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Chinese scientists have developed a highly robust proteome analysis system, which can diagnose markers of early gastric cancer.

2024-05-30 Update From: SLTechnology News&Howtos shulou NAV: SLTechnology News&Howtos > IT Information >

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Shulou(Shulou.com)12/24 Report--

CTOnews.com, December 23, Dalian Institute of Chemical Physics, Chinese Academy of Sciences recently announced the development of a highly robust proteome analysis system for site-specific glycotypes, which can diagnose markers of early gastric cancer.

The project was developed by Ye Mingliang researcher team of Biotechnology Research Department of the Institute (group 1809) and Professor Nie Yongzhan team of Air Force military Medical University. Large-scale analysis of 278 clinical samples for N-glycosylated proteome, screening and verification of new protein glycosylated biomarkers, which can be used for the diagnosis of gastric cancer, especially early gastric cancer.

At present, the tumor markers used in clinic are basically glycoproteins. The abnormality of protein glycosylation is closely related to the occurrence and development of disease, but there is no disease marker of specific sugar type (site-specific sugar type) at specific protein and specific site.

Researchers have developed a highly robust site-specific proteomic analysis system. The system uses automatic method to enrich N-complete glycopeptide in biological samples, then uses microflow LC-MS / MS system to detect N-complete glycopeptide, and uses Glyco-Decipher software developed by the team to identify and quantify site-specific glycopeptides.

Researchers used the system to analyze more than 200 samples and found that their enrichment specificity remained between 88.1% and 91.7%, showing a stable glycopeptide enrichment performance.

In the 10 intra-batch / inter-batch quality control samples, the relative standard deviations of N-complete glycopeptides, glycosylation sites and the number of glycoproteins identified were all less than 7%, and the average Pearson correlation coefficient of quantitative N-complete glycopeptides was 0.909.

After a long time analysis of more than 200 large cohort samples, it was found that each quality control sample could still stably identify an average of 1900 complete N-complete glycopeptides.

Therefore, the system has highly sensitive and robust performance for the identification of N-complete glycopeptide, which provides a powerful tool for large-scale analysis of N-glycosylation site-specific glycopeptide.

CTOnews.com attached the reference address of the paper as follows: https://doi.org/10.1002/advs.202306955.

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