IDDF2024-ABS-0107 In-depth plasma proteomics integrating with genetics identify protein biomarkers for gastric cancer

Background Gastric cancer is the leading cause of cancer-related deaths worldwide. This study aims to identify plasma proteomic biomarkers underlying the etiology of gastric cancer and their potential for early detection. Methods A comprehensive and unbiased plasma proteomic characterization of gastric cancer was conducted through LC-MS/MS in a case-control study involving 100 GCs and 94 non-cancerous controls (IDDF2024-ABS-0107-Figure 1. Study design and proteomic profiling through LC-MS/MS). This study also utilized two GWAS summary datasets, including independent FinnGen and UK Biobank, for proteome-wide Mendelian randomization analysis (IDDF2024-ABS-0107-Figure 3. Mendelian randomization analysis). Summary-level statistics of genetic associations with levels of 4,907 circulating proteins were extracted from a large-scale protein quantitative trait loci study in 35,559 Icelanders. MR analysis was then conducted in cis-regions to identify genes that are causally linked with gastric cancer. Furthermore, biomarker panels for detecting gastric cancer were developed using five machine-learning methods and their performance was compared to traditional risk factors and tumor biomarkers. Results A total of 4652 highly reliable plasma proteins were identified in our case-control study, of which 918 proteins were significantly associated with GC after adjusting for age and sex (IDDF2024-ABS-0107-Figure 2. Proteomic signatures significantly associated with GC). Collectively, genetically predicted levels of 12 proteins were associated with GC risk. Elevated levels of 9 proteins (APOC1, S100A7, ADH7, LYZ, NPNT, C9, MMP7, TPI1, SAR1A) and decreased levels of 3 proteins (REG4, CRISPLD2, ENO2) were associated with an increased risk of GC (IDDF2024-ABS-0107-Figure 4. Summary results from case-control study and mendelian randomization). Based on the importance of variables and area under curve (AUC), APOC1, TPI1 and C9 were selected for constructing the diagnostic model. Our machine learning-derived diagnostic model demonstrated superior performance (AUC=0.97) compared to the traditional model that used clinical risk factors (AUC=0.75). The diagnostic model showed a sensitivity of 95%, which was significantly higher than that of seven clinical blood-based biomarker tests (CA724, CA199, CA50, CA242, CEA, CA125, AFP), which had sensitivities ranging from 5% to 30%. The findings remained consistent when analyses were restricted to early GC (IDDF2024-ABS-0107-Figure 5. Diagnostic model construction and evaluation for GC). Conclusions Our study highlights the potential of plasma proteomics for accurate screening and early detection of GC. Furthermore, it provides insights into the etiology of GC.