title: Epithelial-mesenchymal transition biomarkers and support vector machine guided model in preoperatively predicting regional lymph node metastasis for rectal cancer.
abstract: BACKGROUND
  Current imaging modalities are inadequate in preoperatively predicting regional lymph node metastasis (RLNM) status in rectal cancer (RC). Here, we designed support vector machine (SVM) model to address this issue by integrating epithelial-mesenchymal-transition (EMT)-related biomarkers along with clinicopathological variables.
  
  
  METHODS
  Using tissue microarrays and immunohistochemistry, the EMT-related biomarkers expression was measured in 193 RC patients. Of which, 74 patients were assigned to the training set to select the robust variables for designing SVM model. The SVM model predictive value was validated in the testing set (119 patients).
  
  
  RESULTS
  In training set, eight variables, including six EMT-related biomarkers and two clinicopathological variables, were selected to devise SVM model. In testing set, we identified 63 patients with high risk to RLNM and 56 patients with low risk. The sensitivity, specificity and overall accuracy of SVM in predicting RLNM were 68.3%, 81.1% and 72.3%, respectively. Importantly, multivariate logistic regression analysis showed that SVM model was indeed an independent predictor of RLNM status (odds ratio, 11.536; 95% confidence interval, 4.113-32.361; P<0.0001).
  
  
  CONCLUSIONS
  Our SVM-based model displayed moderately strong predictive power in defining the RLNM status in RC patients, providing an important approach to select RLNM high-risk subgroup for neoadjuvant chemoradiotherapy.
authors.affiliation: Gastrointestinal Institute of Sun Yat-sen University, the Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510655, China.
authors.forename: X-J
authors.initials: XJ
authors.lastname: Fan
authors.forename: X-B
authors.initials: XB
authors.lastname: Wan
authors.forename: Y
authors.initials: Y
authors.lastname: Huang
authors.forename: H-M
authors.initials: HM
authors.lastname: Cai
authors.forename: X-H
authors.initials: XH
authors.lastname: Fu
authors.forename: Z-L
authors.initials: ZL
authors.lastname: Yang
authors.forename: D-K
authors.initials: DK
authors.lastname: Chen
authors.forename: S-X
authors.initials: SX
authors.lastname: Song
authors.forename: P-H
authors.initials: PH
authors.lastname: Wu
authors.forename: Q
authors.initials: Q
authors.lastname: Liu
authors.forename: L
authors.initials: L
authors.lastname: Wang
authors.forename: J-P
authors.initials: JP
authors.lastname: Wang
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D019540:Area Under Curve; D014408:Biomarkers, Tumor / Q000032:analysis*; D058750:Epithelial-Mesenchymal Transition; D005260:Female; D006801:Humans; D007150:Immunohistochemistry; D008207:Lymphatic Metastasis; D008297:Male; D008875:Middle Aged; D009367:Neoplasm Staging; D011237:Predictive Value of Tests; D011379:Prognosis; D012372:ROC Curve; D012004:Rectal Neoplasms / Q000737:chemistry / Q000473:pathology*; D012307:Risk Factors; D060388:Support Vector Machine; D046888:Tissue Array Analysis; D055815:Young Adult
chemical_list: D014408:Biomarkers, Tumor