Abstract
BACKGROUND: The relationship between molecular heterogeneity and clinical features of pancreatic cancer remains unclear. In this study, pancreatic cancer was divided into different subgroups to explore its specific molecular characteristics and potential therapeutic targets. PATIENTS AND METHODS: Expression profiling data were downloaded from The Cancer Genome Atlas database and standardized. Bioinformatics techniques such as unsupervised hierarchical clustering was used to explore the optimal molecular subgroups in pancreatic cancer. Clinical pathological features and pathways in each subgroup were also analyzed to find out the potential clinical applications and initial promotive mechanisms of pancreatic cancer. RESULTS: Pancreatic cancer was divided into three subgroups based on different gene expression features. Patients included in each subgroup had specific biological features and responded significantly different to chemotherapy. CONCLUSION: Three distinct subgroups of pancreatic cancer were identified, which means that patients in each subgroup might benefit from targeted individual management.