A novel definition of microvessel density in renal cell carcinoma: Angiogenesis plus vasculogenic mimicry
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- Published online on: September 3, 2020 https://doi.org/10.3892/ol.2020.12054
- Article Number: 192
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Copyright: © Wu et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
Abstract
Introduction
Angiogenesis, which is the development of new blood vessels from existing vasculature, is a major driving force in numerous types of malignancy by delivering oxygen and nutrients for the growth of tumors (1), while facilitating fast metastasis (2). First introduced by Folkman as a potential target for cancer treatment (3), angiogenesis was thereafter considered an essential pathologic feature and sustaining element of cancer, which has a key role in tumor dissemination/metastasis (4). Therefore, it appears reasonable to predict that the extent of tumor vascularity, measured by the pathological microvessel density (MVD), may be closely associated with the aggressiveness of a tumor (5), including its invasive and metastatic potential. MVD is usually defined by the following equation:
MVD (hotspot)=Individual microvessels (number)/area
The endothelial cell or endothelial cell cluster that was clearly separated from adjacent microvessels, tumor cells and other connective tissue elements was considered a single, countable microvessel (6). An inverse association between MVD and patient survival has been reported for several malignancies, including breast cancer (7) and melanoma (8), as well as prostate (9) and bladder (10) cancer. Previous studies have indicated that the MVD was correlated with vascular endothelial growth factor (VEGF) expression, which is also a crucial factor in the vascular biology of multiple tumors as a mediator of angiogenesis. In the field of metastatic renal cell carcinoma (RCC), which is a highly vascularized solid tumor type (11), anti-angiogenic agents targeting VEGF/VEGF receptor, such as sunitinib, pazopalib and bevacizumab, have been the standard first-line therapy for years; however, they provide a limited benefit and metastatic RCC remains a challenge (12), which suggests that there may be an alternative blood supply besides angiogenesis. Of note, intra-tumoral MVD has been a controversial prognostic predictor for RCC. Nativ et al (13) and Fukata et al (14) reported that higher MVD is associated with shorter survival in RCC. Similarly, other studies have demonstrated this association in patients with ccRCC (15–17). Some of the studies found other associations. For example, Paradis et al (18) and Zhang et al (19) reported a positive association between MVD and VEGF expression levels, and Tuna et al (20) reported positive association between MVD and mast cell infiltration. Notably, Slaton et al (21) reported no significant correlation between MVD and VEGF, Mohseni et al (22) reported lack of correlation between MVD and mast cell infiltration, while others reported a lack of correlation between MVD and survival (23–26). On the contrary, numerous studies (27–32) have reported higher MVD associated with longer survival, and Yoshino et al (33) and Sabo et al (34) also reported this association in patients with low-stage RCC. Delanunt et al (35) reported this association in ccRCC, and Sharaml et al (36) reported this tendency yet the P-value was 0.1. Sandlund et al (37) reported this trend in 2006, but one year later they switched the marker from CD105 to CD31 and found the association disappeared (38). As for the association with stage or grade, Köhler et al (39) reported a negative association between MVD and stage, Hemmerlein et al (40) and Baldewijns et al (41) reported a negative association between MVD and Fuhrman grade and Kavantzas et al (42) reported positive association between MVD and grade, while Sharma et al (43) reported no association. Therefore, plethora of literature makes the current understanding of MVD in the setting of RCC controversial (Table I).
Table I.Review of previously published literature on the clinical significance of MVD in patients with RCC. |
Microvessel or microvasculature is defined as ‘the smallest system of blood vessels in a body, including those responsible for microcirculation, that distribute blood within tissues’ (44). Besides angiogenesis, there is an alternative perfusion source termed ‘vasculogenic mimicry’ (VM), also referred to as ‘vascular mimicry’. The initial study and molecular characterization of VM was conducted in melanoma (45). Later, VM was also assessed in breast cancer (46) and hepatic carcinoma (47). Of note, the results of these studies agreed with those of earlier studies suggesting the perfusion of tumors via non-endothelial-lined channels. Since VM may also serve as a supply system of blood including nutrients, the concept of MVD may require to be modified, as the current understanding of the complexity of vasculature, either endothelium- or tumor cell-derived, improves over the years. Therefore, the present study proposed a modified version of MVD, referred to as total MVD (TMVD), which incorporates the number of MVD and the status of VM, and was defined as follows:
TMVD=Individual microvessel (number)/area + VM
In the present study, the capability of MVD, VM and TMVD in predicting prognosis of patients with RCC was evaluated and compared, and a bioinformatics analysis of the possible genes underlying the clinical significance of VM was performed.
Materials and methods
Patients and clinical data
A retrospective study was performed involving 183 patients with histopathologically verified RCC who underwent nephrectomy between January 2006 and December 2016 at Xinhua Hospital Affiliated to Shanghai Jiao Tong University, School of Medicine (Shanghai, China). The cohort had a median age of 59.3±7.0 years (range, 44–73 years) and comprised 104 males and 79 females. The pre-operative radiological evaluation consisted of chest X-ray, abdominal ultrasonography and contrast-enhanced CT. None of the patients received irradiation or chemotherapy prior to surgery. The follow-up comprised of chest X-ray, abdominal ultrasonography or CT scan. The macroscopic and histological features of RCC were assessed, including tumor stage and Fuhrman nuclear grade (26). The tumor stage was defined according to the 2010 TNM classification (48). At presentation, the tumor stage was pT1 in 73, pT2 in 80 and pT3 in 30 cases, and the Fuhrman grade was I in 58, II in 90, III in 29 and IV in 6 umors. The follow-up program included clinical and radiological examinations. The median follow-up time from diagnosis was 53.9±19.0 months (range, 11–94 months) for surviving patients. The survival time was calculated from the date of surgery to the date of death or latest follow-up. The study was approved by the Ethics Committee of Xinhua Hospital (Shanghai, China; approval no. XHEC-D-2016-061). The requirement for informed consent was waived by the Ethics Committee due to the retrospective nature of this study. The overall/disease-free survival time and gene sequencing data of another 537 patients with RCC were retrieved from The Cancer Genome Atlas (TCGA) database (https://cancergenome.nih.gov/), the Kidney RCC cohort (TCGA, provisional) using cBioPortal (https://www.cbioportal.org/). Survival time was evaluated based on individual gene expression levels.
Immunohistochemistry (IHC)
IHC was performed on conventional 5-µm-thick histological paraffin-embedded tissue serial RCC sections on poly-L-lysine-coated glass slides. After heat-drying, the sections were deparaffinized in xylene and sequentially rehydrated in gradients of ethanol, and next incubated overnight at 4°C with anti-CD34 antibody (cat. no. ab81289; 1:100 dilution; Abcam). Signals were amplified with the VECTASTAIN® ABC kit (Vector Laboratories, Inc.). At ×200 magnification, most of the slides had CD34-positive stain and those without any CD34 signal were considered invalid and restained. Periodic acid Schiff (PAS) staining was performed using a PAS kit (Sigma-Aldrich; Merck KGaA) according to the manufacturer's protocol on one of the CD34-stained slides. Sections were counterstained with Mayer's hematoxylin, coverslips were mounted with Permount Mounting Medium and samples were observed using an Olympus IX73 microscope (Olympus, Corp.). For the negative control, the primary antibody was replaced with non-immune human serum (cat. no. 31876; Thermo Fisher Scientific, Inc.).
MVD quantification and VM identification
MVD was assessed according to consensus guidelines (49) independently by two pathologists by counting individual microvessels in 5 fields at a magnification of ×200 in a highly vascular tumor area (hot spot), excluding areas with prominent hyalinization and necrosis. Microvessels were defined as any CD34-positive endothelial or endothelial cell cluster with or without a viable lumen. In tumors exhibiting a dense microvasculature network, each branch was interpreted as a single vessel. Large anastomosing sinusoidal vessels were counted as single vessels. Only vessels distinct from one another were counted separately. Large vessels with thick muscular walls were excluded from counting. For each tumor, the mean number of microvessels counted in five fields at ×200 magnification was considered as the MVD value, which is a number without unit (50). For CD34/PAS dual-stained slides, VM was defined as any CD34-negative/PAS-positive closed area.
Statistical analysis
Values were expressed as the mean ± standard error of the mean, while in figures MVD were shown in box and whisker plots as minimum to maximum using GraphPad Prism 6 (GraphPad Software, Inc.). Statistical analyses involved Student's t-test, one-way analysis of variance with Bonferroni's post hoc test, the χ2 test and the log-rank (Mantel-Cox) test. The analyses were conducted with SPSS 22 (IBM Corp.) or GraphPad Prism 6 (GraphPad Software, Inc.). In the survival analysis, when two Kaplan-Meier curves crossed, Cox time-dependent covariate analysis was used for adjustment of the P-value. P<0.05 was considered to indicate a statistically significant difference.
Results
MVD is not associated with the stage or grade of RCC
IHC staining for CD34 was performed on the RCC samples. By microscopic observation under ×200 magnification, MVD in a hotspot area was able to be classified into low (between 20 and 30; Fig. 1A), moderate (between 40 and 50; Fig. 1B) and high (between 60 and 80; Fig. 1C). The mean MVD was calculated to be 44.9±12.4. Regarding different stages, the mean MVD was 43.5±10.0 for stage 1, 46.3±13.6 for stage 2 and 44.8±14.2 for stage 3 (Fig. 1D). The mean MVD for different grades was 42.6±10.9 for grade 1, 46.2±12.4 for grade 2 and 45.5±14.3 for grades 3/4 (Fig. 1E). There was no significant difference in MVD between the different stages or grades, and no increasing or decreasing tendency was observed either. The results of Fig. 1 suggested a weak association between MVD and the stage/grade.
VM exhibits a tendency to increase in patients with advanced-stage/grade RCC
CD34/PAS dual staining was performed on serial RCC sections in order to identify the VM structure. Based on CD34 expression, the slides were classified into VM(−), which corresponded to a CD34(+)/PAS(+) status (Fig. 2A), and VM(+), which was defined by the presence of a CD34(−)/PAS(+) enclosed channel that was lined by tumor cells rather than endothelial cells (Fig. 2B). Patients were stratified based on their VM(+) or VM(−) status. By further stratifying the patients based on their stage/grade information, it was observed that, although there was a higher proportion of VM(+) patients in stage 3 compared with those in stage 1 (P=0.0292; Fig. 2C), the differences between stage 1 and 2 or stage 2 and 3 were not statistically significant. Similarly, a higher proportion of VM(+) patients was present in the grade 3/4 group than in the grade 1 group (P=0.0325; Fig. 2D). There was no difference in MVD between patients with VM(+) and VM(−) according to Student's t-test (P=0.4785; Fig. 2E). The patients were then stratified into high or low MVD groups and it was observed that there was no difference in the VM(+) ratio between patients with high or low MVD in their tumor according to the c2 test (P=0.2625; Fig. 2F).
Survival analysis of genes closely associated with the formation of VM
To clarify why the phenotype of VM was reported to be closely associated with the survival of patients with RCC (51,52), the present study attempted to identify the potentially associated genes using TCGA database via cBioPortal. Previous studies reported several genes closely associated with the formation of VM, including vascular endothelial (VE)-cadherin (also known as CDH5), vimentin (VIM) and matrix metalloproteinases (MMPs) (53–55). The clinical data from a large sample were retrieved from TCGA database and the survival length of patients with RCC was analyzed based on the expression levels of those VM-associated genes. Among them, certain genes had a significant negative impact on overall/disease-free survival, including nodal growth differentiation factor (NODAL), caspase-3 (CASP3), MMP9 and galectin-3 (GAL3) (Fig. 3A-H, respectively). Of the two genes that are known to be closely linked to VM, high VE-cadherin was unexpectedly associated with a longer overall survival (P=0.018; Fig. 3I), but not disease-free survival (P=0.494; Fig. 3J). VIM, a well-known oncogene (56,57), had a significant negative effect on overall survival (P=0.0092; Fig. 3K) and disease-free survival (P=3.92×10−7; Fig. 3L).
VM rather than MVD is able to distinguish patients with different survival prognoses, while TMVD demonstrates superior discriminating capability
Upon dividing the patients into two groups based on their MVD levels, there was no significant difference between the survival time of patients with high or low MVD (P=0.348; Fig. 4A), although the survival time had a tendency to be shorter in patients with higher MVD. Stratification of the patients based on their VM status indicated that VM(+) patients had a significantly shorter survival time (P=0.0002; Fig. 4B), demonstrating an inverse association between VM and survival. By applying the TMVD concept, those patients were further stratified into four subgroups. Comparison of the survival curves of these four subgroups indicated that this stratification was able to distinguish patients with different survival prognoses (Fig. 4C). Among patients with a lower MVD, VM(−) patients exhibited significant longer survival than VM(+) patients (P=0.0076); and among patients with a higher MVD, VM(−) patients also had a significantly longer survival time than VM(+) patients (P=0.0093). Of note, patients with a lower MVD combined with a VM(+) status had an even poorer prognosis than those with a higher MVD combined with a VM(−) status (P=0.039).
Discussion
MVD assessment is the most commonly used technique to quantify intratumoral angiogenesis in cancer. It was first developed by Weidner et al (58) in 1991, who used panendothelial IHC staining of blood microvessels. The first step was the identification of the area with the highest neovessel density (the so-called ‘hot spot’). Individual microvessels were then counted at higher power (magnification, ×200) in an adequate area (e.g., 0.74 mm2 per field using a 20× objective lens and a 10× ocular lens). Any stained endothelial cells or clusters separated from adjacent vessels were counted as single microvessels. Despite numerous reports of the clinical prognostic significance of MVD in various types of tumor, its predictive value regarding outcomes in RCC remains controversial, as summarized in Table I. Some of them reported negative correlation between MVD and prognosis (higher MVD correlated with shorter survival) (13–17), some reported positive correlation (27–32) and others reported no significance (21,23–26,38). This may be associated with several non-mechanistic factors, including sample size, sampling bias, different blood vessel markers (such as the more commonly used CD34 or CD31, or the less frequently used FVIII Rag or CD105), the quality of IHC staining, the methods of vasculature quantification and the methods of interpretation. For instance, Sandlund et al (59) reported in 2006 that a higher MVD was associated with longer survival; however, when CD31 was used as the vessel marker instead of CD105, no association with survival was observed (60). Due to the heterogeneity in methodology among these studies, a forest plot may be unpractical and unreasonable. Another possible reason is the different categories of blood vessels. Yao et al (61) proposed that, within clear-cell RCC, there are at least two major categories of blood vessels with contrasting prognostic implications, namely undifferentiated vessels (expressing CD31 but not CD34) and differentiated vessels (expressing both CD31 and CD34), with a higher undifferentiated vessel density indicating poorer prognosis and higher differentiated vessel density correlating with better prognosis. Qian et al (62) also discussed the complexity of tumor vasculature in RCC and recent studies on the concept of vessel co-option (a non-angiogenic process through which tumor cells utilize pre-existing tissue blood vessels to support tumor growth, survival and metastasis) have been published (63–65), thus obscuring whether MVD is a sufficient prognostic factor.
VM is the formation of fluid-conducting channels by highly invasive and genetically dysregulated tumor cells and acts as a complementary source of blood supply. In the present study, TMVD (i.e., MVD plus VM status) demonstrated a better prognosis-predicting capability compared with that of the MVD or VM alone (Fig. 4C), which may be explained by the fact that endothelium-lined blood vessels as well as VM are able to transfer blood, nutrients and oxygen, and theoretically, both may facilitate cancer progression. It is reasonable to assume that during treatment with an anti-angiogenic regimen, when neo-angiogenesis is suppressed, tumor growth may be more dependent on the supply from VM. A comprehensive meta-analysis review by Yang et al (66) revealed that VM is associated with unfavorable prognosis in >10 different types of tumor, and with cancer differentiation, lymph node metastasis and distant metastasis. In other words, VM is not only functional as a delivering channel, but is in itself is a hallmark of potent proliferation and metastasizing capability. Survival analysis of VM-associated genes, including NODAL, CASP3, MMP9 and GAL3, revealed that these genes had a negative impact on overall and disease-free survival in the setting of RCC based on TCGA database. In addition, several studies have been published demonstrating that the above genes also contribute to angiogenesis (67–70). The single most important factor in VM, VE-cadherin, has been indicated to regulate angiogenesis (71) and the single most important factor in angiogenesis, VEGF, has also been reported to promote VM (72). Taken together, angiogenesis and VM may promote tumor progression independently and probably interdependently (Fig. 4D and E). One of the limitations of the present study is that the association between the above-mentioned genes, VM formation and patient survival was not assessed in the present cohort, and therefore, it was not possible to experimentally clarify certain paradoxical results of the bioinformatics analysis, including higher VE-cadherin being associated with longer overall survival.
When the concept of TMVD was proposed, it was expected to be the sum of MVD and VM density, but in reality, the quantification of VM density, if it is able to be quantitated, is rather difficult. The identification process relies greatly on visual observation. If red blood cells (RBCs) are present inside a CD34(−)/PAS(+) area, it is easier to confirm, while the absence of RBCs inside such an area complicates the identification, since PAS staining may not be well demarked. Instead of calculating its density, the status of VM (positive or negative) was incorporated into the formula of TMVD in the present study. Generally speaking, among the four groups classified according to TMVD, the prognosis of patients with low MVD(≤45)/VM(+) was the best, that of patients with high MVD(>45)/VM(−) and low MVD(≤45)/VM(+) was intermediate and that of patients with high MVD(>45)/VM(+) was the worst. The clinical significance and cost-effectiveness of this novel concept of TMVD require to be further investigated, not only in the setting of RCC, but also in other cancer types in which VM may have a critical role. Recently, novel combinational therapy targeting other molecules, including programmed cell death 1 (PD1)/programmed cell death 1 ligand 1 (PDL1) and cytotoxic T-lymphocyte-associated protein 4 (CTLA-4), has demonstrated promising efficiency (73–75). With more clinical trials ongoing, it is possible that checkpoint immunotherapy combined with anti-angiogenesis therapy may be adopted as the first-line treatment for metastatic RCC, and PD1/PDL1/CTLA-4 expression levels, and perhaps other gene expression levels (76–79), combined with TMVD may provide higher accuracy in predicting patient prognosis.
In conclusion, the present study examined the novel concept of TMVD, which is a combination of MVD and VM status, and evaluated its capability in predicting prognosis in patients with RCC compared to that of MVD or VM alone. TMVD demonstrated superior predictive capability, and together with the results of the TCGA data analysis, the present results suggested that angiogenesis and VM promote tumor progression independently and probably interdependently.
Acknowledgements
Not applicable.
Funding
This work was supported by the National Natural Science Foundation (grant nos. 81970657 and 81802522) and the Shanghai Sailing Program (grant no. 18YF1415200).
Availability of data and materials
The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.
Authors' contributions
JQ, ZG, JY and JD designed the study. JY and JD supervised the whole process. YW, KD, WG, DW, HT and NW performed the research, among which WG and JY conducted the IHC staining. YW and KD analyzed the data. YW and JD wrote the manuscript. ZG and JD revised the statistics and the manuscript. All authors read and approved the final manuscript.
Ethics approval and consent to participate
The study was approved by the Ethics Committee of Xinhua Hospital (Shanghai, China; approval no. XHEC-D-2016-061). The requirement of informed consent was waived by the Ethics Committee due to the retrospective nature of the study.
Patient consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
References
Folkman J: Angiogenesis in cancer, vascular, rheumatoid and other disease. Nat Med. 1:27–31. 1995. View Article : Google Scholar : PubMed/NCBI | |
Folkman J: Role of angiogenesis in tumor growth and metastasis. Semin Oncol. 29 (6 Suppl 16):S15–S18. 2002. View Article : Google Scholar | |
Folkman J: Anti-angiogenesis: New concept for therapy of solid tumors. Ann Surg. 175:409–416. 1972. View Article : Google Scholar : PubMed/NCBI | |
Carmeliet P and Jain RK: Angiogenesis in cancer and other diseases. Nature. 407:249–257. 2000. View Article : Google Scholar : PubMed/NCBI | |
Hlatky L, Hahnfeldt P and Folkman J: Clinical application of antiangiogenic therapy: Microvessel density, what it does and doesn't tell us. J Natl Cancer Inst. 94:883–893. 2002. View Article : Google Scholar : PubMed/NCBI | |
Tae K, El-Naggar AK, Yoo E, Feng L, Lee JJ, Hong WK, Hittelman WN and Shin DM: Expression of vascular endothelial growth factor and microvessel density in head and neck tumorigenesis. Clin Cancer Res. 6:2821–2828. 2000.PubMed/NCBI | |
Zhou D, Cheng SQ, Ji HF, Wang JS, Xu HT, Zhang GQ and Pang D: Evaluation of protein pigment epithelium-derived factor (PEDF) and microvessel density (MVD) as prognostic indicators in breast cancer. J Cancer Res Clin Oncol. 136:1719–1727. 2010. View Article : Google Scholar : PubMed/NCBI | |
Pastushenko I, Vermeulen PB, Carapeto FJ, Van den Eynden G, Rutten A, Ara M, Dirix LY and Van Laere S: Blood microvessel density, lymphatic microvessel density and lymphatic invasion in predicting melanoma metastases: Systematic review and meta-analysis. Br J Dermatol. 170:66–77. 2014. View Article : Google Scholar : PubMed/NCBI | |
Miyata Y and Sakai H: Reconsideration of the clinical and histopathological significance of angiogenesis in prostate cancer: Usefulness and limitations of microvessel density measurement. Int J Urol. 22:806–815. 2015. View Article : Google Scholar : PubMed/NCBI | |
Huang J, Ma X, Chen X, Liu X, Zhang B, Minmin L, Nie W, Zhang L and Liu L: Microvessel density as a prognostic factor in bladder cancer: A systematic review of literature and meta-analysis. Cancer Biomark. 14:505–514. 2014. View Article : Google Scholar : PubMed/NCBI | |
Aziz SA, Sznol J, Adeniran A, Colberg JW, Camp RL and Kluger HM: Vascularity of primary and metastatic renal cell carcinoma specimens. J Transl Med. 11:152013. View Article : Google Scholar : PubMed/NCBI | |
Derosa L, Bayar MA, Albiges L, Le Teuff G and Escudier B: A new prognostic model for survival in second line for metastatic renal cell carcinoma: Development and external validation. Angiogenesis. 22:383–395. 2019. View Article : Google Scholar : PubMed/NCBI | |
Nativ O, Sabo E, Reiss A, Wald M, Madjar S and Moskovitz B: Clinical significance of tumor angiogenesis in patients with localized renal cell carcinoma. Urology. 51:693–696. 1998. View Article : Google Scholar : PubMed/NCBI | |
Fukata S, Inoue K, Kamada M, Kawada C, Furihata M, Ohtsuki Y and Shuin T: Levels of angiogenesis and expression of angiogenesis-related genes are prognostic for organ-specific metastasis of renal cell carcinoma. Cancer. 103:931–942. 2005. View Article : Google Scholar : PubMed/NCBI | |
Joo H, Oh D, Kim Y, Lee K and Kim S: Increased expression of caveolin-1 and microvessel density correlates with metastasis and poor prognosis in clear cell renal cell carcinoma. BJU Int. 93:291–296. 2004. View Article : Google Scholar : PubMed/NCBI | |
Minardi D, Lucarini G, Filosa A, Milanese G, Zizzi A, Di Primio R, Montironi R and Muzzonigro G: Prognostic role of tumor necrosis, microvessel density, vascular endothelial growth factor and hypoxia inducible factor-1alpha in patients with clear cell renal carcinoma after radical nephrectomy in a long term follow-up. Int J Immunopathol Pharmacol. 21:447–455. 2008. View Article : Google Scholar : PubMed/NCBI | |
Iakovlev VV, Gabril M, Dubinski W, Scorilas A, Youssef YM, Faragalla H, Kovacs K, Rotondo F, Metias S, Arsanious A, et al: Microvascular density as an independent predictor of clinical outcome in renal cell carcinoma: An automated image analysis study. Lab Invest. 92:46–56. 2012. View Article : Google Scholar : PubMed/NCBI | |
Paradis V, Lagha NB, Zeimoura L, Blanchet P, Eschwege P, Ba N, Benoît G, Jardin A and Bedossa P: Expression of vascular endothelial growth factor in renal cell carcinomas. Virchows Arch. 436:351–356. 2000. View Article : Google Scholar : PubMed/NCBI | |
Zhang X, Yamashita M, Uetsuki H and Kakehi Y: Angiogenesis in renal cell carcinoma: Evaluation of microvessel density, vascular endothelial growth factor and matrix metalloproteinases. Int J Urol. 9:509–514. 2002. View Article : Google Scholar : PubMed/NCBI | |
Tuna B, Yorukoglu K, Unlu M, Mungan MU and Kirkali Z: Association of mast cells with microvessel density in renal cell carcinomas. Eur Urol. 50:530–534. 2006. View Article : Google Scholar : PubMed/NCBI | |
Slaton JW, Inoue K, Perrotte P, El-Naggar AK, Swanson DA, Fidler IJ and Dinney CP: Expression levels of genes that regulate metastasis and angiogenesis correlate with advanced pathological stage of renal cell carcinoma. Am J Pathol. 158:735–743. 2001. View Article : Google Scholar : PubMed/NCBI | |
Mohseni MG, Mohammadi A, Heshmat AS, Kosari F and Meysamie AP: The lack of correlation between mast cells and microvessel density with pathologic feature of renal cell carcinoma. Int Urol Nephrol. 42:109–112. 2010. View Article : Google Scholar : PubMed/NCBI | |
MacLennan GT and Bostwick DG: Microvessel density in renal cell carcinoma: Lack of prognostic significance. Urology. 46:27–30. 1995. View Article : Google Scholar : PubMed/NCBI | |
Gelb AB, Sudilovsky D, Wu CD, Weiss LM and Medeiros LJ: Appraisal of intratumoral microvessel density, MIB-1 score, DNA content, and p53 protein expression as prognostic indicators in patients with locally confined renal cell carcinoma. Cancer. 80:1768–1775. 1997. View Article : Google Scholar : PubMed/NCBI | |
Suzuki K, Morita T, Hashimoto S and Tokue A: Thymidine phosphorylase/platelet-derived endothelial cell growth factor (PD-ECGF) associated with prognosis in renal cell carcinoma. Urol Res. 29:7–12. 2001. View Article : Google Scholar : PubMed/NCBI | |
Minardi D, Lucarini G, Mazzucchelli R, Milanese G, Natali D, Galosi AB, Montironi R, Biagini G and Muzzonigro G: Prognostic role of fuhrman grade and vascular endothelial growth factor in pT1a clear cell carcinoma in partial nephrectomy specimens. J Urol. 174:1208–1212. 2005. View Article : Google Scholar : PubMed/NCBI | |
Anastassiou G, Duensing S, Steinhoff G, Zorn U, Grosse J, Dallmann I, Kirchner H, Ganser A and Atzpodien J: Platelet endothelial cell adhesion molecule-1 (PECAM-1): A potential prognostic marker involved in leukocyte infiltration of renal cell carcinoma. Oncology. 53:127–132. 1996. View Article : Google Scholar : PubMed/NCBI | |
Rioux-Leclercq N, Epstein JI, Bansard JY, Turlin B, Patard JJ, Manunta A, Chan T, Ramee MP, Lobel B and Moulinoux JP: Clinical significance of cell proliferation, microvessel density, and CD44 adhesion molecule expression in renal cell carcinoma. Hum Pathol. 32:1209–1215. 2001. View Article : Google Scholar : PubMed/NCBI | |
Yagasaki H, Kawata N, Takimoto Y and Nemoto N: Histopathological analysis of angiogenic factors in renal cell carcinoma. Int J Urol. 10:220–227. 2003. View Article : Google Scholar : PubMed/NCBI | |
Imao T, Egawa M, Takashima H, Koshida K and Namiki M: Inverse correlation of microvessel density with metastasis and prognosis in renal cell carcinoma. Int J Urol. 11:948–953. 2004. View Article : Google Scholar : PubMed/NCBI | |
Mertz KD, Demichelis F, Kim R, Schraml P, Storz M, Diener PA, Moch H and Rubin MA: Automated immunofluorescence analysis defines microvessel area as a prognostic parameter in clear cell renal cell cancer. Hum Pathol. 38:1454–1462. 2007. View Article : Google Scholar : PubMed/NCBI | |
Yildiz E, Ayan S, Goze F, Gokce G and Gultekin EY: Relation of microvessel density with microvascular invasion, metastasis and prognosis in renal cell carcinoma. BJU Int. 101:758–764. 2008. View Article : Google Scholar : PubMed/NCBI | |
Yoshino S, Kato M and Okada K: Prognostic significance of microvessel count in low stage renal cell carcinoma. Int J Urol. 2:156–160. 1995. View Article : Google Scholar : PubMed/NCBI | |
Sabo E, Boltenko A, Sova Y, Stein A, Kleinhaus S and Resnick MB: Microscopic analysis and significance of vascular architectural complexity in renal cell carcinoma. Clin Cancer Res. 7:533–537. 2001.PubMed/NCBI | |
Delahunt B, Bethwaite P and Thornton A: Prognostic significance of microscopic vascularity for clear cell renal cell carcinoma. Br J Urol. 80:401–404. 1997. View Article : Google Scholar : PubMed/NCBI | |
Schraml P, Struckmann K, Hatz F, Sonnet S, Kully C, Gasser T, Sauter G, Mihatsch MJ and Moch H: VHL mutations and their correlation with tumour cell proliferation, microvessel density, and patient prognosis in clear cell renal cell carcinoma. J Pathol. 196:186–193. 2002. View Article : Google Scholar : PubMed/NCBI | |
Sandlund J, Hedberg Y, Bergh A, Grankvist K, Ljungberg B and Rasmuson T: Endoglin (CD105) expression in human renal cell carcinoma. BJU Int. 97:706–710. 2006. View Article : Google Scholar : PubMed/NCBI | |
Sandlund J, Hedberg Y, Bergh A, Grankvist K, Ljungberg B and Rasmuson T: Evaluation of CD31 (PECAM-1) expression using tissue microarray in patients with renal cell carcinoma. Tumor Biol. 28:158–164. 2007. View Article : Google Scholar | |
Köhler HH, Barth PJ, Siebel A, Gerharz EW and Bittinger A: Quantitative assessment of vascular surface density in renal cell carcinomas. Br J Urol. 77:650–654. 1996. View Article : Google Scholar : PubMed/NCBI | |
Hemmerlein B, Kugler A, Özisik R, Ringert RH, Radzun HJ and Thelen P: Vascular endothelial growth factor expression, angiogenesis, and necrosis in renal cell carcinomas. Virchows Arch. 439:645–652. 2001. View Article : Google Scholar : PubMed/NCBI | |
Baldewijns MM, Thijssen VL, Van den Eynden GG, Van Laere SJ, Bluekens AM, Roskams T, van Poppel H, De Bruïne AP, Griffioen AW and Vermeulen PB: High-grade clear cell renal cell carcinoma has a higher angiogenic activity than low-grade renal cell carcinoma based on histomorphological quantification and qRT-PCR mRNA expression profile. Br J Cancer. 96:1888–1895. 2007. View Article : Google Scholar : PubMed/NCBI | |
Kavantzas N, Paraskevakou H, Tseleni-Balafouta S, Aroni K, Athanassiades P, Agrogiannis G and Patsouris E: Association between microvessel density and histologic grade in renal cell carcinomas. Pathol Oncol Res. 13:145–148. 2007. View Article : Google Scholar : PubMed/NCBI | |
Sharma SG, Aggarwal N, Gupta SD, Singh MK, Gupta R and Dinda AK: Angiogenesis in renal cell carcinoma: Correlation of microvessel density and microvessel area with other prognostic factors. Int Urol Nephrol. 43:125–129. 2011. View Article : Google Scholar : PubMed/NCBI | |
Weidner N: Intratumor microvessel density as a prognostic factor in cancer. Am J Pathol. 147:91995.PubMed/NCBI | |
Maniotis AJ, Folberg R, Hess A, Seftor EA, Gardner LM, Pe'er J, Trent JM, Meltzer PS and Hendrix MJ: Vascular channel formation by human melanoma cells in vivo and in vitro: Vasculogenic mimicry. Am J Pathol. 155:739–752. 1999. View Article : Google Scholar : PubMed/NCBI | |
Shirakawa K, Kobayashi H, Heike Y, Kawamoto S, Brechbiel MW, Kasumi F, Iwanaga T, Konishi F, Terada M and Wakasugi H: Hemodynamics in vasculogenic mimicry and angiogenesis of inflammatory breast cancer xenograft. Cancer Res. 62:560–566. 2002.PubMed/NCBI | |
Sun B, Zhang S, Zhang D, Du J, Guo H, Zhao X, Zhang W and Hao X: Vasculogenic mimicry is associated with high tumor grade, invasion and metastasis, and short survival in patients with hepatocellular carcinoma. Oncol Rep. 16:693–698. 2006.PubMed/NCBI | |
Lee H, Lee M, Lee SE, Byun SS, Kim HH, Kwak C and Hong SK: Outcomes of pathologic stage T3a renal cell carcinoma up-staged from small renal tumor: Emphasis on partial nephrectomy. BMC Cancer. 18:4272018. View Article : Google Scholar : PubMed/NCBI | |
Nowak-Sliwinska P, Alitalo K, Allen E, Anisimov A, Aplin AC, Auerbach R, Augustin HG, Bates DO, van Beijnum JR, Bender RHF, et al: Consensus guidelines for the use and interpretation of angiogenesis assays. Angiogenesis. 21:425–532. 2018. View Article : Google Scholar : PubMed/NCBI | |
Feng Y, Song K, Shang W, Chen L, Wang C, Pang B and Wang N: REDD1 overexpression in oral squamous cell carcinoma may predict poor prognosis and correlates with high microvessel density. Oncol Lett. 19:431–441. 2020.PubMed/NCBI | |
Vartanian AA, Stepanova EV, Gutorov SL, Solomko ES, Grigorieva IN, Sokolova IN, Baryshnikov AY and Lichinitser MR: Prognostic significance of periodic acid-Schiff-positive patterns in clear cell renal cell carcinoma. Can J Urol. 16:4726–4732. 2009.PubMed/NCBI | |
Zhang Y, Sun B, Zhao X, Liu Z, Wang X, Yao X, Dong X and Chi J: Clinical significances and prognostic value of cancer stem-like cells markers and vasculogenic mimicry in renal cell carcinoma. J Surg Oncol. 108:414–419. 2013. View Article : Google Scholar : PubMed/NCBI | |
Qiao L, Liang N, Zhang J, Xie J, Liu F, Xu D, Yu X and Tian Y: Advanced research on vasculogenic mimicry in cancer. J Cell Mol Med. 19:315–326. 2015. View Article : Google Scholar : PubMed/NCBI | |
Paulis YW, Soetekouw PM, Verheul HM, Tjan-Heijnen VC and Griffioen AW: Signalling pathways in vasculogenic mimicry. Biochim Biophys Acta. 1806:18–28. 2010.PubMed/NCBI | |
Kirschmann DA, Seftor EA, Hardy KM, Seftor RE and Hendrix MJ: Molecular pathways: Vasculogenic mimicry in tumor cells: Diagnostic and therapeutic implications. Clin Cancer Res. 18:2726–2732. 2012. View Article : Google Scholar : PubMed/NCBI | |
Bai J, Yeh S, Qiu X, Hu L, Zeng J, Cai Y, Zuo L, Li G, Yang G and Chang C: TR4 nuclear receptor promotes clear cell renal cell carcinoma (ccRCC) vasculogenic mimicry (VM) formation and metastasis via altering the miR490-3p/vimentin signals. Oncogene. 37:5901–5912. 2018. View Article : Google Scholar : PubMed/NCBI | |
Sabo E, Miselevich I, Bejar J, Segenreich M, Wald M, Moskovitz B and Nativ O: The role of vimentin expression in predicting the long-term outcome of patients with localized renal cell carcinoma. Br J Urol. 80:864–868. 1997. View Article : Google Scholar : PubMed/NCBI | |
Weidner N, Semple JP, Welch WR and Folkman J: Tumor angiogenesis and metastasis-correlation in invasive breast carcinoma. N Engl J Med. 324:1–8. 1991. View Article : Google Scholar : PubMed/NCBI | |
Sandlund J, Hedberg Y, Bergh A, Grankvist K, Ljungberg B and Rasmuson T: Endoglin (CD105) expression in human renal cell carcinoma. BJU Int. 97:706–710. 2006. View Article : Google Scholar : PubMed/NCBI | |
Sandlund J, Hedberg Y, Bergh A, Grankvist K, Ljungberg B and Rasmuson T: Evaluation of CD31 (PECAM-1) expression using tissue microarray in patients with renal cell carcinoma. Tumour Biol. 28:158–164. 2007. View Article : Google Scholar : PubMed/NCBI | |
Yao X, Qian CN, Zhang ZF, Tan MH, Kort EJ, Yang XJ, Resau JH and The BT: Two distinct types of blood vessels in clear cell renal cell carcinoma have contrasting prognostic implications. Clin Cancer Res. 13:161–169. 2007. View Article : Google Scholar : PubMed/NCBI | |
Qian CN, Huang D, Wondergem B and Teh BT: Complexity of tumor vasculature in clear cell renal cell carcinoma. Cancer. 115 (10 Suppl):S2282–S2289. 2009. View Article : Google Scholar | |
Kuczynski EA and Reynolds AR: Vessel co-option and resistance to anti-angiogenic therapy. Angiogenesis. 23:55–74. 2020. View Article : Google Scholar : PubMed/NCBI | |
Kuczynski EA, Vermeulen PB, Pezzella F, Kerbel RS and Reynolds AR: Vessel co-option in cancer. Nat Rev Clin Oncol. 16:469–493. 2019. View Article : Google Scholar : PubMed/NCBI | |
Latacz E, Caspani E, Barnhill R, Lugassy C, Verhoef C, Grünhagen D, Van Laere S, Moro CF, Gerling M, Dirix M, et al: Pathological features of vessel co-option versus sprouting angiogenesis. Angiogenesis. 23:43–54. 2020. View Article : Google Scholar : PubMed/NCBI | |
Yang JP, Liao YD, Mai DM, Xie P, Qiang YY, Zheng LS, Wang MY, Mei Y, Meng DF, Xu L, et al: Tumor vasculogenic mimicry predicts poor prognosis in cancer patients: A meta-analysis. Angiogenesis. 19:191–200. 2016. View Article : Google Scholar : PubMed/NCBI | |
Hueng DY, Lin GJ, Huang SH, Liu LW, Ju DT, Chen YW, Sytwu HK, Chang C, Huang SM, Yeh YS, et al: Inhibition of Nodal suppresses angiogenesis and growth of human gliomas. J Neurooncol. 104:21–31. 2011. View Article : Google Scholar : PubMed/NCBI | |
Feng X, Yu Y, He S, Cheng J, Gong Y, Zhang Z, Yang X, Xu B, Liu X, Li CY, et al: Dying glioma cells establish a proangiogenic microenvironment through a caspase 3 dependent mechanism. Cancer Lett. 385:12–20. 2017. View Article : Google Scholar : PubMed/NCBI | |
Bekes EM, Schweighofer B, Kupriyanova TA, Zajac E, Ardi VC, Quigley JP and Deryugina EI: Tumor-recruited neutrophils and neutrophil TIMP-free MMP-9 regulate coordinately the levels of tumor angiogenesis and efficiency of malignant cell intravasation. Am J Pathol. 179:1455–1470. 2011. View Article : Google Scholar : PubMed/NCBI | |
Jia W, Kidoya H, Yamakawa D, Naito H and Takakura N: Galectin-3 accelerates M2 macrophage infiltration and angiogenesis in tumors. Am J Pathol. 182:1821–1831. 2013. View Article : Google Scholar : PubMed/NCBI | |
Bentley K, Franco CA, Philippides A, Blanco R, Dierkes M, Gebala V, Stanchi F, Jones M, Aspalter IM, Cagna G, et al: The role of differential VE-cadherin dynamics in cell rearrangement during angiogenesis. Nat Cell Biol. 16:309–321. 2014. View Article : Google Scholar : PubMed/NCBI | |
Wang JY, Sun T, Zhao XL, Zhang SW, Zhang DF, Gu Q, Wang XH, Zhao N, Qie S and Sun BC: Functional significance of VEGF-a in human ovarian carcinoma: Role in vasculogenic mimicry. Cancer Biol Ther. 7:758–766. 2008. View Article : Google Scholar : PubMed/NCBI | |
Motzer RJ, Tannir NM, McDermott DF, Arén Frontera O, Melichar B, Choueiri TK, Plimack ER, Barthélémy P, Porta C, George S, et al: Nivolumab plus ipilimumab versus sunitinib in advanced renal-cell carcinoma. N Engl J Med. 378:1277–1290. 2018. View Article : Google Scholar : PubMed/NCBI | |
Powles T, Albiges L, Staehler M, Bensalah K, Dabestani S, Giles RH, Hofmann F, Hora M, Kuczyk MA, Lam TB, et al: Updated european association of urology guidelines recommendations for the treatment of first-line metastatic clear cell renal cancer. Eur Urol. 73:311–315. 2018. View Article : Google Scholar : PubMed/NCBI | |
Azuma T, Sugihara T, Honda S, Yoshizaki U, Niimi F, Tsuru I and Kume H: Metastatic renal cell carcinoma regains sensitivity to tyrosine kinase inhibitor after nivolumab treatment: A case report. Oncol Lett. 17:4011–4015. 2019.PubMed/NCBI | |
Wei W, Lv Y, Gan Z, Zhang Y, Han X and Xu Z: Identification of key genes involved in the metastasis of clear cell renal cell carcinoma. Oncol Lett. 17:4321–4328. 2019.PubMed/NCBI | |
Carlsson J, Christiansen J, Davidsson S, Giunchi F, Fiorentino M and Sundqvist P: The potential role of miR-126, miR-21 and miR-10b as prognostic biomarkers in renal cell carcinoma. Oncol Lett. 17:4566–4574. 2019.PubMed/NCBI | |
Gao Y, Qi JC, Li X, Sun JP, Ji H and Li QH: Decreased expression of TXNIP predicts poor prognosis in patients with clear cell renal cell carcinoma. Oncol Lett. 19:763–770. 2020.PubMed/NCBI | |
Yan N, Feng X, Jiang S, Sun W, Sun MZ and Liu S: GRIM-19 deficiency promotes clear cell renal cell carcinoma progression and is associated with high TNM stage and fuhrman grade. Oncol Lett. 19:4115–4121. 2020.PubMed/NCBI |