Research Magazine 2006

Cyber-diagnosing cancer

Jean Gao

Blood analysis has led to the early discovery of many diseases. For instance, high blood glucose may indicate diabetes while a low red blood cell count may portend anemia.

But what can blood tell us about prostate cancer?

According to the American Cancer Society, more than 186,000 U.S. men were diagnosed with prostate cancer in 2008 and more than 28,000 died. Signs of the disease, such as tumors, don’t appear until advanced stages.

Jean Gao

That was until researchers identified a biomarker that helps reveal prostate cancer earlier than other screenings, and computer science and engineering Assistant Professor Jean Gao discovered a way to track serum proteins associated with the biomarker using computer software. A UT Arlington faculty member since 2003, Dr. Gao received the College of Engineering’s Outstanding Young Faculty Award in 2007 and the Research Excellence Award for her department in 2007 and 2008.

The detection of biomarkers in the body can indicate the presence of diseases like prostate cancer. Disease-causing proteins are often associated with these biomarkers.

“The goal is to better understand how the proteins interact with cells by examining their motility,” says Gao, who received a $568,000 grant from the National Science Foundation CAREER program.

The first step of the study focused on identifying the serum proteins closely associated with the biomarkers for prostate cancer. For that, Gao relied on partners from UT Southwestern Medical Center at Dallas who studied blood taken from cancer patients.

“By examining data in this way, there is potential to find common elements in prostate cancer patients and to determine the success of potential treatment options.”

“Protein abnormalities are expressed differently between diseased and healthy individuals,” she says. “They move and act differently, so it is important to visually understand their interactions.”

Her fellow researchers used a mass spectrometer to study the proteins as they moved inside the body and cells and to create a series of images to show how prostate cancer progresses.

The team compiled its data and gave it to Gao and her graduate research students to analyze. They developed the Intercellular Cell Dynamics Analysis System (ICellDAS), which can efficiently predict protein motility in the cells as well as the intensity of the proteins in the body.

“By examining data in this way, there is potential to find common elements in prostate cancer patients and to determine the success of potential treatment options,” Gao says. “We hope to one day find the one that will improve the prognosis of prostate cancer patients.”

Gao’s system also has potential for identifying relapses among ovarian cancer patients.

ICellDAS is available in beta format and can be accessed online at http://visionlab.uta.edu/icelldas/.

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