New research from University of Texas MD Anderson Cancer Center is shining light on pseudogenes to determine their utility as cancer biomarkers. Bioinformatics and Computational Biology Professor Han Liang, PhD, recently published a study in Nature Communications that analyzed nearly 10,000 pseudogenes to better understand biomarkers.
“The study surveyed seven cancer subtypes including those for breast, kidney, ovarian, colorectal, lung, and uterine,” said Dr. Liang in a news release. “Across the cancer types, the tumor subtypes revealed by pseudogene expression showed extensive and strong similarities with subtypes defined by other molecular data.”
Dr. Liang, along with lead author Leng Han and collaborators from MD Anderson, Baylor College of Medicine, and UT Health Science Center at Houston, looked at pseudogene expression in 2,808 patients with seven different cancers. The team began with 378 billion RNA sequences from The Cancer Genome Atlas research program and generated almost 10,000 pseudogenes. This large amount of data was essential to the team’s analysis of pseudogenes, as large numbers of patients samples are required for reliability, and made Dr. Liang’s study unique among previous studies.
Tumor subtypes were consistently classified according to pseudogene expression, and the data agreed with subtyping from other molecular data. Specifically, “Pseudogene expression alone can accurately classify the major subtypes of endometrial cancer,” said Dr. Liang. According to the article, “in kidney cancer, the pseudogene expression subtypes not only significantly correlate with patient survival, but also help stratify patients in combination with clinical variables.”
It was suggested pseudogene expression can be used to explain how cancer occurs and to make diagnoses or prognoses. This study elevates the status of pseudogenes from non-protein-coding genes to potential agents for personalized medicine.