ABSTRACT
Epidermal growth factor receptor (EGFR) is usually overexpressed in nasopharyngeal carcinoma (NPC). Our recent in vitro study has demonstrated that cetuximab (an antibody drug against EGFR) inhibits the growth of NPC cell lines, HK1 and HONE-1. The present study investigates the effect of cetuximab on protein expressions of NPC cell lines. NPC cells were cultured in the absence or presence of cetuximab at the IC50 concentrations (3 nM for HK1 and 0.3 nM for HONE-1) for 48 h, and total cell lysates were extracted. The cell lysates were then subjected to two-dimensional polyacrylamide gel electrophoresis (2D PAGE), and the 2D gel images were compared to discover the protein changes caused by cetuximab treatment. The common differentially expressed proteins in NPC cell lines were identified by peptide mass fingerprinting. We found that heat shock protein gp96 was down-regulated, while alpha-enolase, tumor suppressor protein maspin, and p97 valosin containing protein were up-regulated after cetuximab treatment. Reverse-transcription polymerase chain reaction (RT-PCR) analysis confirmed that the changes in protein levels of gp96, maspin, and p97 coincided with mRNA levels, indicating that these proteins were regulated at transcriptional levels. Up-regulation of gp96 has been observed in various cancers and reported to have tumor protective effects. P97 is a multifunctional AAA (ATPase associated with a variety of activities) protein and is involved in numerous cellular activities including membrane transport, protein folding, protein degradation, and cell division. Maspin has been shown to increase apoptosis, and block the growth, invasion, and metastatic properties of many tumors. The comparative tumor suppression effects of cetuximab and maspin suggest that cetuximab might exert its antitumor effects partly by up-regulation of maspin expression. The study also indicates that proteomic analysis is a promising approach to elucidate the functional mechanisms of anticancer drugs. Pharmacoproteomic study may also help to identify clinical responders for drug treatment and provide insight for new drug development.
Subject(s)
Antibodies, Monoclonal/pharmacology , Antineoplastic Agents/pharmacology , Carcinoma/metabolism , Gene Expression Regulation, Neoplastic , Nasopharyngeal Neoplasms/metabolism , Proteins/metabolism , Adenosine Triphosphatases , Antibodies, Monoclonal, Humanized , Cell Cycle Proteins/metabolism , Cell Line, Tumor , Cetuximab , China , DNA Primers , Dose-Response Relationship, Drug , Electrophoresis, Gel, Two-Dimensional , Genes, Tumor Suppressor , Humans , Membrane Glycoproteins/metabolism , Peptide Mapping , Proteomics , Reverse Transcriptase Polymerase Chain Reaction , Serpins/metabolism , Valosin Containing ProteinABSTRACT
BACKGROUND: Most noninvasive predictive models of liver fibrosis are complicated and have suboptimal sensitivity. This study was designed to identify serum proteomic signatures associated with liver fibrosis and to develop a proteome-based fingerprinting model for prediction of liver fibrosis. METHODS: Serum proteins from 46 patients with chronic hepatitis B (CHB) were profiled quantitatively on surface-enhanced laser desorption/ionization (SELDI) ProteinChip arrays. The identified liver fibrosis-associated proteomic fingerprint was used to construct an artificial neural network (ANN) model that produced a fibrosis index with a range of 0-6. The clinical value of this index was evaluated by leave-one-out cross-validation. RESULTS: Thirty SELDI proteomic features were significantly associated with the degree of fibrosis. Cross-validation showed that the ANN fibrosis indices derived from the proteomic fingerprint strongly correlated with Ishak scores (r = 0.831) and were significantly different among stages of fibrosis. ROC curve areas in predicting significant fibrosis (Ishak score >or=3) and cirrhosis (Ishak score >or=5) were 0.906 and 0.921, respectively. At 89% specificity, the sensitivity of the ANN fibrosis index in predicting fibrosis was 89%. The sensitivity for prediction increased with degree of fibrosis, achieving 100% for patients with Ishak scores >4. The accuracy for prediction of cirrhosis was also 89%. Inclusion of International Normalized Ratio, total protein, bilirubin, alanine transaminase, and hemoglobin in the ANN model improved the predictive power, giving accuracies >90% for the prediction of fibrosis and cirrhosis. CONCLUSIONS: A unique serum proteomic fingerprint is present in the sera of patients with fibrosis. An ANN fibrosis index derived from this fingerprint could differentiate between different stages of fibrosis and predict fibrosis and cirrhosis in CHB infection.
