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Author Spotlight: Unveiling Transmembrane Protein Family-Related Markers in Gastric Cancer and Implications for Targeted Therapies
Published on: September 15, 2023
The LGR5/TFF2 ratio as a stem cell biomarker predicts high-risk status in chronic atrophic gastritis: a retrospective
Qingqing Zhang1, Di Wu2, Fengyun Guo1
1Institute of Digestive Diseases, Xiyuan Hospital of China Academy of Chinese Medical Sciences, Beijing, 100091, China.
Background:
Chronic atrophic gastritis (CAG) severity risk assessment remains challenging due to limitations of current static histologic/endoscopic tools. We evaluated the leucine-rich repeat-containing G-protein coupled receptor 5 (LGR5)/trefoil factor 2 (TFF2) ratio - quantifying gastric stem cell niche imbalance- as a dynamic biomarker for high-risk CAG (OLGA/OLGIM III-IV).
Methods:
In a retrospective cohort (n = 60 CAG patients), we constructed four prediction models (Baseline: demographics; Clinical: +H. pylori/family history; Endoscopic; Full: +LGR5/TFF2 ratio) using Firth penalized regression. Model performance was assessed via area under the curve (AUC), Akaike (AIC), and Bayesian (BIC) information criteria, and likelihood ratio tests. Restricted cubic splines and subgroup analyses validated threshold effects.
Results:
The LGR5/TFF2 ratio > 1.066 (AUC = 0.93) identified high-risk CAG with 48.5-fold increased odds (95% CI: 4.07-578.51, P = 0.002), outperforming endoscopic/histologic models (ΔAUC = + 0.15). A nonlinear dose-response relationship revealed sharp risk escalation beyond this threshold. The high-ratio group (n = 20) exhibited 60.00% prevalence of prior H. pylori eradication, Wnt activation (β-catenin↑32.3%, CyclinD1↑90%, P < 0.001), and stronger endoscopic correlation (ρ = 0.42 for IM nodules) than traditional factors (ρ = 0.28 for H. pylori).
Conclusion:
The LGR5/TFF2 ratio is the first biomarker to molecularly define CAG severity risk by quantifying stem cell niche imbalance (LGR5↑/TFF2↓), achieving superior accuracy (AUC = 0.93) and identifying high-risk cases missed by current methods. Its integration into clinical practice could transform risk-stratified surveillance paradigms.
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