A targeted proteomics approach reveals a serum protein signature as diagnostic biomarker for resectable gastric cancer

Background: Gastric cancer (GC) is the third leading cause of cancer death. Early detection is a key factor to reduce its mortality. Methods: We retrospectively collected pre- and postoperative serum samples as well as tumour tissues and adjacent normal tissues from 100 GC patients. Serum samples from non-cancerous patients were served as controls (n = 50). A high-throughput protein detection technology, multiplex proximity extension assays (PEA), was applied to measure levels of over 300 proteins. Alteration of each protein was analysed by univariate analysis. Elastic-net logistic regression was performed to select serum proteins into the diagnostic model. Findings: We identified 19 serum proteins (CEACAM5, CA9, MSLN, CCL20, SCF, TGF-alpha, MMP-1, MMP-10, IGF-1, CDCP1, PPIA, DDAH-1, HMOX-1, FLI1, IL-7, ZBTB-17, APBB1IP, KAZALD-1, and ADAMTS-15) that together distinguish GC cases from controls with a diagnostic sensitivity of 93%, specificity of 100%, and area under receiver operating characteristic curve (AUC) of 0·99 (95% CI: 0·98–1). Moreover, the 19-serum protein signature provided an increased diagnostic capacity in patients at TNM I-II stage (sensitivity 89%, specificity 100%, AUC 0·99) and in patients with high microsatellite instability (MSI) (91%, 98%, and 0·99) compared to individual proteins. These promising results will inspire a large-scale independent cohort study to be pursued for validating the proposed protein signature. Interpretation: Based on targeted proteomics and elastic-net logistic regression, we identified a 19-serum protein signature which could contribute to clinical GC diagnosis, especially for patients at early stage and those with high MSI. Fund: This study was supported by a European H2020-Marie Skłodowska-Curie Innovative Training Networks grant (316,929, GastricGlycoExplorer). Funder had no influence on trial design, data evaluation, and interpretation.