Label-free peptide quantification coupled with in silico mapping of proteases for identification of potential serum biomarkers in gastric adenocarcinoma patients.

OBJECTIVES

We aimed to identify serum level variations in protein-derived peptides between patients diagnosed with gastric adenocarcinoma (GAC) and non-cancer persons (control) to detect the activity changes of proteases and explore the auxiliary diagnostic value in the context of GAC physiopathology.

METHODS

The label-free quantitative peptidome approach was applied to identify variants in serum levels of peptides that can differentiate GAC patients from the control group. Peptide sequences were submitted against Proteasix tool predicting proteases potentially involved in their generation. The activity change of proteases was subsequently estimated based on the peptides with significantly altered relative abundance. In turn, activity change prediction of proteases was correlated with relevant protease expression data from the literature.

RESULTS

A total of 191 peptide sequences generated by the cleavage of 36 precursor proteins were identified. Using the label-free quantification approach, 33 peptides were differentially quantified (adjusted fold change ≥ 1.5 and p-value < 0.05) in which 19 were up-regulated and 14 were down-regulated in GAC samples. Of these peptides, fibrinopeptide A was significantly decreased and its phosphorylated form ADpSGEGDFLAEGGGVR was upregulated in GAC samples. Activity change prediction yielded 10 proteases including 6 Matrix Metalloproteinases (MMPs), Thrombin, Plasmin, and kallikreins 4 and 14. Among predicted proteases in our analysis, MMP-7 was presented as a more promising biomarker associated with useful assays of clinical practice for GAC diagnosis.

CONCLUSION

Our experimental results demonstrate that the serum levels of peptides were significantly differentiated in GAC physiopathology. The hypotheses built on protease regulation could be used for further investigations to measure proteases and their activity levels that have been poorly studied for GAC diagnosis.