title: [Study of serum proteome biomarkers with relation to the formation of portal vein tumor thrombi in hepatocellular carcinoma patients].
abstract: OBJECTIVE
  To screen serum proteome biomarkers and establish predictive model with relation to the formation of portal vein tumor thrombi (PVTT) in hepatocellular carcinoma (HCC) patients.
  
  
  METHODS
  Serum samples were collected from 135 HCC patients, which were divided, into training set (including 33 HCC patients with PVTT and 62 HCC patients without PVTT) and blind testing set (including 18 HCC patients with PVTT and 22 HCC patients without PVTT). Special serum protein or peptide pattern was determined by SELDI-TOF-MS measurement after treating the sample onto WCX2 protein chip for each case. The obtained data were analyzed by BioMarker Wizard software to screen serum proteome biomarkers with relation to the formation of PVTT, while decision tree classification algorithm and blind validation were determined by Biomarker Patterns Software.
  
  
  RESULTS
  Ranging from 1100 to 30 000 at the m/z value, 100 protein features were detected in the serum protein pattern stably. Among them, 6 protein peaks with the m/z value of 3478, 1314, 1744, 1725, 2022 and 3380 were upregulated, 10 proteins peaks with the m/z value of 8901, 9353, 9415, 8773, 2766, 2745, 8697, 7773, 8569 and 1373 were downregulated respectively in the group of HCC with PVTT. The 7 candidate protein peaks with the m/z value of 3478, 2022, 8901, 9415, 8773, 2766 and 2745 were selected to establish predictive model by BPS with a sensitivity of 75.8% (25/33) and specificity of 82.3% (51/62). An accuracy of 87.5% (35/40), sensitivity of 100% (18/18), specificity of 77.3% (17/22), positive predictive value of 78.3% (18/23), and negative predictive value of 100% (17/17) were validated in blind testing set.
  
  
  CONCLUSIONS
  Sixteen candidate proteome biomarkers may be related with the formation of PVTT in HCC patients. Decision tree classification algorithm may have great clinical significance in predicting the formation of PVTT.
authors.affiliation: Liver Cancer Institute & Department of Liver Surgery, Zhongshan Hospital, Fudan University, Shanghai 200032, China.
authors.forename: Cheng
authors.initials: C
authors.lastname: Huang
authors.forename: Jia
authors.initials: J
authors.lastname: Fan
authors.forename: Jian
authors.initials: J
authors.lastname: Zhou
authors.forename: Yin-kun
authors.initials: YK
authors.lastname: Liu
authors.forename: Jie-feng
authors.initials: JF
authors.lastname: Cui
authors.forename: Xiao-nan
authors.initials: XN
authors.lastname: Kang
authors.forename: Peng-yuan
authors.initials: PY
authors.lastname: Yang
authors.forename: Zhao-you
authors.initials: ZY
authors.lastname: Tang
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D014408:Biomarkers, Tumor / Q000378:metabolism*; D006528:Carcinoma, Hepatocellular / Q000473:pathology*; D003663:Decision Trees; D005260:Female; D006801:Humans; D008113:Liver Neoplasms / Q000473:pathology*; D008297:Male; D008875:Middle Aged; D009360:Neoplastic Cells, Circulating / Q000378:metabolism* / Q000473:pathology; D011169:Portal Vein; D020543:Proteome / Q000378:metabolism*; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization
chemical_list: D014408:Biomarkers, Tumor; D020543:Proteome