[Tissue and plasma proteomic signatures associated with the risk of gastric cancer]

Objective: To identify proteins associated with the risk of gastric cancer (GC) and build a protein risk score for risk prediction of GC based on proteomic analysis. Methods: Gastric mucosal proteomics data were used to construct Dataset One, comprising 94 GC cases and 230 individuals with different stages of gastric mucosal lesions. The GC cases were recruited from the National Upper Gastrointestinal Cancer Early Detection (UGCED) Program in Linqu, Shandong Province, as well as clinical patients from the Fifth Medical Center, General Hospital of PLA, and Peking University Cancer Hospital. Non-cancer individuals were enrolled from the National UGCED Program in Linqu and community screening programs at the Dongfang Hospital. All participants were pathologically confirmed. Multivariate logistic regression analysis was employed to identify gastric mucosal proteins significantly associated with GC risk. Subsequently, plasma proteomics data from the UK Biobank Pharma Proteomics Project (UKB-PPP) were used to construct Dataset Two, including 40 baseline GC cases and 47 933 non-cancer individuals, and Dataset Three, comprising 138 incident GC cases and 47 933 non-cancer individuals during a prospective follow-up period. In Dataset Two, multivariate logistic regression analysis was conducted to assess associations between plasma protein levels and baseline GC risk. In Dataset Three, multivariate Cox regression analysis was used to examine associations with the risk of incident GC. A poly-protein risk score (PRS) was developed using a weighted summation method based on protein effect sizes from Dataset Two. Its associations with GC risk and the progression of gastric mucosal lesions were evaluated using linear regression trend tests. Results: A total of 324, 47 973 and 48 071 participants were included in Datasets One, Two, and Three, respectively. Across the three datasets, the proportions of males and individuals aged>60 years were higher in the GC group than in the non-GC group (all P values<0.05). The follow-up period in Dataset Three had a M (P, P) of 14.47 (13.7, 15.2) years, with a median of 7.4 (4.6, 11.3) years for those who progressed to GC. Based on Dataset One, 2 524 tissue-differential proteins associated with GC risk were identified through multivariate logistic regression analysis adjusted for age and sex. Among these, seven proteins were consistently associated with GC risk across tissue and plasma levels in Datasets Two and Three, with consistent directions of association. Five proteins (MRC1, APOL1, BST2, PON2, and GGH) were positively associated with GC risk, while two (GSN and CLEC3B) were negatively associated. Analysis of the PRS based on these seven proteins showed that for each standard deviation increase in the tissue-derived PRS, the risk of GC increased by 6.26 times (95%CI: 4.02-9.75). In Dataset Two, each standard deviation increase in the plasma-derived PRS was associated with a 2.13-fold increase in GC risk (95%CI: 1.68-2.69). In the prospective cohort of Dataset Three, individuals in the high PRS group had a 2.27-fold higher risk of GC compared to the low PRS group (95%CI: 1.50-3.45). Moreover, each standard deviation increase in the plasma PRS was associated with a 57% higher risk of GC (HR=1.57, 95%CI: 1.34-1.84). Additionally, the tissue-derived PRS showed an increasing trend with the progression of gastric mucosal lesions. Conclusion: The tissue and plasma proteomics identified seven individual proteins that may indicate the risk of developing gastric cancer, showing the potential as biomarkers for aiding in the screening of gastric cancer.