SELDI-TOF-MS技术在贲门癌诊断中的初步研究

目的寻找贲门癌肿瘤标记物并建立诊断模型。筛选提示发生淋巴转移的差异蛋白,为临床早期诊断贲门癌及判断预后提供新方法。方法 CM10蛋白芯片结合表面增强激光解析电离飞行时间质谱(SELDI-TOF-MS)检测101例贲门癌患者及118例正常对照者血清蛋白,Biomarker Wizard软件和Biomarker Patterns System(BPS)分析得差异蛋白并建立贲门癌诊断分类树,双盲法验证其准确性。进一步分析对比有、无淋巴转移的贲门癌患者血清蛋白指纹图谱,得到差异蛋白。结果对照分析贲门癌患者和正常对照组的血清蛋白指纹图谱得56个差异蛋白(P<0.05),经BPS软件筛选,以质荷比为5 480.49(P<10-8)的蛋白为主根结点建立贲门癌诊断模型,其灵敏度为95.05%,特异度为95.76%。得到可提示淋巴转移的差异蛋白3 820.87(P<0.05),其灵敏度为84.4%,特异度为82.1%。结论 SELDI-TOF-MS技术可建立有较高灵敏度和特异度的贲门癌诊断模型,筛选出能够提示发生淋巴转移的差异蛋白,有助于贲门癌的早期筛查、诊断和预后判断。

Molecular Identification of Castanopsis hystrix,Castanopsis carlesii and Quercus griffithii Using ISSR-PCR Method

[Objective] This research aimed to develop molecular identification method for Castanopsis hystrix,Castanopsis carlesii and Quercus griffithii.[Method] DNA fingerprints of C.hystrix,C.carlesii and Q.griffithii were established by using ISSR-PCR method.Cluster Analysis was carried out by using UPGMA method based on Nei＇s genetic distances among each individual.[Result] Six polymorphic primers were selected from 50 ISSR primers for ISSR-PCR amplification,and totally 86 discernible DNA bands were amplified with 53 polymorphic bands,accounting for 61.2% of the total.The average number of DNA bands amplified by each primer was 10.75.Specifically,totally 5 primers had amplified differential bands and specific bands,which were able to accurately identify C.hystrix,C.carlesii and Q.griffithii.As calculated by DPS v3.01 software,the genetic distances among test materials were ranged from 0.166 67 to 0.809 52,with an average of 0.563 57.[Conclusion] ISSR-PCR method can be used to identify C.hystrix,C.carlesii and Q.griffithii effectively.

Diagnostic model of saliva protein finger print analysis of patients with gastric cancer

AIM:To explore the method for early diagnosis of gastric cancer by screening the expression spectrum of saliva protein in gastric cancer patients using mass spectrometry for proteomics.METHODS:Proportional peptide mass fingerprints were obtained by analysis based on proteomics matrix-assisted laser desorption ionization time-of-flight/mass spectrometry.A diagnosis model was established using weak cation exchange magnetic beads to test saliva specimens from gastric cancer patients and healthy subjects.RESULTS:Significant differences were observed in the mass to charge ratio(m/z) peaks of four proteins(1472.78 Da,2936.49 Da,6556.81 Da and 7081.17 Da) between gastric cancer patients and healthy subjects.CONCLUSION:The finger print mass spectrum of saliva protein in patients with gastric cancer can be established using gastric cancer proteomics.A diagnostic model for distinguishing protein expression mass spectra of gastric cancer from non-gastric-cancer saliva can be established according to the different expression of proteins 1472.78 Da,2936.49 Da,6556.81 Da and 7081.17 Da.The method for early diagnosis of gastric cancer is of certain value for screening special biological markers.

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.