Proteomics for hepatocellular carcinoma marker discovery.
ABSTRACT Refinements of serological markers and screening of patients at high risk for developing hepatocellular carcinoma (HCC) may lead to better HCC detection, earlier intervention, and successful treatment, improving long-term outcomes. Proteomics promises the discovery of biomarkers for early HCC detection and diagnosis. Proteomic-based profiling uniquely allows delineation of global changes in expression patterns resulting from transcriptional and posttranscriptional control, posttranslational modifications, and shifts in proteins between cellular compartments. Approaches to that effect include direct serum protein profiling and comparative analysis of protein expression in normal, precancerous, and early-stage tumor tissues. Identification of panels of tumor antigens that elicit a humoral response also may contribute to the discovery of new markers for HCC screening and diagnosis. Today, 2-dimensional polyacrylamide gel electrophoresis, multidimensional liquid chromatography, mass spectrometry, and protein microarrays are among the proteomic tools available for biomarker and drug target discovery. We review these technologies and their application to the study of HCC. Our objective is to provide a framework for appreciating the promise, while at the same time understanding the challenges behind translating proteomics discovery into novel diagnostic tests.
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ABSTRACT: Despite great progress in the treatment of hepatocellular carcinoma (HCC) over the last-decade, intrahepatic recurrence is still the most frequent serious adverse event after all the treatments including microwave ablation. This study aimed to predict early recurrence of HCC after microwave ablation using serum proteomic signature. After curative microwave ablation of HCC, 86 patients were followed-up for 1 year. Serum samples were collected before microwave ablation. The mass spectra of proteins were generated using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS). Serum samples from 50 patients were randomly selected as a training set and for biomarkers discovery and model development. The remaining serum samples were categorized for validation of the algorithm. According to preablation serum protein profiling obtained from the 50 HCC samples in the training set, nine significant differentially-expressed proteins were detected in the serum samples between recurrent and non-recurrent patients. Decision classification tree combined with three candidate proteins with m/z values of 7787, 6858 and 6646 was produced using Biomarker Patterns Software with sensitivity of 85.7% and specificity of 88.9% in the training set. When the SELDI marker pattern was tested with the blinded testing set, it yielded a sensitivity of 80.0%, a specificity of 88.5% and a positive predictive value of 86.1%. Differentially-expressed protein peaks in preablation serum screened by SELDI are associated with prognosis of HCC. The decision classification tree is a potential tool in predicting early intrahepatic recurrence in HCC patients after microwave ablation.PLoS ONE 01/2013; 8(12):e82448. · 3.53 Impact Factor
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ABSTRACT: Hepatocellular carcinoma (HCC) is one of the primary hepatic malignancies and is the third most common cause of cancer related death worldwide. Although a wealth of knowledge has been gained concerning the initiation and progression of HCC over the last half century, efforts to improve our understanding of its pathogenesis at a molecular level are still greatly needed, to enable clinicians to enhance the standards of the current diagnosis and treatment of HCC. In the post-genome era, advanced mass spectrometry driven multi-omics technologies (e.g., profiling of DNA damage adducts, RNA modification profiling, proteomics, and metabolomics) stand at the interface between chemistry and biology, and have yielded valuable outcomes from the study of a diversity of complicated diseases. Particularly, these technologies are being broadly used to dissect various biological aspects of HCC with the purpose of biomarker discovery, interrogating pathogenesis as well as for therapeutic discovery. This proof of knowledge-based critical review aims at exploring the selected applications of those defined omics technologies in the HCC niche with an emphasis on translational applications driven by advanced mass spectrometry, toward the specific clinical use for HCC patients. This approach will enable the biomedical community, through both basic research and the clinical sciences, to enhance the applicability of mass spectrometry-based omics technologies in dissecting the pathogenesis of HCC and could lead to novel therapeutic discoveries for HCC. © 2014 Wiley Periodicals, Inc. Mass Spec Rev.Mass Spectrometry Reviews 06/2014; · 7.74 Impact Factor
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ABSTRACT: Hepatocellular carcinoma (HCC) represents one of the leading causes of cancer death and has proved to be highly refractory to treatment. Extensive analysis of the disease has demonstrated that it arises predominantly in response to high-risk etiological challenges, most notably hepatitis virus. However, with evolving vaccination and the obesity epidemic, progressively more cases are associated with underlying metabolic dysfunction. Pathologically diverse forms of HCC are observed, and recent sequencing analysis has defined common events that target well-known cancer pathways including β-catenin/Axin, TP53, and RB/CDKN2A, as well as frequent aberrations in chromatin remodeling factors. However, there are a myriad of low frequency genetic events that make each HCC case unique. Gene expression profiling approaches have successfully been deployed for prognostic assessment of hepatocellular carcinoma and to detect the earliest stages of disease. Despite more extensive research, systemic treatment for HCC is exceedingly limited, with only a handful of drugs providing benefit. Ongoing clinical trials are attempting to exploit specific biological dependencies of HCC to improve the dismal prognosis. Overall, the future of HCC treatment will rely on an understanding of the interplay between etiological factors, molecular features of disease, and rational therapeutic intervention.American Journal Of Pathology 12/2013; · 4.60 Impact Factor