Collaborative overexpression of matrix metalloproteinase-1 and vascular endothelial growth factor-C predicts adverse prognosis in patients with gliomas

Xu, Yi; Zhong, Zhiwei; Yuan, Jun; Zhang, Ziheng; Wei, Quantang; Song, Weizheng; Chen, Hongwu

doi:10.1016/j.canep.2013.06.006

Cited by 16 publications

(14 citation statements)

References 33 publications

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“…The VEGF is active in angiogenesis and endothelial cell growth and can also affect the permeability of blood vessels. High VEGFC expression has been implicated with nodal metastasis and poor prognosis in T1 esophageal squamous cell carcinoma [29], gastric cancer [30], gliomas [31], and breast cancer [32]. …”

Section: Resultsmentioning

confidence: 99%

Nonlinear Quantitative Radiation Sensitivity Prediction Model Based on NCI-60 Cancer Cell Lines

Zhang

Girard

Das

et al. 2014

The Scientific World Journal

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We proposed a nonlinear model to perform a novel quantitative radiation sensitivity prediction. We used the NCI-60 panel, which consists of nine different cancer types, as the platform to train our model. Important radiation therapy (RT) related genes were selected by significance analysis of microarrays (SAM). Orthogonal latent variables (LVs) were then extracted by the partial least squares (PLS) method as the new compressive input variables. Finally, support vector machine (SVM) regression model was trained with these LVs to predict the SF2 (the surviving fraction of cells after a radiation dose of 2 Gy γ-ray) values of the cell lines. Comparison with the published results showed significant improvement of the new method in various ways: (a) reducing the root mean square error (RMSE) of the radiation sensitivity prediction model from 0.20 to 0.011; and (b) improving prediction accuracy from 62% to 91%. To test the predictive performance of the gene signature, three different types of cancer patient datasets were used. Survival analysis across these different types of cancer patients strongly confirmed the clinical potential utility of the signature genes as a general prognosis platform. The gene regulatory network analysis identified six hub genes that are involved in canonical cancer pathways.

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Section: Resultsmentioning

confidence: 99%

Nonlinear Quantitative Radiation Sensitivity Prediction Model Based on NCI-60 Cancer Cell Lines

Zhang

Girard

Das

et al. 2014

The Scientific World Journal

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“…Furthermore, VEGF-A promotes GSC viability and tumor growth, because the agonistic VEGF receptor 2 is preferentially expressed on GSCs [41,42]. VEGF-C is also expressed in GBM primary tumors [43,44]. Moreover, the VEGF-C receptor, VEGFR3, which is mainly expressed in lymphatic vessels, has been observed in blood endovascular cells [43] and can promote tumor angiogenesis [45,46].…”

Section: Discussionmentioning

confidence: 99%

Senescence from glioma stem cell differentiation promotes tumor growth

Ouchi

Okabe

Migita

et al. 2016

Biochemical and Biophysical Research Communications

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Glioblastoma (GBM) is a lethal brain tumor composed of heterogeneous cellular populations including glioma stem cells (GSCs) and differentiated non-stem glioma cells (NSGCs). While GSCs are involved in tumor initiation and propagation, NSGCs’ role remains elusive. Here, we demonstrate that NSGCs undergo senescence and secrete pro-angiogenic proteins, boosting the GSC-derived tumor formation in vivo. We used a GSC model that maintains stemness in neurospheres, but loses the stemness and differentiates into NSGCs upon serum stimulation. These NSGCs downregulated telomerase, shortened telomeres, and eventually became senescent. The senescent NSGCs released pro-angiogenic proteins, including vascular endothelial growth factors and senescence-associated interleukins, such as IL-6 and IL-8. Conditioned medium from senescent NSGCs promoted proliferation of brain microvascular endothelial cells, and mixed implantation of GSCs and senescent NSGCs into mice enhanced the tumorigenic potential of GSCs. The senescent NSGCs seem to be clinically relevant, because both clinical samples and xenografts of GBM contained tumor cells that expressed the senescence markers. Our data suggest that senescent NSGCs promote malignant progression of GBM in part via paracrine effects of the secreted proteins.

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“…Patients with anaplastic astrocytomas with positive immunoreactivity for the cleaved caspase-3 in more than 10 % tumor cells had better survival rate in a study by Kobayashi et al (2007). On the contrary, higher expression of ADAM8 (He et al 2012a), CSN5 (Jab1) (He et al 2012b), MMP13 , kallikrein 6 (Drucker et al 2013), uPA (Bindal et al 1994;Hsu et al 1995), MMP1 Xu et al 2013), and cathepsin-B (Colin et al 2009) are negative prognostic factors in gliomas. On the contrary, higher expression of ADAM8 (He et al 2012a), CSN5 (Jab1) (He et al 2012b), MMP13 , kallikrein 6 (Drucker et al 2013), uPA (Bindal et al 1994;Hsu et al 1995), MMP1 Xu et al 2013), and cathepsin-B (Colin et al 2009) are negative prognostic factors in gliomas.…”

Section: Exploitation Of Glioma-associated Proteases As Possible Markmentioning

confidence: 96%

“…Increased (Stojic et al 2008;, expression associated with worse survival Xu et al 2013), polymorphism in the promotor region linked to glioblastoma (McCready et al 2005), possible role in glioma invasion (Gessler et al 2011); mediates the NO-induced increase of glioma cell motility (Pullen and Fillmore 2010), promotes tumor-induced angiogenesis (Pullen et al 2012). …”

Section: Csn5/jab1mentioning

confidence: 99%

Glioma Cell Biology

Šedo¹,

Mentlein²

2014

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Collaborative overexpression of matrix metalloproteinase-1 and vascular endothelial growth factor-C predicts adverse prognosis in patients with gliomas

Cited by 16 publications

References 33 publications

Nonlinear Quantitative Radiation Sensitivity Prediction Model Based on NCI-60 Cancer Cell Lines

Nonlinear Quantitative Radiation Sensitivity Prediction Model Based on NCI-60 Cancer Cell Lines

Senescence from glioma stem cell differentiation promotes tumor growth

Glioma Cell Biology

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