Preclinical and post-treatment changes in the HCC-associated serum proteome.

SELDI-based proteomic profiling of body fluids is currently in widespread use for cancer biomarker discovery. We have successfully used this technology for the diagnosis of hepatocellular carcinoma (HCC) in hepatitis C patients and now report its application to serial serum samples from 37 hepatitis C patients before development of HCC, with HCC and following radiofrequency ablation of the tumour. As with alpha-fetoprotein, an accepted biomarker for HCC, we hypothesised that HCC-associated proteomic features would 'return to normal' following successful treatment and the primary aim of our study was to test this hypothesis. Several SELDI peaks that changed significantly during HCC development were detected but they did not reverse following treatment. These data may be interpreted to suggest that the characteristic SELDI profile is not linearly related to tumour burden but may result from the progression of underlying liver disease or from the emergence of precancerous lesions. beta2-Microglobulin, a protein previously reported to be markedly elevated in patients with HCV related HCC, was also the most significantly HCC associated proteomic feature (m/z 11720) in this study.

Changes in the serum proteome associated with the development of hepatocellular carcinoma in hepatitis C-related cirrhosis.

Early diagnosis of hepatocellular carcinoma (HCC) is the key to the delivery of effective therapies. The conventional serological diagnostic test, estimation of serum alpha-fetoprotein (AFP) lacks both sensitivity and specificity as a screening tool and improved tests are needed to complement ultrasound scanning, the major modality for surveillance of groups at high risk of HCC. We have analysed the serum proteome of 182 patients with hepatitis C-induced liver cirrhosis (77 with HCC) by surface-enhanced laser desorption/ionisation time-of-flight mass spectrometry (SELDI). The patients were split into a training set (84 non-HCC, 60 HCC) and a 'blind' test set (21 non-HCC, 17 HCC). Neural networks developed on the training set were able to classify the blind test set with 94% sensitivity (95% CI 73-99%) and 86% specificity (95% CI 65-95%). Two of the SELDI peaks (23/23.5 kDa) were elevated by an average of 50% in the serum of HCC patients (P<0.001) and were identified as kappa and lambda immunoglobulin light chains. This approach may permit identification of several individual proteins, which, in combination, may offer a novel way to diagnose HCC.