title: A prognostic model of triple-negative breast cancer based on miR-27b-3p and node status.
abstract: OBJECTIVE
  Triple-negative breast cancer (TNBC) is an aggressive but heterogeneous subtype of breast cancer. This study aimed to identify and validate a prognostic signature for TNBC patients to improve prognostic capability and to guide individualized treatment.
  
  
  METHODS
  We retrospectively analyzed the prognostic performance of clinicopathological characteristics and miRNAs in a training set of 58 patients with invasive ductal TNBC diagnosed between 2002 and 2012. A prediction model was developed based on independent clinicopathological and miRNA covariates. The prognostic value of the model was further validated in a separate set of 41 TNBC patients diagnosed between 2007 and 2008.
  
  
  RESULTS
  Only lymph node status was marginally significantly associated with poor prognosis of TNBC (P = 0.054), whereas other clinicopathological factors, including age, tumor size, histological grade, lymphovascular invasion, P53 status, Ki-67 index, and type of surgery, were not. The expression levels of miR-27b-3p, miR-107, and miR-103a-3p were significantly elevated in the metastatic group compared with the disease-free group (P value: 0.008, 0.005, and 0.050, respectively). The Cox proportional hazards regression analysis revealed that lymph node status and miR-27b-3p were independent predictors of poor prognosis (P value: 0.012 and 0.027, respectively). A logistic regression model was developed based on these two independent covariates, and the prognostic value of the model was subsequently confirmed in a separate validation set. The two different risk groups, which were stratified according to the model, showed significant differences in the rates of distant metastasis and breast cancer-related death not only in the training set (P value: 0.001 and 0.040, respectively) but also in the validation set (P value: 0.013 and 0.012, respectively).
  
  
  CONCLUSIONS
  This model based on miRNA and node status covariates may be used to stratify TNBC patients into different prognostic subgroups for potentially individualized therapy.
authors.affiliation: Department of Breast Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.
authors.forename: Songjie
authors.initials: S
authors.lastname: Shen
authors.affiliation: Department of Breast Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.
authors.forename: Qiang
authors.initials: Q
authors.lastname: Sun
authors.affiliation: Department of Pathology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.
authors.forename: Zhiyong
authors.initials: Z
authors.lastname: Liang
authors.affiliation: Department of Surgery, Department of Obstetrics and Gynecology, Women's Cancer Program, Samuel Oschin Comprehensive Cancer Institute, Cedars Sinai Medical Center, Los Angeles, California, United States of America.
authors.forename: Xiaojiang
authors.initials: X
authors.lastname: Cui
authors.affiliation: Department of Pathology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.
authors.forename: Xinyu
authors.initials: X
authors.lastname: Ren
authors.affiliation: Department of Microbiology, Zhejiang Institute of Microbiology, Hangzhou, Zhejiang, China.
authors.forename: Huan
authors.initials: H
authors.lastname: Chen
authors.affiliation: Biostatistics and Bioinformatics Core, Samuel Oschin Comprehensive Cancer Institute, Cedars Sinai Medical Center, Los Angeles, California, United States of America.
authors.forename: Xiao
authors.initials: X
authors.lastname: Zhang
authors.affiliation: Department of Breast Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.
authors.forename: Yidong
authors.initials: Y
authors.lastname: Zhou
mesh_terms: D000367:Age Factors; D000368:Aged; D018270:Carcinoma, Ductal, Breast / Q000175:diagnosis* / Q000235:genetics / Q000401:mortality / Q000473:pathology; D005260:Female; D006801:Humans; D019394:Ki-67 Antigen / Q000235:genetics / Q000378:metabolism; D008198:Lymph Nodes / Q000378:metabolism / Q000473:pathology*; D008207:Lymphatic Metastasis; D035683:MicroRNAs / Q000235:genetics* / Q000378:metabolism; D008875:Middle Aged; D015233:Models, Statistical; D060787:Neoplasm Grading; D011379:Prognosis; D016016:Proportional Hazards Models; D012189:Retrospective Studies; D016019:Survival Analysis; D064726:Triple Negative Breast Neoplasms / Q000175:diagnosis* / Q000235:genetics / Q000401:mortality / Q000473:pathology; D047368:Tumor Burden; D016159:Tumor Suppressor Protein p53 / Q000235:genetics / Q000378:metabolism
chemical_list: D019394:Ki-67 Antigen; C544749:MIRN103 microRNA, human; C549045:MIRN107 microRNA, human; C531388:MIRN27 microRNA, human; D035683:MicroRNAs; D016159:Tumor Suppressor Protein p53