| Title: | Plasma proteomic signatures improve risk stratification and personalized screening for gastric cancer |
| Journal: | Gastric Cancer |
| Published: | 14 May 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/42133180/ |
| DOI: | https://doi.org/10.1007/s10120-026-01749-4 |
Publication 20031
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Abstract
BackgroundAccurate identification of individuals at high risk of gastric cancer (GC) remains a major challenge for effective screening. We aimed to identify plasma proteomic signatures and develop a risk prediction model for GC risk stratification.MethodsPlasma proteomic profiling was performed using liquid chromatography-tandem mass spectrometry in a case-control discovery set (100 GC cases and 94 controls). Candidate proteins were evaluated in 52,552 UK Biobank participants with a median follow-up of 13.63 years, during which 92 incident GC cases were identified. Risk models integrating clinical, genetic, and proteomic factors were developed using LASSO-penalized Cox regression with stability selection and internally validated using bootstrap resampling.ResultsAmong 2306 differentially expressed proteins in discovery, 25 were replicated in validation at nominal significance (P < 0.05) with consistent directions. Two proteins (CTSD and GGH) remained significant after false discovery rate correction. A primary proteomic model (clinical factors plus five proteins) improved discrimination versus clinical model (optimism-corrected C-index: 0.745 vs. 0.732, P = 0.046). Risk stratification revealed a clear GC risk gradient: hazard ratios were 6.08 (95% CI 2.15-17.20) for moderate-risk and 23.88 (95% CI 8.66-65.87) for high-risk groups. The risk score was also associated with GC risk as continuous variable (HR per standard deviation: 1.09, 95% CI 1.08-1.11). The 15-year cumulative incidence ranged from 0.02 to 0.56% across risk groups. Decision curve analysis indicated improved clinical utility.ConclusionsPlasma proteomic signatures may improve GC risk stratification beyond traditional clinical factors and could support more targeted screening strategies. Further validation is warranted.</p>
11 Authors
- Xue Li
- Wen-Hao Shi
- Juan Zhu
- Ying Chen
- Bin Liu
- Nai-Ren Zheng
- Le Wang
- Li Yuan
- Ying-Ying Mao
- Xiang-Dong Cheng
- Ling-Bin Du
1 Application
| Application ID | Title |
|---|---|
| 65851 | Large-scale proteomic profiling to facilitate genetics-guided drug discovery and precision medicine: The UK Biobank - Pharma Proteomics Project (UKB-PPP) |
Enabling scientific discoveries that improve human health