title: Serum proteomics and biomarkers in hepatocellular carcinoma and chronic liver disease.
abstract: OBJECTIVE
  Proteomic profiling using surface enhanced laser desorption/ionization time of flight mass spectrometry (SELDI-TOF MS) enables the identification of biomarkers for cancer. We evaluated the sensitivity and specificity of SELDI-TOF MS for detection of established hepatocellular cancer (HCC) and compared it against alpha-fetoprotein (AFP), Lens culinaris agglutinin-reactive AFP (AFP-L3), and prothrombin induced by vitamin K absence-II (PIVKA-II).
  
  
  METHODS
  Forty-one patients with HCC and 51 patients with hepatitis C cirrhosis were enrolled. Serum was analyzed by SELDI-TOF MS using three Ciphergen protein array types.
  
  
  RESULTS
  An 11-peak algorithm for HCC detection was identified. Using the AFP cutoff of 20 ng/mL, the sensitivity was 73% and the specificity was 71%. Using the AFP-L3 cutoff of 10% yielded a sensitivity of 63% and a specificity of 94%. Using the PIVKA-II cutoff of 125 milliabsorbance units (mAU), the sensitivity was 84% and the specificity was 69%. Overall, the sensitivity and specificity of SELDI-TOF MS for HCC were 79% and 86%, respectively. In multivariate analysis, the 11-peak SELDI profile was predictive of HCC independent of AFP, PIVKA, and AFP-L3. Among eight patients with the largest tumor size of <2 cm, SELDI-TOF MS correctly identified seven whereas AFP, AFP-L3, and PIVKA-II identified only three, one, and one, respectively. One of the 11 peaks in the SELDI-TOF MS 11-peak predictor from SELDI-TOF MS was identified as cystatin C.
  
  
  CONCLUSIONS
  SELDI-TOF MS accurately distinguished patients with HCC from those with hepatitis C virus cirrhosis, was more accurate than traditional biomarkers in identifying small tumors, and should be further evaluated.
authors.affiliation: Liver Center, Department of Gastroenterology and Hepatology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA 02115, USA.
authors.forename: Noah T
authors.initials: NT
authors.lastname: Zinkin
authors.forename: Franck
authors.initials: F
authors.lastname: Grall
authors.forename: Killimangalam
authors.initials: K
authors.lastname: Bhaskar
authors.forename: Hasan H
authors.initials: HH
authors.lastname: Otu
authors.forename: Dimitrios
authors.initials: D
authors.lastname: Spentzos
authors.forename: Brett
authors.initials: B
authors.lastname: Kalmowitz
authors.forename: Meghan
authors.initials: M
authors.lastname: Wells
authors.forename: Manuel
authors.initials: M
authors.lastname: Guerrero
authors.forename: John M
authors.initials: JM
authors.lastname: Asara
authors.forename: Towia A
authors.initials: TA
authors.lastname: Libermann
authors.forename: Nezam H
authors.initials: NH
authors.lastname: Afdhal
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D014408:Biomarkers, Tumor / Q000097:blood*; D006528:Carcinoma, Hepatocellular / Q000097:blood*; D055316:Cystatin C; D015891:Cystatins / Q000097:blood; D005260:Female; D019698:Hepatitis C, Chronic / Q000097:blood*; D006801:Humans; D008103:Liver Cirrhosis / Q000097:blood*; D008113:Liver Neoplasms / Q000097:blood*; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D040901:Proteomics / Q000379:methods*; D012680:Sensitivity and Specificity; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization; D000509:alpha-Fetoproteins / Q000032:analysis
chemical_list: D014408:Biomarkers, Tumor; C525030:CST3 protein, human; D055316:Cystatin C; D015891:Cystatins; D000509:alpha-Fetoproteins