title: Identification of plasma protein profiles associated with risk groups of prostate cancer patients.
abstract: OBJECTIVE
  Early detection of prostate cancer (PC) using prostate-specific antigen (PSA) in blood reduces PC-death among unscreened men. However, due to modest specificity of PSA at commonly used cut-offs, there are urgent needs for additional biomarkers contributing enhanced risk classification among men with modestly elevated PSA.
  
  
  METHODS
  Recombinant antibody microarrays were applied for protein expression profiling of 80 plasma samples from routine PSA-measurements, a priori divided into four risk groups, based on levels of total and %free PSA.
  
  
  RESULTS
  The results demonstrated that plasma protein profiles could be identified that pin-pointed PC (a malignant biomarker signature) and most importantly that showed moderate to high correlation with biochemically defined PC risk groups. Notably, the data also implied that the risk group with midrange PSA and low %free PSA, a priori known to be heterogeneous, could be further stratified into two subgroups, more resembling the lowest and highest risk groups, respectively.
  
  
  CONCLUSIONS
  In this pilot study, we have shown that plasma protein biomarker signatures, associated with risk groups of PC, could be identified from crude plasma samples using affinity proteomics. This approach could in the longer perspective provide novel opportunities for improved risk classification of PC patients.
authors.affiliation: Department of Immunotechnology, Lund University, Medicon Village, Lund, Sweden; CREATE Health, Lund University, Medicon Village, Lund, Sweden.
authors.forename: Malin
authors.initials: M
authors.lastname: Nordström
authors.forename: Christer
authors.initials: C
authors.lastname: Wingren
authors.forename: Carsten
authors.initials: C
authors.lastname: Rose
authors.forename: Anders
authors.initials: A
authors.lastname: Bjartell
authors.forename: Charlotte
authors.initials: C
authors.lastname: Becker
authors.forename: Hans
authors.initials: H
authors.lastname: Lilja
authors.forename: Carl A K
authors.initials: CA
authors.lastname: Borrebaeck
mesh_terms: D000368:Aged; D014408:Biomarkers, Tumor / Q000097:blood / Q000378:metabolism; D001798:Blood Proteins / Q000378:metabolism*; D042241:Early Diagnosis; D006801:Humans; D008297:Male; D008875:Middle Aged; D010865:Pilot Projects; D017430:Prostate-Specific Antigen / Q000097:blood / Q000378:metabolism; D011471:Prostatic Neoplasms / Q000097:blood / Q000175:diagnosis / Q000378:metabolism*; D020543:Proteome / Q000378:metabolism*; D040901:Proteomics / Q000379:methods*; D012372:ROC Curve; D015203:Reproducibility of Results; D012307:Risk Factors
chemical_list: D014408:Biomarkers, Tumor; D001798:Blood Proteins; D020543:Proteome; D017430:Prostate-Specific Antigen
keywords: Affinity proteomics; Antibody microarray; Biomarker; Diagnosis; Prostate cancer