Identification of plasma proteomic signatures associated with the progression of cardia gastric cancer and precancerous lesions

Objective: Considering that there are no effective biomarkers for the screening of cardia gastric cancer (CGC), we developed a noninvasive diagnostic approach, employing data-independent acquisition (DIA) proteomics to identify candidate protein markers. Methods: Plasma samples were obtained from 40 subjects, 10 each for CGC, cardia high-grade dysplasia (CHGD), cardia low-grade dysplasia (CLGD), and healthy controls. Proteomic profiles were obtained through liquid chromatography-mass spectrometry (LC-MS/MS-based DIA proteomics. Candidate plasma proteins were iden-tified by weighted gene co-expression network analysis (WGCNA) combined with machine learning and further validated by the Human Protein Atlas (HPA) database. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of the biomarker panel. Results: There was a clear distinction in proteomic features among CGC, CHGD, CLGD, and the healthy controls. According to the WGCNA, we found 42 positively associated and 164 inversely associated proteins related to CGC progression and demonstrated several canonical cancer-associated pathways. Combined with the results from random forests, LASSO regression, and immunohistochemical results from the HPA database, we identified three candidate proteins (GSTP1, CSRP1, and LY6G6F) that could together distinguish CLGD (AUC = 0.91), CHGD (AUC = 0.99) and CGC (AUC = 0.98) from healthy controls with excellent accuracy. Conclusions: The panel of protein biomarkers showed promising diagnostic potential for CGC and precancerous lesions. Further validation and a larger-scale study are warranted to assess its potential clinical applications, suggesting a potential avenue for CGC prevention in the future.