Cancer’s hidden sugar code opens diagnostic opportunities

The complex sugar molecules that festoon our cells are often treated as little more than biological decoration. A new study suggests they hold hidden patterns — distinct signatures that can separate one cancer from another.

By tracing the genetic machinery that sculpts these sugar tags, called glycans, scientists at KAUST have uncovered a sweet-and-simple diagnostic code, one that could make identifying and classifying tumors faster and more precise^[1].

“We have created the building blocks for a one-stop classification system for all cancers,” says cell biologist Jasmeen Merzaban, who co-led the study.

The project began as a collaboration between Merzaban and her colleague, computational biologist Xin Gao, who specializes in applying artificial intelligence to health-related challenges. Together, they trained a machine learning algorithm on gene expression data from thousands of tumor samples — focusing not on the full catalogue of gene readouts, as existing AI cancer-classification tools have done, but on a lean set of 71 genes responsible for building glycans. These cancer-pattern glycosyltransferases, or CPGTs, are known to play a pivotal role in how tumors proliferate and spread.

The resulting model proved remarkably powerful: it sorted tumors into 27 categories with more than 95 percent accuracy — a performance on par with, and in some cases surpassing, gold-standard genomic classifiers that rely on far larger gene sets. And, unlike many cancer-classification systems that require massive datasets and heavy computational power, the KAUST model runs quickly on a standard laptop. Results can be generated in under half an hour, enabling broader use in hospitals and labs that lack high-performance computing resources.

The payoff was not just in speed. In gliomas — an especially aggressive form of brain cancer — glycan-related gene expression patterns predicted patient survival more reliably than standard clinical markers. In breast cancer, the CPGT-based classifier nearly doubled the accuracy of a widely used genomic test in distinguishing tumor subtypes.

“That means CPGTs may reveal not only what kind of cancer a patient has, but also how the disease is likely to progress,” notes Jing Kai, a Ph.D. student in Merzaban’s lab group, co-first author on the study. “Our work here shows the untapped potential for using glycans in cancer diagnosis and prognosis,” she adds.

Ali AlZahrani, a thyroid cancer specialist and co-author of the study from the King Faisal Specialist Hospital and Research Center in Riyadh, agrees. “This study is proof of a new concept in the diagnosis and classification of cancer with potentially wide-reaching applications,” he says.

To move the technology closer to clinical use, the KAUST team is now streamlining their methods for analyzing CPGT expression and, in collaboration with AlZahrani and other Saudi clinicians, validating their model in larger patient cohorts. At the same time, the researchers have joined forces with KAUST structural biologist Andreas Naschberger to solve the three-dimensional structures of key CPGTs, aiming to identify new therapeutic targets that could inspire future drug development.

“The diagnostic tool marks a first step toward turning sugar biology into a practical tool for precision medicine for people with cancer,” notes Merzaban. “This ultimately broadens the toolkit for both basic discovery and translational oncology,” she concludes.

Reference

1. Kai, J., Yang, L., AbuElela, A.F., Abdel-Haleem, A.M., AlAmoodi, A.S., Bin Nafisah, A.A., Alshaibani, A., Alzahrani, A.S., Lagani, V., Gomez-Cabrero, D., Gao, X. & Merzaban, J.S. (2025). Building simplified cancer subtyping and prediction models with glycan gene signatures. Cell Reports Methods 5, 101140 (2025).| article.