New Approach for Differential Glycomics Expression Analysis Developed

Scientists at the University of Gothenburg have devised a suite of methods to facilitate differential glycomics expression analysis. Their work aims to advance glycomics research and translation by making analysis and data interpretation more accessible.

By employing their innovative methods, the study, led by Daniel Bojar and published in Cell Reports Methods, was able to detect different types of cancer. “We have analyzed data from about 220 patients with 11 differently diagnosed cancers and have identified differences in the substructure of the glycan depending on the type of cancer. By letting our newly developed method, enhanced by AI, work through large amounts of data, we were able to find these connections,” explained Bojar.

There are also other research groups that study the substructures of the glycan in search of biomarkers but their work often involves statistical tests using mass spectroscopy to find out whether the level of individual sugars is significantly higher or lower in cancer, according to Bojar. These more traditional methods have too low sensitivity and are not reliable because different sugars are structurally related and therefore not independent of each other.

The new methods take these problems into account and can find the patterns in the data sets where others fail. “We can rely on our results; they are statistically significant. If we know what we are looking for, it is easier to find the correct result. Now we will take these biomarkers and develop test methods,” added Bojar.

Bojar envisions the development of a swift and reliable analytical method for detecting not only the presence of cancer but also its specific type through easily accessible samples like blood or saliva. He anticipates clinical tests on human samples within the next 4-5 years, marking a potential paradigm shift in cancer diagnostics.