0.886). The accuracy/sensitivity/specificity values of the AI model (74.1/73.1/75.0%) and the real-time diagnoses of experts (75.5/79.1/72.2%) in the external validation dataset were comparable. Conclusions: Our AI model demonstrated a diagnostic performance equivalent to that of experts

A machine learning model for predicting the lymph node metastasis of early gastric cancer not meeting the endoscopic curability criteria

The version of record of this article, first published in Gastric Cancer, is available online at Publisher’s website: https://doi.org/10.1007/s10120-024-01511-8.Background: We developed a machine learning (ML) model to predict the risk of lymph node metastasis (LNM) in patients with early gastric cancer (EGC) who did not meet the existing Japanese endoscopic curability criteria and compared its performance with that of the most common clinical risk scoring system, the eCura system. Methods: We used data from 4,042 consecutive patients with EGC from 21 institutions who underwent endoscopic submucosal dissection (ESD) and/or surgery between 2010 and 2021. All resected EGCs were histologically confirmed not to satisfy the current Japanese endoscopic curability criteria. Of all patients, 3,506 constituted the training cohort to develop the neural network-based ML model, and 536 constituted the validation cohort. The performance of our ML model, as measured by the area under the receiver operating characteristic curve (AUC), was compared with that of the eCura system in the validation cohort. Results: LNM rates were 14% (503/3,506) and 7% (39/536) in the training and validation cohorts, respectively. The ML model identified patients with LNM with an AUC of 0.83 (95% confidence interval, 0.76–0.89) in the validation cohort, while the eCura system identified patients with LNM with an AUC of 0.77 (95% confidence interval, 0.70–0.85) (P = 0.006, DeLong’s test). Conclusions: Our ML model performed better than the eCura system for predicting LNM risk in patients with EGC who did not meet the existing Japanese endoscopic curability criteria. Mini-abstract: We developed a neural network-based machine learning model that predicts the risk of lymph node metastasis in patients with early gastric cancer who did not meet the endoscopic curability criteria

A novel role for Helicobacter pylori cytotoxin-associated gene A in negative regulation of autophagy in human gastric cells

Abstract Background Autophagy plays an important role in carcinogenesis and tumor progression in many cancers, including gastric cancer. Cytotoxin-associated gene A (CagA) is a well-known virulent factor in Helicobacter pylori (H. pylori) infection that plays a critical role in gastric inflammation and gastric cancer development. However, its role in autophagy during these processes remains unclear. Therefore, we aimed to clarify the role of CagA in autophagy in CagA-related inflammation. Methods We evaluated the autophagic index of AGS cells infected with wild-type cagA-positive H. pylori (Hp-WT) and cagA-knockout H. pylori (Hp-ΔcagA) and rat gastric mucosal (RGM1) cells transfected with CagA genes. To identify the mechanisms underlying the down regulation of autophagy in AGS cells infected with H. pylori, we evaluated protein and mRNA expression levels of autophagy core proteins using western blotting and quantitative reverse transcription-polymerase chain reaction (RT-PCR). To determine whether autophagy induced the expression of the pro-inflammatory mediator, cyclooxygenase-2 (COX-2), we evaluated COX-2 expression in AGS cells treated with an autophagy inducer and inhibitor and infected with H. pylori. In addition, we evaluated whether COX-2 protein expression in AGS cells influenced beclin-1 (BECN1) expression with si-RNA transfection when infected with H. pylori. Results Autophagic flux assay using chloroquine showed that autophagy in AGS cells was significantly suppressed after H. pylori infection. The autophagic index of AGS cells infected with Hp-WT was decreased significantly when compared with that in AGS cells infected with Hp-ΔcagA. The autophagic index of RGM1 cells transfected with CagA was lower, suggesting that CagA inhibits autophagy. In addition, BECN1 expression levels in AGS cells infected with Hp-WT were reduced compared to those in AGS cells infected with Hp-ΔcagA. Furthermore, COX-2 expression in AGS cells infected with H. pylori was controlled in an autophagy-dependent manner. When AGS cells were transfected with small interfering RNA specific for BECN1 and infected with Hp-WT and Hp-ΔcagA, COX-2 was upregulated significantly in cells infected with Hp-ΔcagA. Conclusions In conclusion, the H. pylori CagA protein negatively regulated autophagy by downregulating BECN1. CagA-induced autophagy inhibition may be a causative factor in promoting pro-inflammatory mediator production in human gastric epithelial cells