Comprehensive Proteomic Profiling Identifies Serum Proteomic Signatures for Detection of Hepatocellular Carcinoma and Its Subtypes

Abstract

Background: Detection of hepatocellular carcinoma (HCC) in patients with chronic liver disease (CLD) is difficult. We investigated the use of comprehensive proteomic profiling of sera to differentiate HCC from CLD. Methods: Proteomes in sera from 20 CLD patients with ␣-fetoprotein (AFP) <500 g/L (control group) and 38 HCC patients (disease group) were profiled by anionexchange fractionation (first dimension), two types (IMAC3 copper and WCX2) of ProteinChip ® Arrays (second dimension), and time-of-flight mass spectrometry (third dimension). Bioinformatic tests were used to identify tumor-specific proteomic features and to estimate the values of the tumor-specific proteomic features in the diagnosis of HCC. Cross-validation was performed, and we also validated the models with pooled sera from the control and disease groups, serum from a CLD patient with AFP >500 g/L, and postoperative sera from two HCC patients. Results: Among 2384 common serum proteomic features, 250 were significantly different between the HCC and CLD cases. Two-way hierarchical clustering differentiated HCC and CLD cases. Most HCC cases with advanced disease were clustered together and formed

Identification of serum biomarkers for colon cancer by proteomic analysis

“…The significant proteomic features common to both experiments were considered as suitable candidates for purification and identification. The initial strategy to isolate the peaks of interest was based on the serum fractionation protocol used by Poon et al (2003). The sera were diluted in a buffer designed to disrupt protein/protein interactions (9 M urea and 2% CHAPS, pH 9).…”

“…The initial step in the purification of the 6.44, 6.64, 8.94 and 50.7 kDa peaks (based on a method validated by our group previously; Poon et al, 2003) was to dilute the serum (100 ml) threefold in 9 M urea, 50 mM Tris/HCl, pH 9 and 2% CHAPS buffer, and apply it to Q Ceramic HyperD F anion exchange beads (Pall, New York, USA) in spin-cup filters (Pierce, Rockford, Illinois, USA). The proteins that did not bind were collected by centrifugation.…”

“…Multivariate analysis can then be used to determine whether the intensities of the peaks in the SELDI spectra of different patient groups possess discriminatory ability. Studies have suggested the possible utility of SELDI analysis in diagnosing ovarian (Petricoin et al, 2002;Kozak et al, 2003), prostate Qu et al, 2002;Banez et al, 2003), breast (Li et al, 2002), bladder (Vlahou et al, 2001), hepatic (Poon et al, 2003;Ward et al, 2006) and pancreatic cancer using serum. Concerns with several aspects of this approach have been raised, including potential bias in sample collection protocols, accuracy/ resolution of the PBS IIc mass spectrometer employed in SELDI analysis, alignment of the detected peaks and over fitting of the data (Ransohoff, 2004).…”

Array-based proteomics: mapping of protein circuitries for diagnostics, prognostics, and therapy guidance in cancer

“…Using a two-way clustering algorithm, hepatocellular carcinoma was successfully differentiated from chronic liver disease [47]. A further study utilizing reverse-phase protein microarrays demonstrated segregation of primary colorectal cancer from metastatic disease to the liver by a similar clustering algorithm [48].…”

“…Artificial neural networks offer a further algorithm, which can be trained to recognize and classify complex patterns. This method has been used to discriminate comprehensive proteomic profiles of sera from cancer patients from individuals without cancer [47].…”