Context-based bipartite graph matching- a computational approach to find associations between microRNAs and biological pathways in pancreatic cancer
Advisor Information
Dhundy Bastola
Location
UNO Criss Library, Room 112
Presentation Type
Oral Presentation
Start Date
7-3-2014 3:45 PM
End Date
7-3-2014 4:00 PM
Abstract
Cancer research has generated a valuable body of knowledge about the mutations that play a significant role in cancer proliferation. These mutations have lead to gain of function in oncogenes where as detrimental loss of function in tumor suppressor genes. Pancreatic cancer (PC), in particular pancreatic adenocarcinoma (PA), is one of the deadliest forms of cancer, resulting in 38000 deaths in the United States per year. The current 5-year survival rate for patients treated with state-of-the-art therapies is merely 5%. To date, abnormal CA19-9 level is the most reliable diagnostic serum marker. However, it is still not effective in detecting the cancer early enough for available therapy to be effective. This lack of early diagnosis has been recognized as the major cause for the high mortality rate observed in pancreatic cancer. More recently, microRNAs have been identified as potential biomarkers in the diagnosis of pancreatic cancer. MicroRNAs (or miRNAs) are short ~21-22 nucleotide long non-coding RNAs that act as regulators of gene expression. Gene expression studies have shown existence of deregulation of miRNA genes during tumor conditions. In this study, we developed a computational approach to identify relationship between miRNAs and biological processes or pathways involved in pancreatic cancer using bipartite graph matching. The long-term goal of our research is to establish a computational framework capable of integrating multiple-relevant knowledgebase (miRNA-mRNA interaction, gene expression, biological process and metabolic pathway data) to identify candidate therapeutic miRNA(s). Successful completion of this goal is expected to increase the specificity of therapeutics and reduce the side effects associated with current methods.
Context-based bipartite graph matching- a computational approach to find associations between microRNAs and biological pathways in pancreatic cancer
UNO Criss Library, Room 112
Cancer research has generated a valuable body of knowledge about the mutations that play a significant role in cancer proliferation. These mutations have lead to gain of function in oncogenes where as detrimental loss of function in tumor suppressor genes. Pancreatic cancer (PC), in particular pancreatic adenocarcinoma (PA), is one of the deadliest forms of cancer, resulting in 38000 deaths in the United States per year. The current 5-year survival rate for patients treated with state-of-the-art therapies is merely 5%. To date, abnormal CA19-9 level is the most reliable diagnostic serum marker. However, it is still not effective in detecting the cancer early enough for available therapy to be effective. This lack of early diagnosis has been recognized as the major cause for the high mortality rate observed in pancreatic cancer. More recently, microRNAs have been identified as potential biomarkers in the diagnosis of pancreatic cancer. MicroRNAs (or miRNAs) are short ~21-22 nucleotide long non-coding RNAs that act as regulators of gene expression. Gene expression studies have shown existence of deregulation of miRNA genes during tumor conditions. In this study, we developed a computational approach to identify relationship between miRNAs and biological processes or pathways involved in pancreatic cancer using bipartite graph matching. The long-term goal of our research is to establish a computational framework capable of integrating multiple-relevant knowledgebase (miRNA-mRNA interaction, gene expression, biological process and metabolic pathway data) to identify candidate therapeutic miRNA(s). Successful completion of this goal is expected to increase the specificity of therapeutics and reduce the side effects associated with current methods.