Letter: predictive model for gastric cancer after eradication of Helicobacter pylori-a survival analysis using a deep learning algorithm

The role of gastric mucins and mucin-related glycans in gastric cancers

Gastric mucins serve as a protective barrier on the stomach's surface, protecting from external stimuli including gastric acid and gut microbiota. Their composition typically changes in response to the metaplastic sequence triggered by Helicobacter pylori infection. This alteration in gastric mucins is also observed in cases of gastric cancer, although the precise connection between mucin expressions and gastric carcinogenesis remains uncertain. This review first introduces the relationship between mucin expressions and gastric metaplasia or cancer observed in humans and mice. Additionally, we discuss potential pathogenic mechanisms of how aberrant mucins and their glycans affect gastric carcinogenesis. Finally, we summarize challenges to target tumor-specific glycans by utilizing lectin-drug conjugates that can bind to specific glycans. Understanding the correlation and mechanism between these mucin expressions and gastric carcinogenesis could pave the way for new strategies in gastric cancer treatment.

Development and validation of LightGBM algorithm for optimizing of Helicobacter pylori antibody during the minimum living guarantee crowd based gastric cancer screening program in Taizhou, China

Gastric cancer continues to be a significant health concern in China, with a high incidence rate. To mitigate its impact, early detection and treatment is key. However, conducting large-scale endoscopic gastric cancer screening is not feasible in China. Instead, a more appropriate approach would be to initially screen high-risk groups and follow up with endoscopic testing as needed. We conducted a study on 25,622 asymptomatic participants aged 45-70 years from a free gastric cancer screening program in the Taizhou city government's Minimum Living Guarantee Crowd (MLGC) initiative. Participants completed questionnaires, blood tests, and underwent gastrin-17 (G-17), pepsinogen I and II (PGI and PGII), and H. pylori IgG antibody (IgG) assessments. Using the light gradient boosting machine (lightGBM) algorithm, we developed a predictive model for gastric cancer risk. In the full model, F1 score was 2.66%, precision was 1.36%, and recall was 58.14%. In the high-risk model, F1 score was 2.51%, precision was 1.27%, and recall was 94.55%. Excluding IgG, the F1 score was 2.73%, precision was 1.40%, and recall was 68.62%. We conclude that H. pylori IgG appears to be able to be excluded from the prediction model without significantly affecting its performance, which is important from a health economic point of view. It suggests that screening indicators can be optimized, and expenditures reduced. These findings can have important implications for policymakers, as we can focus resources on other important aspects of gastric cancer prevention and control.

Machine learning-based personalized prediction of gastric cancer incidence using the endoscopic and histologic findings at the initial endoscopy

BACKGROUND AND AIMS

Accurate risk stratification for gastric cancer is required for optimal endoscopic surveillance in patients with chronic gastritis. We aimed to develop a machine learning (ML) model that incorporates endoscopic and histologic findings for an individualized prediction of gastric cancer incidence.

METHODS

We retrospectively evaluated 1099 patients with chronic gastritis who underwent EGD and biopsy sampling of the gastric mucosa. Patients were randomly divided into training and test sets (4:1). We constructed a conventional Cox proportional hazard model and 3 ML models. Baseline characteristics, endoscopic atrophy, and Operative Link on Gastritis-Intestinal Metaplasia Assessment (OLGIM)/Operative Link on Gastritis Assessment (OLGA) stage at initial EGD were comprehensively assessed. Model performance was evaluated using Harrel's c-index.

RESULTS

During a mean follow-up of 5.63 years, 94 patients (8.55%) developed gastric cancer. The gradient-boosting decision tree (GBDT) model achieved the best performance (c-index from the test set, .84) and showed high discriminative ability in stratifying the test set into 3 risk categories (P < .001). Age, OLGIM/OLGA stage, endoscopic atrophy, and history of malignant tumors other than gastric cancer were important predictors of gastric cancer incidence in the GBDT model. Furthermore, the proposed GBDT model enabled the generation of a personalized cumulative incidence prediction curve for each patient.

CONCLUSIONS

We developed a novel ML model that incorporates endoscopic and histologic findings at initial EGD for personalized risk prediction of gastric cancer. This model may lead to the development of effective and personalized follow-up strategies after initial EGD.

Clinicopathological Features of Gastric Cancer with Autoimmune Gastritis

Most gastric cancers develop in patients with chronic gastritis. Chronic gastritis can be classified into two major subtypes: Helicobacter pylori (H. pylori)-induced gastritis and autoimmune gastritis (AIG). Whereas H. pylori-related gastric cancers are more common and have been extensively investigated, the clinicopathological features of gastric cancer with autoimmune gastritis are unclear. Patients diagnosed with gastric cancer and hospitalized in the University Tokyo Hospital from 1998 to 2017 were enrolled. Diagnosis of autoimmune gastritis was based on positivity for serum anti-parietal cell antibody (APCA). We evaluated mucin expression and immune cell infiltration by immunohistochemical staining for MUC5AC, MUC6, PD-L1, CD3, CD11, Foxp3, and PD1. We also examined the presence of bacterial taxa that are reportedly enriched in AIG. Survival analyses of recurrence and 5-year mortality were also performed. In total, 261 patients (76 APCA-positive and 185 APCA-negative) were analyzed. Immunohistochemical staining in the matched cohort showed that AIG-related gastric cancer had higher MUC5AC expression (p = 0.0007) and MUC6 expression (p = 0.0007). Greater infiltration of CD3-positive (p = 0.001), Foxp3-positive (p < 0.001), and PD1-positive cells (p = 0.001); lesser infiltration of CD11b-positive (p = 0.005) cells; and a higher prevalence of Bacillus cereus (p = 0.006) were found in AIG-related gastric cancer patients. The cumulative incidences of gastric cancer recurrence were 2.99% at 2 years, 15.68% at 6 years, and 18.81% at 10 years in APCA-positive patients; they were 12.79% at 2 years, 21.35% at 6 years, and 31.85% at 10 years in APCA-negative patients. The cumulative incidences of mortality were 0% at 3 years and 0% at 5 years in APCA-positive patients; they were 1.52% at 3 years and 2.56% at 5 years in APCA-negative patients. We identified molecular differences between AIG and non-AIG gastric cancer. Differences in T-cell populations and the gastric microbiota may contribute to the pathogenesis of gastric cancers and potentially affect the response to immunotherapy.