Data-independent acquisition mass spectrometry identification of extracellular vesicle biomarkers for gastric adenocarcinoma

Early diagnosis of gastric adenocarcinoma (GAC) can effectively prevent the progression of the disease and significantly improve patient survival. Currently, protein markers in clinical practice barely meet patient needs; it is therefore imperative to develop new diagnostic biomarkers with high sensitivity and specificity. In this study, we extracted extracellular vesicles (EV) from the sera of 33 patients with GAC and 19 healthy controls, then applied data-independent acquisition (DIA) mass spectrometry to measure protein expression profiles. Differential protein expression analysis identified 23 proteins showing expression patterns across different cancer stages, from which 15 proteins were selected as candidate biomarkers for GAC diagnosis. From this subset of 15 proteins, up to 6 proteins were iteratively selected as features and logistic regression was used to distinguish patients from healthy controls. Furthermore, serum-derived EV from a new cohort of 12 patients with gastric cancer and 18 healthy controls were quantified using the same method. A classification panel consisting of GSN, HP, ORM1, PIGR, and TFRC showed the best performance, with a sensitivity and negative predictive value (NPV) of 0.83 and 0.82. The area under curve (AUC) of the receiver operating characteristic (ROC) is 0.80. Finally, to facilitate the diagnosis of advanced stage GAC, we identified a 3-protein panel consisting of LYZ, SAA1, and F12 that showed reasonably good performance with an AUC of 0.83 in the validation dataset. In conclusion, we identified new protein biomarker panels from serum EVs for early diagnosis of gastric cancer that worth further validation.

Automated summary

This study extracted extracellular vesicles from serum samples and identified a series of protein biomarkers for gastric cancer diagnosis. A panel of proteins was selected and logistic regression was used for classification. A classification panel with high performance and a protein panel for advanced stage gastric cancer diagnosis were identified.