Proteomic investigation of 5-fluorouracil resistance in a human hepatocellular carcinoma cell line

Multi-drug resistance (MDR) is a major obstacle towards a successful treatment of hepatocellular carcinoma (HCC). The mechanisms of MDR are intricate and have not been fully understood. Therefore, we employed a cell-line model consisting of the 5-fluorouracil (5-FU) resistant BEL7402/5-FU cell line and its parental BEL7402 cell line. Using relative and absolute quantification (iTRAQ)-coupled 2D LC-MS/MS, a successfully exploited high-throughput proteomic technology, in total, 660 unique proteins were identified and 52 proteins showed to be differentially expressed in BEL7402/5-FU compared with BEL7402. Several differentially expressed proteins were further validated by Western blot and real-time quantitative RT-PCR analysis. Furthermore, the association of MDR with ANXA3, one of the highly expressed proteins in BEL7402/5-FU, was verified. Our study represents the first successful application of iTRAQ technology for MDR mechanisms analysis in HCC. Many of the differentially expressed proteins identified had not been linked to MDR in HCC before, which provide valuable information for further understanding of MDR.     

Identifying FGA peptides as nasopharyngeal carcinoma-associated biomarkers by magnetic beads

Early diagnosis and treatment is known to improve prognosis for nasopharyngeal carcinoma (NPC). The study determined the specific peptide profiles by comparing the serum differences between NPC patients and healthy controls, and provided the basis for the diagnostic model and identification of specific biomarkers of NPC. Matrix‐assisted laser desorption/ionization time‐of‐flight mass spectrometry (MALDI‐TOF‐MS) can be used to detect the molecular mass of peptides. Mass spectra of peptides were generated after extracting and purification of 40 NPC samples in the training set, 21 in the single center validation set and 99 in the multicenter validation set using weak cationic‐exchanger magnetic beads. The spectra were analyzed statistically using FlexAnalysis? and ClinProt? bioinformatics software. The four most significant peaks were selected out to train a genetic algorithm model to diagnose NPC. The diagnostic sensitivity and specificity were 100% and 100% in the training set, 90.5% and 88.9% in the single center validation set, 91.9% and 83.3% in the multicenter validation set, and the false positive rate (FPR) and false negative rate (FNR) were obviously lower in the NPC group (FPR, 16.7%; FNR, 8.1%) than in the other cancer group (FPR, 39%; FNR, 61%), respectively. So, the diagnostic model including four peptides can be suitable for NPC but not for other cancers. FGA peptide fragments identified may serve as tumor‐associated biomarkers for NPC. J. Cell. Biochem. 113: 2268–2278, 2012. © 2012 Wiley Periodicals, Inc.