title: Molecular prognostic prediction for locally advanced nasopharyngeal carcinoma by support vector machine integrated approach.
abstract: BACKGROUND
  Accurate prognostication of locally advanced nasopharyngeal carcinoma (NPC) will benefit patients for tailored therapy. Here, we addressed this issue by developing a mathematical algorithm based on support vector machine (SVM) through integrating the expression levels of multi-biomarkers.
  
  
  RESULTS
  Ninety-seven locally advanced NPC patients in a randomized controlled trial (RCT), consisting of 48 cases serving as training set and 49 cases as testing set of SVM models, with 5-year follow-up were studied. We designed SVM models by selecting the variables from 38 tissue molecular biomarkers, which represent 6 tumorigenesis signaling pathways, and 3 EBV-related serological biomarkers. We designed 3 SVM models to refine prognosis of NPC with 5-year follow-up. The SVM1 displayed highly predictive sensitivity (sensitivity, specificity were 88.0% and 81.9%, respectively) by integrating the expression of 7 molecular biomarkers. The SVM2 model showed highly predictive specificity (sensitivity, specificity were 84.0% and 94.5%, respectively) by grouping the expression level of 12 molecular biomarkers and 3 EBV-related serological biomarkers. The SVM3 model, constructed by combination SVM1 with SVM2, displayed a high predictive capacity (sensitivity, specificity were 88.0% and 90.3%, respectively). We found that 3 SVM models had strong power in classification of prognosis. Moreover, Cox multivariate regression analysis confirmed these 3 SVM models were all the significant independent prognostic model for overall survival in testing set and overall patients.
  
  
  CONCLUSIONS
  Our SVM prognostic models designed in the RCT displayed strong power in refining patient prognosis for locally advanced NPC, potentially directing future target therapy against the related signaling pathways.
authors.affiliation: Department of Medical Oncology, the Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of China.
authors.forename: Xiang-Bo
authors.initials: XB
authors.lastname: Wan
authors.forename: Yan
authors.initials: Y
authors.lastname: Zhao
authors.forename: Xin-Juan
authors.initials: XJ
authors.lastname: Fan
authors.forename: Hong-Min
authors.initials: HM
authors.lastname: Cai
authors.forename: Yan
authors.initials: Y
authors.lastname: Zhang
authors.forename: Ming-Yuan
authors.initials: MY
authors.lastname: Chen
authors.forename: Jie
authors.initials: J
authors.lastname: Xu
authors.forename: Xiang-Yuan
authors.initials: XY
authors.lastname: Wu
authors.forename: Hong-Bo
authors.initials: HB
authors.lastname: Li
authors.forename: Yi-Xin
authors.initials: YX
authors.lastname: Zeng
authors.forename: Ming-Huang
authors.initials: MH
authors.lastname: Hong
authors.forename: Quentin
authors.initials: Q
authors.lastname: Liu
mesh_terms: D014408:Biomarkers, Tumor / Q000378:metabolism*; D002277:Carcinoma; D003936:Diagnosis, Computer-Assisted; D006801:Humans; D007150:Immunohistochemistry; D053208:Kaplan-Meier Estimate; D008954:Models, Biological; D000077274:Nasopharyngeal Carcinoma; D009303:Nasopharyngeal Neoplasms / Q000175:diagnosis* / Q000235:genetics*; D011379:Prognosis; D012044:Regression Analysis; D012680:Sensitivity and Specificity; D060388:Support Vector Machine
chemical_list: D014408:Biomarkers, Tumor