Analysis Of CD34 Positive Microvascular Patterns Clinical Outcome And The Perivascular Niche In Glioblastoma

Chapter 1: CD34 Positive Microvascular Patterns ClinicalOutcome in glioblastomaObjective: Through the K-Means cluster analysis Glioblastoma(GBM)in different CD34 + microvascular microvascular patterns(MVPs)to explore different MVPs classification relationship with prognosis.Method: We evaluated MVPs of 61 glioblastomas by CD34 immunofluorescence staining and by cluster analysis of the percentage of microvascular area for distinct microvascular formations.The determination of tumor microvascular area percentage(MVA%),We using the K-Means cluster analysis to classify the characteristic of MVP.The survival probabilities were computed by using the Kaplan-Meier method.Log-rank test and Cox proportional hazards regression model were used for univariate and multivariate analyses of PFS and OS.Results: CD34 + in glioblastoma tumor microvascular phenotype can be divided into four kinds: the specific blood vessels(microvascular sprouting,MS),the hyperplasia of cluster blood vessels(vascular cluster,VC),the wreath sample vascular plexus(vascular garland,VG)and glomerular sample arteriolar(glomeruloid vascular effort,GVP).Through the analysis of the composition of each phenotype,we found that contains less of the MS and VC that contains rich VG and GVP in glioblastoma.Based on these characteristics by the clustering analysis of glioblastoma MVPs can be divided into two Types,type I comprised primarily of MS and VC and type II with many VG and GVP.Univariate analysis showed that the OS and PFS of patients with tumor of type II MVP was significantly inferior to that of type I MVP(P< 0.05).Multivariate analysis indicate that the MVP is an independent prognostic factors for PFS and OS in glioblastoma(P < 0.05).Conclusion: Glioblastoma CD34 positive MVPs obvious heterogeneity,can be simply divided into two categoriesy by certain histopathologic and statistical analysesand may influence clinical outcome.Chapter 2: The Pathological Structure of Perivascular Niche inDifferent Microvascular Patterns of GlioblastomaObjective: Learnning glioblastoma of vascular niche in different mode of microvascular pathological structure,provide relevant parameters in vitro glioma niche.Method: We investigated the distribution and biological characteristics of different microvascular patterns niche(MVPN)in glioblastoma(GBM)by detecting CD34,CD133,Nestin,α-SMA,GFAP and CD14 in perivascular niche using multiple immunofluorescence.The four basic microvascular patterns are microvascular sprouting(MS),vascular cluster(VC),vascular garland(VG),and glomeruloid vascular proliferation(GVP).Results: By analyzing the proportion of the area of each marker in four types of formation,the results indicated that the expression of CD34,CD133 and Nestin in MS,VC was significantly lower than that in the VG,GVP(P<0.05).Furthermore,the results showed that α-SMA expression increased as the increase of microvascular heterogeneity(P<0.05).Yet GFAP and CD14 expression in each types of formation exhibited no statistical difference(P>0.05).Conclusion: Four different MVPs classification of vascular niche pathological structure and cell distribution exist obvious differences,can provide reference data for in vitro glioblastoma niche.Chapter 3: Perivascular Niches proteomic distributions inGlioblastoma by imaging mass spectrometry Objective: Matrix-assisted laser desorption/ionization imaging massspectrometry(MALDI-IMS)is playing a key role in cancer research and it can be useful to unravel the molecular profile of thin tissue sections of Glioblastoma Multiforme(GBM)related to the vessels.The disease state of the tissue sections was determined by independent histopathological examination.Method: By immunofluorescence method to detect CD34 positive expression of capillaries in the brain glioblastoma and classification,application Autoflex MALDI-TOF mass spectrometer in the mass-to-charge ratio range of 1000-10000 brain glioblastoma tissue biopsies scanned for mass spectrometry,using software to MVPs classification of glioblastoma after statistical analysis.Pick out the expression differences of protein molecules to compose a quality,and the like,observe different mass-to-charge ratio of protein molecules in different of glioblastoma brain microvascular distribution pattern in the week of vascular niche.Results: Multivariate statistics revealed a number of mass ion peaks obtained from different tumor regions that were distinguishable from the adjacent heterogeneous vessels regions within a given specimen.These ion peaks have been used to generate ion images and visualize the difference between tumor and heterogeneous vessels regions.Mass peaks at m/z 1037 and 8960 exhibited stronger ion signals in the heterogeneous vessels regions,and 3240,3265 exhibited stronger ion signals in the less vessels regions.Conclusion: This study reports statistically significant mass ion peaks associated with tumor vascular regions in Primary glioblastoma and adds to the clinical utility of MALDI-IMS for analysis of FFPE tissue at a molecular level that supersedes all other standard histopathologic techniques for diagnostic purposes used in the current clinical practice.