Computational Biology: Toward Early Detection of Pancreatic Cancer.

Abstract

Pancreatic cancer is the eleventh most common cancer type and the seventh leading cause of cancer mortality globally. Although chemotherapy is widely employed in the treatment of any cancer type, the response rate in pancreatic cancer is very low. Hence, new and effective techniques in the treatment of pancreatic cancer are needed. Recent advances in molecular profiling as well as high-throughput sequencing technologies, for example, next-generation sequencing technologies, have revolutionized the field of cancer research. Protein-protein interaction among cancer and normal cells plays an important role in any cancer molecular mechanisms, and identifying key genes or protein via experimental technologies requires huge expenditures of capital and time. Thus, integrated computational approaches are urgently needed in cancer research. In this review, we discuss different computational approaches developed to detect novel key genes (TRIM24, CDK14, ECT2 and PSRC1), miRNA (e.g., miR-424, miR-203, miR-1266, miR-1293, and miR-4772), and pancreatic cancer drugs (e.g., trifluoperazine dihydrochloride and trifluoperazine). In the near future, the information presented here will be highly useful in the early diagnosis as well as treatment of pancreatic malignancy.