A Proteomic Approach for the Discovery of Early Detection Markers of Hepatocellular Carcinoma. Issue 3 (13th June 2013)

• Record Type: Journal Article
• Authors: Steel, Laura F.; Mattu, Taj S.; Mehta, Anand; Hebestreit, Holger; Dwek, Raymond; Evans, Alison A.; London, W. Thomas; Block, Timothy
• Is Part Of: Disease markers. Volume 17:Issue 3(2001)
• Page Start: 179
• Page End: 189
• Publication Date: 2013-06-13
• Subjects: Diagnosis -- Periodicals; Biochemical markers -- Periodicals; Pathology -- Periodicals; 616
• DOI: 10.1155/2001/963023
• Languages: English
• ISSNs: 0278-0240

Abstract

Abstract : Individuals chronically infected with hepatitis B or C virus (HBV, HCV) are at high risk for the development of hepatocellular carcinoma (HCC), with disease progression occurring relentlessly over many years. The diagnosis of HCC usually occurs at late stages in the disease when there are few effective treatment options and the prognosis for patients with HCC is very poor. The long latency period, together with clearly identified at risk populations, provide opportunities for earlier detection that will allow more timely and effective treatment of this devastating cancer. We are using a proteomic approach to test the hypothesis that changes in the amount of certain serum polypeptides, or changes in their post-translational modifications, can be used to predict the onset of HCC. Advances in the standardization of two dimensional gel electrophoresis (2DE) coupled with computerized image analysis now permit the reproducible resolution of thousands of polypeptides per run. Serum polypeptides from individuals at different stages in the disease continuum are being resolved by 2DE to identify those that change with disease progression. Polypeptides found by this method can be further characterized by mass spectrometry. In addition, the potential for changes in the glycan structure of certain polypeptides to serve as a marker for disease progression can be explored. The proteomic approach is expected to liberate us from the need to "cherry pick" or guess the best biomarkers and let the data tell us which are the best indicators of disease. Information may also be gleaned about the pathobiology of the disease process.