Ying Xu, Ph.D.
The University of Georgia
Ying Xu’s bioinformatics approach to cancer research suggests a significant rethinking of how cancer begins — and how it can be treated.
Xu is a computational biologist who uses a portfolio of technology-based tools, such as large-scale mining of biological data. Unlike most cancer researchers, who study cultures derived from cancerous cells, Xu and his team analyze genetic data derived from tissues. Their techniques have uncovered important information about how cancer begins.
Recently, Xu and his colleagues presented a model that explains how chronic inflammation and hypoxia, or low oxygen levels in cells, can lead to cell proliferation and, ultimately, cancer. The model challenges the prevailing view of genetic mutations as cancer's main cause.
In developing the model, Xu compared gene expression data from humans, mice, and rats, all of whom do get cancer, with similar data from frogs, turtles, and naked mole rats, who don’t — a mystery that has long puzzled researchers. This data revealed key differences in how low-oxygen environments affect the cellular energy metabolism for each group, forming the basis for Xu’s unconventional hypothesis about what causes cancer.
Xu's work could significantly shift the landscape of cancer treatment and research. Current research focuses primarily on drug therapies that target genetic mutations associated with a particular type of cancer. But if Xu’s model turns out to be correct, it would create a new paradigm to treat and perhaps prevent cancer, with therapies aimed at stopping hypoxia in cells.
Xu’s major areas of research include:
- Computational prediction and modeling of protein structures and protein complex structures
- Computational inference and reconstruction of biological pathways
- Computational techniques for in silico biological investigation
Xu is also using cancer bioinformatics to develop techniques for early detection — one of the most crucial factors in successful treatment. He and his colleagues developed a breakthrough computational method for predicting which abnormal proteins might be excreted in the urine of a cancer patient: a warning sign found in a simple urine test. Their research has focused on a diagnostic for gastric cancer, but their techniques could be applied to other cancers as well.
Dr. Xu is motivated by a desire to build a nationally recognized program in bioinformatics research and education — and help train the next generation of computational biologists. At the University of Georgia he’s found an ideal opportunity to do this, supported by the Georgia Research Alliance. Xu says, “This is a fantastic environment to conduct research at the intersection of life science and computational science.”