Data-independent acquisition mass spectrometry identification of extracellular vesicle biomarkers for gastric adenocarcinoma - Gu, Lei; Chen, Jin; Yang, Yueying; Zhang, Yunpeng; Tian, Yuying; Jiang, Jinhua; Zhou, Donglei; Liao, Lujian (Frontiers in Oncology, 2022)
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 of multiple cancer stages identified 23 proteins showing the same expression trend across different stages, among which 15 EV proteins were selected as candidate biomarkers for GAC diagnosis. From this subset of differentially expressed 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 ROC curve 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.