Proteomic Analysis for Finding Serum Pathogenic Factors and Potential Biomarkers in Multiple Myeloma

Background:

Multiple myeloma (MM) is a malignant tumor, which takes the second place in malignant blood disease. The clinical symptoms are complicated that make more difficult to diagnose and therapy. Lots of researches focus on the proteins about MM in order to solve those problems. We used proteomic methods to find potential biomarkers in MM patients.

Methods:

We applied the peptide ligand library beads (PLLBs) to deplete high abundance proteins in serum for finding potential pathogenic factors and biomarkers of MM. Using 1D-Gel-liquid chromatography-tandem mass spectrometry (LC-MS/MS), we identified 789 and 849 unique serum proteins in MM patients and in healthy controls, respectively.

Results:

Twenty-two proteins were found differentially expressed between the two groups including serum amyloid A protein, vitamin D-binding protein isoform-1 precursor, plasma kallikrein, and apolipoprotein A-I. Changes of integrin alpha-11 and isoform-1 of multimerin-1 were validated with Western blotting. The linkage of the differentially expressed proteins and the pathogenesis pathways of MM were discussed.

Conclusions:

PLLB combined with 1D-gel-LC-MS/MS analysis is an efficient method to identify differentially expressed proteins in serum from patients with MM.     

Applied research on serum protein fingerprints for prediction of Qi deficiency syndrome and phlegm and blood stasis in patients with non-small cell lung cancer

OBJECTIVE:This study screened serum tumor biomarkers by surface enhanced laser desorption/ionization time-of-flight mass spectrometry(SELDI-TOF-MS) to establish a subset which could be used for the prediction of Qi deficiency syndrome and phlegm and blood stasis in patients with non-small cell lung cancer;and as diagnostic model of Chinese medicine.METHODS:Serum samples from 63 lung cancer patients with Qi deficiency syndrome and phlegm and blood stasis,and 28 lung cancer patients with non-Qi deficiency syndrome and phlegm and blood stasis were analyzed using SELDI-TOF-MS with a PBS II-C protein chip reader.Protein profiles were generated using immobilized metal affinity capture(IMAC3) protein chips.Differentially-expressed proteins were screened.Protein peak clustering and classification analyses were performed using Biomarker Wizard and Biomarker Pattern software packages,respectively.RESULTS:A total of 268 effective protein peaks were detected in the 1,000-10,000 Da molecular range for the 15 serum proteins screened(P<0.05).The decision tree model was M 2284.97,with a sensitivity of 96.2% and a specificity of 66.7%.CONCLUSION:SELDI-TOF-MS techniques,combined with a decision tree model,can help identify serum proteomic biomarkers related to Qi deficiency syndrome and phlegm and blood stasis in lung cancer patients;and the predictive model can be used to discriminate between Chinese medicine diagnostic models of disease.     

血清蛋白质质谱与支持向量机模型在胰腺癌检测上的应用

目的:为提高胰腺癌的早期检测率筛选新的标志物,应用蛋白芯片结合表面增强激光解吸电离飞行时间质谱(SELDI-TOF-MS)技术建立胰腺癌的血清蛋白质质谱模型。方法:用弱阳离子交换芯片(CM10)结合SELDI-TOF-MS技术检测了73例血清样本,其中31例胰腺癌,22例胰腺炎,20例健康人。用支持向量机方法建立胰腺癌和健康人以及胰腺癌和胰腺炎的辨别模型。结果:胰腺癌和健康人辨别模型用了3个蛋白质峰,辨别的敏感性和特异性均为100%,而胰腺癌和胰腺炎辨别模型用了5个蛋白质峰,辨别的特异性和敏感性分别为95.5%和93.5%。结论:SELDI-TOF-MS技术结合生物信息学方法检测胰腺癌具有较高的敏感性和特异性。