The role of the gastric mucosal microbiome in Helicobacter pylori-negative gastric cancer (GC) remains unclear. Therefore, we aimed to characterize the microbial alterations and host inflammatory cytokine responses in H. pylori-negative GC. Gastric mucosal samples were obtained from 137 H. pylori-negative patients with GC (n = 45) and controls (chronic gastritis or intestinal metaplasia, n = 92). We performed 16S rRNA gene sequencing (n = 67), a quantitative reverse transcription-polymerase chain reaction to determine the relative mRNA expression levels of TNF (tumor necrosis factor), IL1B (interleukin 1 beta), IL6 (interleukin 6), CXCL8 (C-X-C motif chemokine ligand 8), IL10 (interleukin 10), IL17A (interleukin 17A), TGFB1 (transforming growth factor beta 1) (n = 113), and the correlation analysis between sequencing and expression data (n = 47). Gastric mucosal microbiota in patients with GC showed reduced diversity and a significantly different composition compared to that of the controls. Lacticaseibacillus was significantly enriched, while Haemophilus and Campylobacter were depleted in the cancer group compared to the control group. These taxa could distinguish the two groups in a random forest algorithm. Moreover, the combined relative abundance of these taxa, a GC microbiome index, significantly correlated with gastric mucosal IL1B expression, which was elevated in the cancer group. Overall, altered gastric mucosal microbiota was found to be associated with increased mucosal IL1B expression in H. pylori-negative GC.

BACKGROUND/AIMS: While DNA methylation and gastric microbiome are each associated with gastric cancer (GC), their combined role in predicting GC remains unclear. This study investigated the potential of a combined DNA methylation and gastric microbiome signature to predict Helicobacter pylori-negative GC. METHODS: In this case-control study, we conducted quantitative methylation-specific polymerase chain reaction to measure the methylation levels of DKK3, SFRP1, EMX1, NKX6-1, MIR124-3, and TWIST1 in the gastric mucosa from 75 H. pylori-negative patients, including chronic gastritis (CG), intestinal metaplasia (IM), and GC. A combined analysis of DNA methylation and gastric microbiome, using 16S rRNA gene sequencing, was performed in 30 of 75 patients. RESULTS: The methylation levels of DKK3, SFRP1, EMX1, MIR124-3, and TWIST1 were significantly higher in patients with GC than in controls (all q<0.05). MIR124-3 and TWIST1 methylation levels were higher in patients with IM than those with CG and also in those with GC than in those with IM (all q<0.05). A higher methylation level of TWIST1 was an independent predictor for H. pylori-negative GC after adjusting for age, sex, and atrophy (odds ratio [OR], 15.15; 95% confidence interval [CI], 1.58 to 145.46; p=0.018). The combination of TWIST1 methylation and GC microbiome index (a microbiome marker) was significantly associated with H. pylori-negative GC after adjusting for age, sex, and atrophy (OR, 50.00; 95% CI, 1.69 to 1,476; p=0.024). CONCLUSIONS: The combination of TWIST1 methylation and GC microbiome index may offer potential as a biomarker for predicting H. pylori-negative GC.

BACKGROUND/AIMS: Risk prediction models using a deep neural network (DNN) have not been reported to predict the risk of advanced colorectal neoplasia (ACRN). The aim of this study was to compare DNN models with simple clinical score models to predict the risk of ACRN in colorectal cancer screening. METHODS: Databases of screening colonoscopy from Kangbuk Samsung Hospital (n=121,794) and Kyung Hee University Hospital at Gangdong (n=3,728) were used to develop DNN-based prediction models. Two DNN models, the Asian-Pacific Colorectal Screening (APCS) model and the Korean Colorectal Screening (KCS) model, were developed and compared with two simple score models using logistic regression methods to predict the risk of ACRN. The areas under the receiver operating characteristic curves (AUCs) of the models were compared in internal and external validation databases. RESULTS: In the internal validation set, the AUCs of DNN model 1 and the APCS score model were 0.713 and 0.662 (p<0.001), respectively, and the AUCs of DNN model 2 and the KCS score model were 0.730 and 0.667 (p<0.001), respectively. However, in the external validation set, the prediction performances were not significantly different between the two DNN models and the corresponding APCS and KCS score models (both p>0.1). CONCLUSIONS: Simple score models for the risk prediction of ACRN are as useful as DNN-based models when input variables are limited. However, further studies on this issue are warranted to predict the risk of ACRN in colorectal cancer screening because DNN-based models are currently under improvement.