title: A novel detection method of non-small cell lung cancer using multiplexed bead-based serum biomarker profiling.
abstract: OBJECTIVE
  Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related mortality. Development of an early diagnosis method may improve survivals. We aimed to develop a new diagnostic model for NSCLC using serum biomarkers.
  
  
  METHODS
  We set up a patient group diagnosed with NSCLC (n = 122) and a healthy control group (n = 225). Thirty serum analytes were selected on the basis of previous studies and a literature search. An antibody-bead array of 30 markers was constructed using the Luminex bead array platform (Luminex Inc, Austin, Tex) and was analyzed. Each marker was ranked by importance using the random forest method and then selected. Using selected markers, multivariate classification algorithms were constructed and were validated by application to independent validation cohort of 21 NSCLC and 28 control subjects.
  
  
  RESULTS
  There was no difference in demographics between patients and the control population except for age (64.8 ± 10.0 for patients vs 53.0 ± 7.6 years for the control group). Among the 30 serum proteins, 23 showed a difference between the 2 groups (12 increased and 11 decreased in the patient group). We found the highest accuracy of multivariate classification algorithms when using the 5 highest-ranked biomarkers (A1AT, CYFRA 21-1, IGF-1, RANTES, AFP). When we applied the algorithms on a validation cohort, each method recognized the patients from the controls with high accuracy (89.8% with random forest, 91.8% with support vector machine, 88.2% with linear discriminant analysis, and 90.5% with logistic regression).
  
  
  CONCLUSIONS
  We confirmed that a new diagnostic method using 5 serum biomarkers profiling constructed by multivariate classification algorithms could distinguish NSCLC from healthy controls with high accuracy.
authors: Department of Molecular Oncology, Cancer Research Institute, Seoul National University Graduate School of Medicine, Seoul, Korea.
authors: Hyun Joo
authors: HJ
authors: Lee
authors: Young Tae
authors: YT
authors: Kim
authors: Pil Je
authors: PJ
authors: Park
authors: Yong Sung
authors: YS
authors: Shin
authors: Kyung Nam
authors: KN
authors: Kang
authors: Yongdai
authors: Y
authors: Kim
authors: Chul Woo
authors: CW
authors: Kim
mesh_terms: D000368:Aged; D000465:Algorithms; D000951:Antigens, Neoplasm / Q000097:blood; D014408:Biomarkers, Tumor / Q000097:blood*; D002289:Carcinoma, Non-Small-Cell Lung / Q000097:blood / Q000175:diagnosis*; D016022:Case-Control Studies; D018946:Chemokine CCL5 / Q000097:blood; D003661:Decision Support Techniques; D016002:Discriminant Analysis; D005260:Female; D005434:Flow Cytometry; D006801:Humans; D007118:Immunoassay; D007334:Insulin-Like Growth Factor I / Q000032:analysis; D053539:Keratin-19 / Q000097:blood; D016014:Linear Models; D016015:Logistic Models; D008175:Lung Neoplasms / Q000097:blood / Q000175:diagnosis*; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D011379:Prognosis; D015203:Reproducibility of Results; D056910:Republic of Korea; D060388:Support Vector Machine; D000515:alpha 1-Antitrypsin / Q000097:blood; D000509:alpha-Fetoproteins / Q000032:analysis
chemical_list: C097353:AFP protein, human; D000951:Antigens, Neoplasm; D014408:Biomarkers, Tumor; C483024:CCL5 protein, human; D018946:Chemokine CCL5; D053539:Keratin-19; C043392:SERPINA1 protein, human; D000515:alpha 1-Antitrypsin; D000509:alpha-Fetoproteins; C102012:antigen CYFRA21.1; D007334:Insulin-Like Growth Factor I