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Войти в исследования Cancer medicine · 2026
Exploring the High-Risk Factors of Gastric "Inflammatory Cancer Transformation" Based on Inflammatory Immune Response: Construction of RHPI Complicated With CAG Risk Prediction Model.
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Jiang Tian-Tong, Liu-Feng Yi, Ya-Xuan Wang, Wang Shao-Li, Zhang Yan-Jiao, Tian Zhi-Hua, Chu Xin-Qiao, Chen Jing-Yu, Luo Yi, Luo Nan, Liu Zhen
Аннотация
OBJECTIVE: To investigate risk factors for refractory Helicobacter pylori infection (RHPI) complicated by chronic atrophic gastritis (CAG) and to develop a risk prediction model for early identification of high-risk patients.
METHODS: A retrospective cohort study enrolled 367 RHPI patients from January 2015 to December 2024. Demographic, inflammatory immune, tumor marker, and H. pylori antibody data were collected. Univariate and multivariate logistic regression identified risk factors, followed by LASSO regression for variable selection. A nomogram prediction model was constructed and evaluated using ROC curves, calibration curves, and decision curve analysis (DCA). In vitro experiments validated key findings.
RESULTS: Multivariate analysis identified age (OR = 1.05, 95% CI: 1.02-1.08), smoking (OR = 2.37, 95% CI: 1.46-3.85), CA199 (OR = 1.12, 95% CI: 1.03-1.22), and lymphocyte ratio (LYMPH%) (OR = 1.08, 95% CI: 1.01-1.15) as independent risk factors (all p < 0.05). The nomogram demonstrated excellent predictive performance with AUC 0.833 (95% CI: 0.791-0.876), sensitivity 94.50%, and specificity 78.40%. Calibration and decision curves confirmed good fit and clinical utility. In vitro experiments showed refractory H. pylori strains promoted gastric mucosal damage through enhanced inflammatory immune responses (e.g., IL-10, TNF-α; p < 0.05).
CONCLUSION: This study establishes the first disease-specific prediction model for RHPI complicated by CAG using readily available clinical indicators. The model shows strong predictive performance and clinical utility for early screening and intervention. Further multicenter studies are needed to validate generalizability and explore associations with gastric precancerous progression.
METHODS: A retrospective cohort study enrolled 367 RHPI patients from January 2015 to December 2024. Demographic, inflammatory immune, tumor marker, and H. pylori antibody data were collected. Univariate and multivariate logistic regression identified risk factors, followed by LASSO regression for variable selection. A nomogram prediction model was constructed and evaluated using ROC curves, calibration curves, and decision curve analysis (DCA). In vitro experiments validated key findings.
RESULTS: Multivariate analysis identified age (OR = 1.05, 95% CI: 1.02-1.08), smoking (OR = 2.37, 95% CI: 1.46-3.85), CA199 (OR = 1.12, 95% CI: 1.03-1.22), and lymphocyte ratio (LYMPH%) (OR = 1.08, 95% CI: 1.01-1.15) as independent risk factors (all p < 0.05). The nomogram demonstrated excellent predictive performance with AUC 0.833 (95% CI: 0.791-0.876), sensitivity 94.50%, and specificity 78.40%. Calibration and decision curves confirmed good fit and clinical utility. In vitro experiments showed refractory H. pylori strains promoted gastric mucosal damage through enhanced inflammatory immune responses (e.g., IL-10, TNF-α; p < 0.05).
CONCLUSION: This study establishes the first disease-specific prediction model for RHPI complicated by CAG using readily available clinical indicators. The model shows strong predictive performance and clinical utility for early screening and intervention. Further multicenter studies are needed to validate generalizability and explore associations with gastric precancerous progression.