• Users Online: 74
  • Print this page
  • Email this page


 
 
Table of Contents
ORIGINAL ARTICLE
Year : 2018  |  Volume : 1  |  Issue : 6  |  Page : 201-207

Assessment of microvascular patterns and density in glioblastoma and their correlation with matrix metalloproteinase-9, p53, glial fibrillary acidic protein, and Ki-67


1 Department of Pathology, Institute of Human Behaviour and Allied Sciences, Delhi, India
2 Department of Biostatistics and Medical Informatics, University College of Medical Sciences, Delhi, India

Date of Web Publication27-Dec-2018

Correspondence Address:
Dr. Sujata Chaturvedi
Department of Pathology, Institute of Human Behaviour and Allied Sciences, Dilshad Garden, Delhi - 110 095
India
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/glioma.glioma_31_18

Rights and Permissions
  Abstract 

Background and Aim: Microvascular patterns (MVPs) and microvessel density (MVD) can influence the progression of glioblastomas. This study aims to study MVP and MVD using immunohistochemistry, and examine any correlation with the expression of matrix metalloproteinase-9 (MMP-9), p53, glial fibrillary acidic protein (GFAP), and Ki-67 labeling index (Ki-67 LI) in 24 cases of glioblastoma multiforme. Materials and Methods: MVPs and MVD were studied by a dual staining method using periodic acid–Schiff stain with CD34 (MVDCD34), CD31 (MVDCD31), von Willebrand factor (MVDvWF), and factor VIII (MVDFVIII). The expression of MMP-9, p53, GFAP, and Ki-67 LI was analyzed using immunohistochemistry. The Pearson coefficient of correlation and intraclass correlation were obtained using SPSS software. Results: Five distinct categories of MVP were found: Microvascular sprouting (MS)/simple vessels, vascular clusters (VCs), vascular garlands, glomeruloid tufts, and vasculogenic mimicry. Of the MVPs, MS was the most common pattern and was present in all cases. On calculating the Pearson's correlation coefficient, different MVPs gave varying results regarding their correlation with MMP-9, p53, GFAP, and Ki-67 LI. MSCD34, CD31, vWF showed significant correlation with MMP-9 and Ki-67 LI, while MSFVIII did not show any correlation with Ki-67 LI. Only VCCD34 had a correlation with Ki-67 LI. No correlation between any of the MVPs and GFAP and p53 was appreciated. MVD ranged from: CD34 (9.2–41.9/hpf), FVIII (6.05–40.5/hpf), CD31 (5.1–40.7/hpf), and vWF (8.7–35.5/hpf). MVDCD34 and MVDCD31 correlated with MMP-9 and Ki-67, whereas, MVDvWF and MVD FVIII correlated with MMP-9. Interobserver agreement was seen only in the assessment of MVD and the MS type of MVP. Conclusion: MVD and MVPs had correlation with MMP-9, p53, GFAP, and Ki-67. These results could impact the development of strategies using antiangiogenic therapies.

Keywords: Glioblastoma, microvascular patterns, microvessel density


How to cite this article:
Jha K, Pant I, Singh R, Bansal AK, Chaturvedi S. Assessment of microvascular patterns and density in glioblastoma and their correlation with matrix metalloproteinase-9, p53, glial fibrillary acidic protein, and Ki-67. Glioma 2018;1:201-7

How to cite this URL:
Jha K, Pant I, Singh R, Bansal AK, Chaturvedi S. Assessment of microvascular patterns and density in glioblastoma and their correlation with matrix metalloproteinase-9, p53, glial fibrillary acidic protein, and Ki-67. Glioma [serial online] 2018 [cited 2022 Nov 27];1:201-7. Available from: http://www.jglioma.com/text.asp?2018/1/6/201/248706


  Introduction Top


Angiogenesis plays a key role in disease progression in infiltrating astrocytic tumors.[1] A host of factors contribute to the process of angiogenesis including angiogenic factors and their receptors, adhesion molecules, β-catenin, regulators of extracellular matrix remodeling, and genetic alterations.[2] The degree of angiogenesis impacts both tumor biology and the aggressiveness of the tumor. Therefore, the quantitative assessment of angiogenesis in terms of microvessel density (MVD), has been studied in a variety of malignant tumors.[3],[4],[5] Its prognostic significance has been shown in carcinomas of the breast. In many other tumors, its role as a prognostic and predictive factor is yet to be established.[6] Thus, we attempted to study the quantitative and qualitative aspects of angiogenesis in terms of MVD and microvascular patterns (MVPs) in glioblastoma. We attempted to correlate MVD and MVP with the expression of markers of tumorigenicity (p53), tumor spread (MMP-9), glial differentiation, glial fibrillary acidic protein [GFAP], and the proliferation marker (Ki-67 labeling index [Ki-67 LI]).


  Materials and Methods Top


Clinical details

This is a retrospective study of 24 histologically diagnosed cases of glioblastoma over a period of 1 year. Paraffin-embedded blocks with tumor samples were retrieved from the archives for further immunohistochemical evaluation. From the clinical records, gender, age, tumor site, and size were recorded [Table 1]. In these cases, glioblastoma had been diagnosed on the basis of the histopathological criteria of increased cellularity, distinct nuclear atypia, mitotic activity, endothelial proliferation, and necrosis.
Table 1: Demographic details of the patients

Click here to view


Immunohistochemistry for microvessel density and microvascular patterns

Dual staining (periodic acid-Schiff with CD34, CD31, factor VIII, and vWF)

Sections were cut at a thickness of 4 μm from routinely prepared, formalin-fixed, and paraffin-embedded blocks. Dual staining was done according to the method described by Yue and Chen.[7] For CD34 staining, we used anti-CD34 mouse monoclonal antibody (Biogenex, clone QBEND/10, ready-to-use), anti-CD31 rabbit monoclonal antibody (Biocare, clone BC2, ready-to-use), anti-vWF rabbit polyclonal anti-human antibody (Dako, clone N/A, ready-to-use), and anti-factor VIII rabbit polyclonal antibody (Biocare, clone N/A, ready-to-use). Microvessels from nonneoplasic brain tissue adjacent to tumor served as internal morphological controls [Figure 1]A. Primary antibodies were omitted for the negative controls. Two observers (KJ and RS) made independent MVD and MVP observations on a twin header microscope to ensure similarity of fields observed.
Figure 1: (A) Cortical tissue as internal positive control for vessels (CD34 + PAS, ×100); (B) microvascular sprouting (CD34 + PAS, ×400); (C) vascular clusters (CD34 + PAS, ×400); (D) vascular garlands (CD34 + PAS, ×400); (E) glomeruloid tufts (CD34 + PAS, ×400); (F) vasculogenic mimicry (CD34 + PAS, ×400)

Click here to view


Assessment of microvascular patterns

We have followed the criteria suggested by Chen et al.[8] Five different categories of MVP were defined: microvascular sprouts/simple vessels (MS), vascular clusters (VC), vascular garlands (VG), glomeruloid tufts (GT), and vasculogenic mimicry (VM). For vessels marked by antiendothelial cell (EC) antibodies, four types of formations were defined as follows: (1) microvascular sprouting (MS)/simple vessels, defined as delicate capillary-like vessels with a lumen, (2) vascular cluster (VC), defined as distinct focal aggregations of vessels without connective stroma, (3) vascular garland (VG), defined as clustered vessels arranged in garland like formation, with or without connective tissue stroma, and (4) GT, defined as clustered vessels embedded by connective tissue stroma with a glomerulus-like appearance (containing 15–100 nuclei). A stained lumen or a separate cell stained positive for the endothelial markers was regarded as a single countable microvessel. The vessels with muscular walls were not included in the study. The fifth type of pattern was VM, defined as vascular channels lined by cells that stained negative for the endothelial markers, but showed periodic acid–Schiff (PAS) positivity.

Microvessel density estimation

MVD measurement was performed after staining the sections with the four endothelial markers. Briefly, the stained sections were screened at ×100 magnification under a light microscope to identify the ten regions of the section with the highest number of microvessels. Microvessels were then counted in the selected areas at ×400 magnification (high-power field, hpf), and the average was counted. For detection of microvessels, a microvessel was defined as any brown, immunostained EC separated from adjacent microvessels, tumor cells, and other connective tissue elements. The MVD count was further grouped into three classes: low (<10/hpf), intermediate (10–20/hpf), and high (>20/hpf). The average MVD was then correlated with the immunohistochemical expression of MMP-9, p53, GFAP, and Ki-67 LI.

Immunohistochemical analyses for matrix metalloproteinase-9, p53, glial fibrillary acidic protein, and Ki-67 labeling index

Serial sections from matched specimens were selected for immunohistochemistry. The following antibodies were used with standard protocols: anti-MMP-9 rabbit monoclonal antibody (Biogenex, clone EP1255Y, ready-to-use), anti-p53 mouse monoclonal antibody (Biogenex, clone BP53-12, ready-to-use), anti-GFAP mouse monoclonal antibody (Biogenex, clone GA-5, ready-to-use), and anti-Ki-67 rabbit monoclonal antibody (Biocare, clone EPR3611, ready-to-use). As positive controls, previously tested known positive cases were taken. Negative controls were generated by omission of the primary antibody.[9] The expression of molecular markers was recorded using Allred score.[10] A score of 0–2 was regarded as negative, while a score of 3–8 was positive. For Ki-67 LI, the number of cells showing nuclear positivity was counted out of a total of 1000 tumor cells. Immunohistochemical analysis of MMP-9, p53, GFAP, and Ki-67 LI was performed.

Statistical analysis

Statistical analyses were performed using SPSS software, version 20.0 (SPSS, Inc., Chicago, IL, USA). P < 0.05 was defined as statistically significant. Associations between MVPs, MVD, and MMP-9, p53, GFAP, and Ki-67 LI were determined by the Pearson's correlation coefficient. Interobserver agreement in the assessment of MVD, and MVPs was assessed by intraclass correlation coefficient (ICC).


  Results Top


Clinical characteristics

The age of the patients ranged from 15 to 77 years. Of them, 16 were male and 8 were female. The size of the tumor received ranged from 2.5 to 8.5 cm. Different locations of the tumor are detailed in [Table 1].

Classification of microvascular patterns

All 24 cases were heterogeneous in the distribution and proportion of the five MVPs. The most prominent pattern detected was MS/simple vessels, present in all cases. The other patterns in decreasing order of frequency were: (1) VC, (2) VG, (3) GT, and (4) VM [Table 2] and [Figure 1], [Figure 2], [Figure 3].
Table 2: Classification of microvascular patterns by different markers

Click here to view
Figure 2: (A) Microvascular sprouting (FVIII + PAS, ×400); (B) vascular clusters (FVIII + PAS, ×400); (C) vascular clusters, four vessels are seen separated by connective tissue pillar (FVIII + PAS, ×400); (D) Vascular garlands (FVIII + PAS, ×400); (E) glomeruloid tufts (FVIII + PAS, ×400); (F) vasculogenic mimicry (FVIII + PAS, ×400)

Click here to view
Figure 3: (A) Microvascular sprouting (CD31 + PAS, ×400); (B) vascular clusters (CD31 + PAS, ×400); (C) vascular garlands (CD31 + PAS, ×400); (D) glomeruloid tufts (CD31 + PAS, ×400); (E) vasculogenic mimicry (CD31 + PAS, ×400);(F) microvascular sprouting (vWF + PAS, ×400); (G) vascular clusters (vWF + PAS, ×400); (H) vascular garlands (vWF + PAS, ×400); (I) glomeruloid tufts (vWF + PAS, ×400); (J) vasculogenic mimicry (vWF + PAS, ×400)

Click here to view


Estimation of microvessel density by different markers

The MVD range (per hpf) given by CD34, FVIII, CD31, and vWF were 9.2–41.9, 5.1–40.7, 8.7–35.5, and 6.05–40.5, respectively. Majority of the cases fell into the high MVD count category [Table 3].
Table 3: Estimation of microvascular density by different markers

Click here to view


Correlation between microvessel density and microvascular patterns with matrix metalloproteinase-9, p53, glial fibrillary acidic protein, and Ki-67 labeling index

MMP-9 and GFAP immunopositivity was observed in the cytoplasmic and fibrillary network. Ki-67 and p53 expression was nuclear [Figure 4]. In our study, whereas, MVDCD34 and MVDCD31 showed statistically significant association with MMP-9 and Ki-67 LI, MVDvWF and MVDFVIII showed association with only MMP-9. Among the various MVPs studied with different markers, MSCD34, CD31, vWF also showed a similar association with MMP-9 and Ki-67 LI, except MSFVIII, which did not show any association with Ki-67 LI. The rest of the patterns failed to show any association except for VCCD34 showing association with Ki-67 LI. No association was found between any of the vascular parameters and GFAP or p53 [Table 4].
Figure 4: (A) Strong Matrix metalloproteinase-9 expression in tumor cells, ×200; (B) nuclear p53 expression in tumor cells, ×400; (C) glial fibrillary acidic protein positivity in tumor, ×200; (D) Ki-67 expression in areas of highest proliferation, ×200

Click here to view
Table 4: Pearson's correlation between microvascular density and microvascular patterns with matrix metalloproteinase-9, p53, glial fibrillary acidic protein, and Ki-67 labeling index; significance, P, two-tailed

Click here to view


Comparison of different endothelial markers

The highest number of MVPs and MVD were detected by CD34. The rest of the endothelial markers gave comparable results [Table 3] and [Table 4].

Interobserver agreement

The interobserver agreement was found in the counting of MVD by CD34 (ICC = 0.784, P < 0.01), factor VIII (ICC = 0.989, P < 0.01), and CD31 (ICC = 0.993, P < 0.01). Among the various MVPs, there was agreement in the assessment of MS by all the markers. Rest of the patterns showed variable results [Table 5].
Table 5: Assessment of interobserver agreement

Click here to view



  Discussion Top


Angiogenesis is important in the pathogenesis of a variety of nonpathologic and pathologic conditions including malignancies.[11] Marked heterogeneity is seen in the organization, structure, function, gene expression, and antigen composition of tumor vessels. This is quite different from normal vasculature. It was Judah Folkman who first proposed in 1971, that tumor growth and metastasis are dependent on angiogenesis, and blocking angiogenesis could be the key to block the progression of the disease.[12] Although there are several studies on MVD in glioblastoma,[9],[13],[14],[15] very few articles mention the qualitative aspects of MVPs.[8],[16]

Glioblastoma is a highly vascularized tumor, and microvascular proliferation forms an integral part in the progression of low-grade glioma to glioblastoma.[14] The vascular networks show heterogeneity in morphology, as well as, in the mechanisms leading to their formation and development.[17] The evidence of angiogenesis playing a critical role in the biological behavior of these tumors and patients' prognosis has necessitated studies on the basic mechanisms of vascularization.[18] For almost 30 years, sprouting angiogenesis was considered the exclusive mechanism of vascularization in tumors. However, several additional mechanisms such as intussusceptive angiogenesis, vessel co-option, glomeruloid angiogenesis, recruitment of bone marrow-derived endothelial progenitor cells, and VM have been identified.[19],[20],[21],[22]

Growing tumors have been found to increase their blood supply and meet increased oxygen and nutritional demands by the following mechanisms: formation of new capillary buds from preexisting vessels (sprouting);[20] lining along the preexisting blood vessels (vessel co-option);[21] migration of angioblasts/endothelial progenitor cells from bone marrow under the influence of growth factors, (also known as vasculogenesis);[20],[23] splitting of preexisting vessel with the formation of connective columns (tissue pillars) in the lumen of the vessel (intussusceptive microvascular growth [IMG]);[24] remodeling of existing vasculature to form complex glomeruloid bodies (glomeruloid angiogenesis); and finally,[22] the tumor cells themselves dedifferentiating and acquiring an endothelial phenotype forming PAS-positive vascular patterns (VM).[25]

IMG has been found to be a faster, more economical way of increasing the complexity and density of the tumor microvessel network, independent of EC proliferation.[24] It has been found that this process sets into play once the sprouting of blood vessels has begun. It has been suggested that the absence of EC proliferation in IMG implies that neovascularization by this mechanism would be resistant to angiosuppressive treatment.[22]

Maniotis et al.[26] first described VM in 1999 in uveal melanoma. It has since been observed in a variety of tumors such as carcinoma of breast, prostate, ovary, lung, synovial sarcoma, rhabdomyosarcoma, pheochromocytoma, and glioblastomas.[7] The discovery of VM has challenged the hypothesis that angiogenesis is the only means by which tumor acquires a blood supply. According to Folberg et al.,[27] the mere detection of VM anywhere in the tumor sections has a prognostic significance. In a recent study by Wang et al.,[28] the presence of VM has been proposed as an adverse prognostic factor for postoperative survival in newly diagnosed cases of glioblastoma.

The studies have found the presence of “classic” (capillary-like) and “bizarre” (GT, VCs, and vascular garlanding), vascular patterns along with VM in glioblastoma.[8],[9],[16] In consonance with these studies, our study also confirms the presence of these patterns. Our findings suggest that MS and simple vessels form the predominant pattern in these tumors. The 24 cases in our study had a variable composition of the bizarre patterns. Chen et al.[8] in their study found that the cases with fewer microvascular sprouts and VCs had a significantly higher number of GTs and VM. However, we found that the bizarre vascular patterns comprised only up to 10% of the total MVP found in most of the cases. VM was found in approximately 50% of our cases, in contrast to 38.1% in another study by Liu et al.[25] and 28.75% in the study by Wang et al.[28] In the study by Liu et al.,[25] MVD was lower in VM positive cases. No such observation was seen in our study. In concordance with the study by Wang et al.,[28] we found a few vessels lying closely approximated together, separated either by a common connective tissue wall or juxtaposed together [Figure 2]C and [Figure 3]B, pointing toward intussusception being the likely underlying mechanism.

These findings led us to speculate whether vascular parameters have significance apart from tumor grade. Several trials have evaluated the impact of antiangiogenic treatment on the overall survival of glioblastoma patients, but with inconsistent results.[29] Alternate mechanisms of vascularization operating during tumor proliferation and growth may be responsible for the failure of antiangiogenic therapy. Due to the existence of multiple vascularization mechanisms and angiogenic signaling pathways, the inhibition of a single signaling pathway likely triggers alternative vascularization mechanisms.[22] A review of the literature shows that some key genes have been studied in the molecular pathways involved in the development and proliferation of vascular networks in glioblastoma.[20] Identifying the key molecular pathways in the above-mentioned vascularization mechanisms will be a first step in providing a more targeted antivascular therapy in glioblastoma.

Matrix metalloproteinases (MMPs) have been recognized as modulators of tumor microenvironment.[30] The formation of new blood capillaries is dependent on the extracellular matrix, which serves as structural support for existing and developing vessels and matrix metalloproteinases such as MMP-2 and 9.[20] MMPs are known to affect vascular density and regulate vessel branching in glioblastoma. These zinc-dependent endopeptidases have been implicated in modulating the vascular functionality by regulating pericyte activation and recruitment.[30],[31] In the present study, a statistically significant association was found between MMP-9 and MVD. A significant relationship was seen in the expression of MMP-9 with microvascular sprouts and simple vessels. However, the complex patterns failed to show an association with MMP-9 expression. In the study done by Zhang et al.,[32] inhibition of MMP-2 and MMP-9 expression led to suppressed VM channel and mosaic vessel formations in melanoma. No association was seen between VM formation and MMP-9 expression in our study.

Ki-67 LI is a proliferation marker that denotes higher grade and aggressiveness of malignant gliomas.[33] TP53 mutations bear a favorable prognostic impact irrespective of whether the glioblastoma is primary or secondary.[34] Chen et al.[8] observed a potential relationship between expression of Ki-67 and p53 with microvascular heterogeneity. Correlation between Ki-67 LI and MVD has been studied in other tumors with variable results.[35],[36],[37] In our study, the Ki-67 index showed a similar relationship as MMP-9 with MVD and MS, and p53 expression was not associated with the vascular parameters studied. We also studied the expression of GFAP in the glioblastoma cells. It is a well-known fact that the malignant astrocytomas are often GFAP negative and seem to lose GFAP expression with the progression of the grade. It has also been found that GFAP-negative tumor cells proliferate more rapidly as compared to the GFAP-positive tumor cells. A strong negative correlation between loss of GFAP expression and dedifferentiation of malignant tumor cells has also been documented by several in vitro studies.[38] Vascularization and loss of GFAP expression are seen with the progression of the malignant phenotype of the tumor cells. In the current study, no association was seen in the vascular parameters and GFAP expression.

Often, when MVD and MVPs are analyzed manually by multiple observers, interobserver variability yields variable results.[9] The selection of vascularized fields for quantification likely plays an important role in this variability. The “hot spot” method yields better results as compared to the random selection of vascular areas within the tumor.[9] Preusser et al.[9] state that highest agreement is achieved when different observers counted MVD in the same area. However, observer agreement on vascular patterns was poor in their study. In our study, there was better agreement in counting MVD and microvascular sprouts (statistically significant). There was no agreement in the assignment of VC, VG, GT, and VM in the present study. One of the possible reasons for the absence of such agreement is the presence of many intermediate formations in vascular patterns as has been mentioned in other studies.[8]

Researchers have been somewhat divided on their views about the selection of the most suitable endothelial marker.[6],[16] We used four endothelial markers to study MVD and MVPs. In our hands, we found CD34 to be the best endothelial marker. Using CD34, we found the vessels were better highlighted and had finer detail.

This preliminary study revealed varying MVD and MVPs assessed by different immunohistochemical markers; their correlation with MMP-9, p53, GFAP, and Ki-67; and inter-observer agreement pattern in the assessment of MVD and MVPs. As antiangiogenic therapeutic strategies are being developed for glioblastoma, further studies that standardize the vascular parameters such as MVD and MVPs are needed. Our study was limited by a small sample size and the lack of follow-up data.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.

 
  References Top

1.
Vokuda RS, Srinivas BH, Madhugiri VS, Verma SK. Vascular endothelial growth factor as an angiogenic marker in malignant astrocytoma and oligodendroglioma: An Indian scenario. J Clin Diagn Res 2017;11:EC05-7.  Back to cited text no. 1
    
2.
Schiffer D. Brain Tumor Pathology: Current Diagnostic Hotspots and Pitfalls. Netherlands: Springer; 2006. p. 189-98.  Back to cited text no. 2
    
3.
Kather JN, Marx A, Reyes-Aldasoro CC, Schad LR, Zöllner FG, Weis CA. Continuous representation of tumor microvessel density and detection of angiogenic hotspots in histological whole-slide images. Oncotarget 2015;6:19163-76.  Back to cited text no. 3
    
4.
Tzoutzos K, Batistatou A, Kitsos G, Liasko R, Stefanou D. Study of microvascular density and expression of vascular endothelial growth factor and its receptors in cancerous and precancerous lesions of the eyelids. Anticancer Res 2014;34:4977-83.  Back to cited text no. 4
    
5.
Hasan J, Byers R, Jayson GC. Intra-tumoural microvessel density in human solid tumours. Br J Cancer 2002;86:1566-77.  Back to cited text no. 5
    
6.
Białas M, Okoń K, Czopek J. Assessing microvessel density in gastric carcinoma: A comparison of three markers. Pol J Pathol 2003;54:249-52.  Back to cited text no. 6
    
7.
Yue WY, Chen ZP. Does vasculogenic mimicry exist in astrocytoma? J Histochem Cytochem 2005;53:997-1002.  Back to cited text no. 7
    
8.
Chen L, Lin ZX, Lin GS, Zhou CF, Chen YP, Wang XF, et al. Classification of microvascular patterns via cluster analysis reveals their prognostic significance in glioblastoma. Hum Pathol 2015;46:120-8.  Back to cited text no. 8
    
9.
Preusser M, Heinzl H, Gelpi E, Schonegger K, Haberler C, Birner P, et al. Histopathologic assessment of hot-spot microvessel density and vascular patterns in glioblastoma: Poor observer agreement limits clinical utility as prognostic factors: A translational research project of the European Organization for Research and Treatment of Cancer Brain Tumor Group. Cancer 2006;107:162-70.  Back to cited text no. 9
    
10.
Pleşan DM, Georgescu CV, Ciobotea S, Pătrană N, Mitroi L, Pleşan C. Immunohistochemical evaluation of hormone receptors with predictive value in mammary carcinomas. Curr Health Sci J 2009;35:184-9.  Back to cited text no. 10
    
11.
Carmeliet P, Jain RK. Angiogenesis in cancer and other diseases. Nature 2000;407:249-57.  Back to cited text no. 11
    
12.
Cao Y. Future options of anti-angiogenic cancer therapy. Chin J Cancer 2016;35:21.  Back to cited text no. 12
    
13.
Birlik B, Canda S, Ozer E. Tumour vascularity is of prognostic significance in adult, but not paediatric astrocytomas. Neuropathol Appl Neurobiol 2006;32:532-8.  Back to cited text no. 13
    
14.
Tena-Suck ML, Celis-Lopez MA, Collado-Ortiz MA, Castillejos-Lopez M, Tenorio-Serralta M. Glioblastoma multiforme and angiogenesis: A clinicopathological and immunohistochemistry approach. J Neurol Res 2015;5:199-206.  Back to cited text no. 14
    
15.
Sica G, Lama G, Anile C, Geloso MC, La Torre G, De Bonis P, et al. Assessment of angiogenesis by CD105 and nestin expression in peritumor tissue of glioblastoma. Int J Oncol 2011;38:41-9.  Back to cited text no. 15
    
16.
Sharma S, Sharma MC, Sarkar C. Morphology of angiogenesis in human cancer: A conceptual overview, histoprognostic perspective and significance of neoangiogenesis. Histopathology 2005;46:481-9.  Back to cited text no. 16
    
17.
Das S, Marsden PA. Angiogenesis in glioblastoma. N Engl J Med 2013;369:1561-3.  Back to cited text no. 17
    
18.
Würdinger T, Tannous BA. Glioma angiogenesis: Towards novel RNA therapeutics. Cell Adh Migr 2009;3:230-5.  Back to cited text no. 18
    
19.
Hillen F, Griffioen AW. Tumour vascularization: Sprouting angiogenesis and beyond. Cancer Metastasis Rev 2007;26:489-502.  Back to cited text no. 19
    
20.
Conway EM, Collen D, Carmeliet P. Molecular mechanisms of blood vessel growth. Cardiovasc Res 2001;49:507-21.  Back to cited text no. 20
    
21.
Qian CN, Tan MH, Yang JP, Cao Y. Revisiting tumor angiogenesis: Vessel co-option, vessel remodeling, and cancer cell-derived vasculature formation. Chin J Cancer 2016;35:10.  Back to cited text no. 21
    
22.
Döme B, Hendrix MJ, Paku S, Tóvári J, Tímár J. Alternative vascularization mechanisms in cancer: Pathology and therapeutic implications. Am J Pathol 2007;170:1-5.  Back to cited text no. 22
    
23.
Nolan DJ, Ciarrocchi A, Mellick AS, Jaggi JS, Bambino K, Gupta S, et al. Bone marrow-derived endothelial progenitor cells are a major determinant of nascent tumor neovascularization. Genes Dev 2007;21:1546-58.  Back to cited text no. 23
    
24.
Nico B, Crivellato E, Guidolin D, Annese T, Longo V, Finato N, et al. Intussusceptive microvascular growth in human glioma. Clin Exp Med 2010;10:93-8.  Back to cited text no. 24
    
25.
Liu XM, Zhang QP, Mu YG, Zhang XH, Sai K, Pang JC, et al. Clinical significance of vasculogenic mimicry in human gliomas. J Neurooncol 2011;105:173-9.  Back to cited text no. 25
    
26.
Maniotis AJ, Folberg R, Hess A, Seftor EA, Gardner LM, Pe'er J, et al. Vascular channel formation by human melanoma cells in vivo and in vitro: Vasculogenic mimicry. Am J Pathol 1999;155:739-52.  Back to cited text no. 26
    
27.
Folberg R, Hendrix MJ, Maniotis AJ. Vasculogenic mimicry and tumor angiogenesis. Am J Pathol 2000;156:361-81.  Back to cited text no. 27
    
28.
Wang SY, Ke YQ, Lu GH, Song ZH, Yu L, Xiao S, et al. Vasculogenic mimicry is a prognostic factor for postoperative survival in patients with glioblastoma. J Neurooncol 2013;112:339-45.  Back to cited text no. 28
    
29.
Bergers G, Hanahan D. Modes of resistance to anti-angiogenic therapy. Nat Rev Cancer 2008;8:592-603.  Back to cited text no. 29
    
30.
Du R, Petritsch C, Lu K, Liu P, Haller A, Ganss R, et al. Matrix metalloproteinase-2 regulates vascular patterning and growth affecting tumor cell survival and invasion in GBM. Neuro Oncol 2008;10:254-64.  Back to cited text no. 30
    
31.
Page-McCaw A, Ewald AJ, Werb Z. Matrix metalloproteinases and the regulation of tissue remodelling. Nat Rev Mol Cell Biol 2007;8:221-33.  Back to cited text no. 31
    
32.
Zhang S, Li M, Gu Y, Liu Z, Xu S, Cui Y, et al. Thalidomide influences growth and vasculogenic mimicry channel formation in melanoma. J Exp Clin Cancer Res 2008;27:60.  Back to cited text no. 32
    
33.
Jaiswal S. Role of immunohistochemistry in the diagnosis of central nervous system tumors. Neurol India 2016;64:502-12.  Back to cited text no. 33
[PUBMED]  [Full text]  
34.
Schmidt MC, Antweiler S, Urban N, Mueller W, Kuklik A, Meyer-Puttlitz B, et al. Impact of genotype and morphology on the prognosis of glioblastoma. J Neuropathol Exp Neurol 2002;61:321-8.  Back to cited text no. 34
    
35.
Ammendola M, Sacco R, Marech I, Sammarco G, Zuccalà V, Luposella M, et al. Microvascular density and endothelial area correlate with Ki-67 proliferative index in surgically-treated pancreatic ductal adenocarcinoma patients. Oncol Lett 2015;10:967-71.  Back to cited text no. 35
    
36.
Himani B, Meera S, Abhimanyu S, Usha R. Ki-67 immunostaining and its correlation with microvessel density in patients with mutiple myeloma. Asian Pac J Cancer Prev 2016;17:2559-64.  Back to cited text no. 36
    
37.
Khalili M, Mahdavi N, Beheshti R, Baghai Naini F. Immunohistochemical evaluation of angiogenesis and cell proliferation in tongue squamous cell carcinoma. J Dent (Tehran) 2015;12:846-52.  Back to cited text no. 37
    
38.
Wilhelmsson U, Eliasson C, Bjerkvig R, Pekny M. Loss of GFAP expression in high-grade astrocytomas does not contribute to tumor development or progression. Oncogene 2003;22:3407-11.  Back to cited text no. 38
    


    Figures

  [Figure 1], [Figure 2], [Figure 3], [Figure 4]
 
 
    Tables

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5]



 

Top
 
  Search
 
    Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
    Access Statistics
    Email Alert *
    Add to My List *
* Registration required (free)  

 
  In this article
Abstract
Introduction
Materials and Me...
Results
Discussion
References
Article Figures
Article Tables

 Article Access Statistics
    Viewed3748    
    Printed243    
    Emailed0    
    PDF Downloaded281    
    Comments [Add]    

Recommend this journal