Proteomic signature corresponding to alpha fetoprotein expression in liver cancer cells

Proteomics for the early detection and treatment of hepatocellular carcinoma

The prognosis for hepatocellular carcinoma (HCC) is poor and has not improved in recent years, largely owing to lack of early diagnosis, frequent recurrence after surgery and resistance to chemotherapy. Proteomics holds the promise of improving our understanding of HCC carcinogenesis and progression as well as of discovering novel diagnostics and therapeutics. Proteomic analyses of HCC cell lines, animal models and serum and tumor tissue from patients with HCC have been performed to date. Proteomic technologies have greatly improved in the past few years as reviewed here. It is anticipated that with the recent development of protein tagging, protein separation methods and mass spectrometry sensitivity, proteomic studies of HCC will allow the identification of diagnostic and prognostic biomarkers as well as therapeutic targets, which could greatly improve the clinical management of HCC patients.

Proteomic analysis in cancer research: potential application in clinical use

The ultimate goal of cancer proteomics is to adapt proteomic technologies for routine use in clinical laboratories for the purpose of diagnostic and prognostic classification of disease states, as well as in evaluating drug toxicity and efficacy. The novel technologies allows researchers to facilitate the comprehensive analyses of genomes, transcriptomes, and proteomes in health and disease. The information that is expected from such technologies may soon exert a dramatic change in cancer research and impact dramatically on the care of cancer patients. Analysis of tumor-specific proteomic profiles may also allow better understanding of tumor development and the identification of novel targets for cancer therapy. The localization of gene products, which is often difficult to deduce from the sequence, can be determined experimentally. Mechanisms, such as regulation of protein function by proteolysis, recycling, and isolation in cell compartments, affect gene products, not genes. Finally, protein-protein interactions and the molecular composition of cellular structures can be determined only at the protein level. The biological variability among patient samples as well as the great dynamic range of biomarker concentrations are currently the main challenges facing efforts to deduce diagnostic patterns that are unique to specific disease states. While several strategies exist to address this problem, we have tried to offer a wide perspective about the current possibilities.

Genetic Classification of Lung Adenocarcinoma Based on Array-Based Comparative Genomic Hybridization Analysis: Its Association with Clinicopathologic Features

The array-based comparative genomic hybridization using microarrayed bacterial artificial chromosome clones allows high-resolution analysis of genome-wide copy number changes in tumors. To analyze the genetic alterations of primary lung adenocarcinoma in a high-throughput way, we used laser-capture microdissection of cancer cells and array comparative genomic hybridization focusing on 800 chromosomal loci containing cancer-related genes. We identified a large number of chromosomal numerical alterations, including frequent amplifications on 7p12, 11q13, 12q14-15, and 17q21, and two homozygous deletions on 9p21 and one on 8p23. Unsupervised hierarchical clustering analysis of multiple alterations revealed three subgroups of lung adenocarcinoma that were characterized by the accumulation of distinct genetic alterations and associated with smoking history and gender. The mutation status of the epidermal growth factor receptor (EGFR) gene was significantly associated with specific genetic alterations and supervised clustering analysis based on EGFR gene mutations elucidated a subgroup including all EGFR gene mutated tumors, which showed significantly shorter disease-free survival. Our results suggest that there exist multiple molecular carcinogenesis pathways in lung adenocarcinoma that may associate with smoking habits and gender, and that genetic cancer profiling will reveal previously uncharacterized genetic heterogeneity of cancer and be beneficial in estimating patient prognosis and discovering novel cancer-related genes including therapeutic targets.