title: Molecular subtypes of pancreatic cancer based on miRNA expression profiles have independent prognostic value.
abstract: OBJECTIVE
  Altered microRNAs (miRNA) expression, a typical feature of many cancers, is reportedly associated with prognosis according to several studies. Although numerous studies on miRNAs in pancreatic ductal adenocarcinoma have also attempted to identify prognostic biomarkers, more large-scale clinical studies are needed to establish the clinical significance of the results. Present study aimed to identify prognosis-related molecular subtypes of primary pancreas tumors using miRNA expression profiling.
  
  
  METHODS
  Expression profiles of 1733 miRNAs were obtained by using microarray analysis of 104 pancreatic tumors of Korean patients. To detect subgroups informative in predicting the patient's prognosis, we applied unsupervised clustering methods and then analyzed the association of the molecular subgroups with survival time. Then, we constructed a classifier to predict the subgroup using penalized regression models.
  
  
  RESULTS
  We have determined three pancreatic ductal adenocarcinoma tumor subtypes associated with prognosis based on miRNA expression profiles. These subtypes showed significantly different survival time for patients with the same clinical conditions. This demonstrates that our prognostic molecular subgroup has independent prognostic utility. The molecular subtypes can be predicted with a classifier of 19 miRNAs. Of the 19 signature miRNAs, miR-106b-star, miR-324-3p, and miR-615 were related to a p53 canonical pathway, and miR-324, miR-145-5p, miR-26b-5p, and miR-574-3p were related to a Cox-2 centered pathway.
  
  
  CONCLUSIONS
  Our prognostic molecular subtypes demonstrated that miRNA profiles could be used as prognostic markers. Additionally, we have constructed a classifier that may be used to determine the molecular subgroup of new patient sample data. Further studies are needed for validation.
authors.affiliation: Bioinformatics Tech. Lab, Healthcare group, Future Technology R&D Division, SK Telecom, Co., Seoul, Korea.
authors.forename: Junghyun
authors.initials: J
authors.lastname: Namkung
authors.affiliation: Department of Surgery and Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea.|Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
authors.forename: Wooil
authors.initials: W
authors.lastname: Kwon
authors.affiliation: Bioinformatics Tech. Lab, Healthcare group, Future Technology R&D Division, SK Telecom, Co., Seoul, Korea.
authors.forename: Yonwhan
authors.initials: Y
authors.lastname: Choi
authors.affiliation: Bioinformatics Tech. Lab, Healthcare group, Future Technology R&D Division, SK Telecom, Co., Seoul, Korea.
authors.forename: Sung Gon
authors.initials: SG
authors.lastname: Yi
authors.affiliation: Bioinformatics Tech. Lab, Healthcare group, Future Technology R&D Division, SK Telecom, Co., Seoul, Korea.
authors.forename: Sangjo
authors.initials: S
authors.lastname: Han
authors.affiliation: Department of Surgery and Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea.
authors.forename: Mee Joo
authors.initials: MJ
authors.lastname: Kang
authors.affiliation: Department of Surgery and Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea.
authors.forename: Sun-Whe
authors.initials: SW
authors.lastname: Kim
authors.affiliation: Department of Statistics, Seoul National University, Seoul, Korea.
authors.forename: Taesung
authors.initials: T
authors.lastname: Park
authors.affiliation: Department of Surgery and Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea.
authors.forename: Jin-Young
authors.initials: JY
authors.lastname: Jang
mesh_terms: D000368:Aged; D014408:Biomarkers, Tumor / Q000235:genetics*; D021441:Carcinoma, Pancreatic Ductal / Q000145:classification / Q000235:genetics* / Q000401:mortality / Q000473:pathology; D002454:Cell Differentiation; D016009:Chi-Square Distribution; D016000:Cluster Analysis; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D053208:Kaplan-Meier Estimate; D008297:Male; D035683:MicroRNAs / Q000235:genetics*; D008875:Middle Aged; D009361:Neoplasm Invasiveness; D020411:Oligonucleotide Array Sequence Analysis; D010190:Pancreatic Neoplasms / Q000145:classification / Q000235:genetics* / Q000401:mortality / Q000473:pathology; D011237:Predictive Value of Tests; D011379:Prognosis; D016016:Proportional Hazards Models; D012044:Regression Analysis; D056910:Republic of Korea; D012189:Retrospective Studies
chemical_list: D014408:Biomarkers, Tumor; D035683:MicroRNAs
keywords: miRNA profile; molecular subtype; pancreatic ductal adenocarcinoma; prognosis