Circulating Proangiogenic Cells and Proteins in Patients with Glioma and Acute Myocardial Infarction: Differences in Neovascularization between Neoplasia and Tissue Regeneration

Double Immunohistochemical Staining on Formalin-Fixed Paraffin-Embedded Tissue Samples to Study Vascular Co-option

Vascular co-option is a non-angiogenic mechanism whereby tumor growth and progression move on by hijacking the pre-existing and nonmalignant blood vessels and is employed by various tumors to grow and metastasize.The histopathological identification of co-opted blood vessels is complex, and no specific markers were defined, but it is critical to develop new and possibly more effective therapeutic strategies. Here, in glioblastoma, we show that the co-opted blood vessels can be identified, by double immunohistochemical staining, as weak CD31⁺ vessels with reduced P-gp expression and proliferation and surrounded by highly proliferating and P-gp- or S100A10-expressing tumor cells. Results can be quantified by the Aperio Colocalization algorithm, which is a valid and robust method to handle and investigate large data sets.

Differential P-Glycoprotein/CD31 Expression as Markers of Vascular Co-Option in Primary Central Nervous System Tumors

BACKGROUND

Vascular co-option is one of the main features of brain tumor progression. It is identified using histopathological analysis, but no antibody-specific markers were found, and no universally accepted histological features were defined.

METHODS

We employed double immunohistochemical stainings for CD31, P-gp, S100A10, and mitochondria on formalin-fixed, paraffin-embedded human samples of IDH-WT glioblastoma, IDH-mutant astrocytoma, and meningioma to study vascular co-option across different brain tumors and across normal, peritumoral, and intratumoral areas using the Aperio colocalization algorithm, which is a valid and robust method to handle and investigate large data sets.

RESULTS

The results have shown that (i) co-opted vessels could be recognized by the presence of metabolically overactive (evaluated as mitochondria expression) and P-gp⁺ or S100A10⁺ tumor cells surrounding CD31⁺ endothelial cells; (ii) vascular co-option occurs in the intratumoral area of meningioma and astrocytoma; and (iii) vascular co-option is prevalent in peritumoral glioblastoma area.

CONCLUSIONS

The described approach identifies new markers for cellular components of the vessel wall and techniques that uncover the order and localization of vascularization mechanisms, which may contribute to developing new and possibly more effective therapeutic strategies.

Construction and Validation of an Immune-Related Risk Score Model for Survival Prediction in Glioblastoma

Background

As one of the most important brain tumors, glioblastoma (GBM) has a poor prognosis, especially in adults. Immune-related genes (IRGs) and immune cell infiltration are responsible for the pathogenesis of GBM. This study aimed to identify new tumor markers to predict the prognosis of patients with GBM.

Methods

The Cancer Genome Atlas (TCGA) database and ImmPort database were used for model construction. The Wilcoxon rank-sum test was applied to identify the differentially expressed IRGs (DEIRGs) between the GBM and normal samples. Univariate Cox regression analysis and Kaplan–Meier analysis was performed to investigate the relationship between each DEIRG and overall survival. Next, multivariate Cox regression analysis was exploited to further explore the prognostic potential of DEIRGs. A risk-score model was constructed based on the above results. The area under the curve (AUC) values were calculated to assess the effect of the model prediction. Furthermore, the Chinese Glioma Genome Atlas (CGGA) dataset was used for model validation. STRING database and functional enrichment analysis were used for exploring the gene interactions and the underlying functions and pathways. The CIBERSORT algorithm was used for correlation analysis of the marker genes and the tumor-infiltrating immune cells.

Results

There were 198 DEIRGs in GBM, including 153 upregulated genes and 45 downregulated genes. Seven marker genes (LYNX1, PRELID1P4, MMP9, TCF12, RGS14, RUNX1, and CCR2) were filtered out by sequential screening for DEIRGs. The regression coefficients (0.0410, 1.335, 0.005, −0.021, 0.123, 0.142, and −0.329) and expression data of the marker genes were used to construct the model. The AUC values for 1, 2, and 3 years were 0.744, 0.737, and 0.749 in the TCGA–GBM cohort and 0.612, 0.602, and 0.594 in the CGGA-GBM cohort, respectively, which indicated a high predictive power. The results of enrichment analysis revealed that these genes were enriched in the activation of T cell and cytokine receptor interaction pathways. The interaction network map demonstrated a close relationship between the marker genes MMP9 and CCR2. Infiltration analysis of the immune cells showed that dendritic cells (DCs) could identify GBM, while LYNX1, RUNX1, and CCR2 were significantly positively correlated with DCs expression.

Conclusion

This study analyzed the expression of IRGs in GBM and identified seven marker genes for the construction of an immune-related risk score model. These marker genes were found to be associated with DCs and were enriched in similar immune response pathways. These findings are likely to provide new insights for the immunotherapy of patients with GBM.

Tumor Vessels Fuel the Fire in Glioblastoma

Glioblastoma, a subset of aggressive brain tumors, deploy several means to increase blood vessel supply dedicated to the tumor mass. This includes typical program borrowed from embryonic development, such as vasculogenesis and sprouting angiogenesis, as well as unconventional processes, including co-option, vascular mimicry, and transdifferentiation, in which tumor cells are pro-actively engaged. However, these neo-generated vascular networks are morphologically and functionally abnormal, suggesting that the vascularization processes are rather inefficient in the tumor ecosystem. In this review, we reiterate the specificities of each neovascularization modality in glioblastoma, and, how they can be hampered mechanistically in the perspective of anti-cancer therapies.

Circulating angiogenic cells in glioblastoma: toward defining crucial functional differences in CAC-induced neoplastic versus reactive neovascularization

Background

In order to identify suitable therapeutic targets for glioma anti-angiogenic therapy, the process of neovascularization mediated by circulating angiogenic cells (CACs) needs to be scrutinized.

Methods

In the present study, we compared the expression of neovascularization-related genes by 3 circulating CAC subsets (hematopoietic progenitor cells [HPCs], CD34⁺, and KDR⁺ cells; internal controls: peripheral blood mononuclear cells and circulating endothelial cells) of treatment-naïve patients with glioblastoma (GBM) to those of patients undergoing reactive neovascularization (myocardial infarction (MI). CACs from umbilical cord (representing developmental neovascularization) and healthy subjects served as controls. Fluorescent-activated cell sorting was used to isolate CACs, RT-PCR to determine the expression levels of a panel of 48 neovascularization-related genes, and Luminex assays to measure plasma levels of 21 CAC-related circulating molecules.

Results

We found essential differences in gene expression between GBM and MI CACs. GBM CACs had a higher expression of proangiogenic factors (especially, KITL, CXCL12, and JAG1), growth factor and chemotactic receptors (IGF1R, TGFBR2, CXCR4, and CCR2), adhesion receptor monomers (ITGA5 and ITGA6), and matricellular factor POSTN. In addition, we found major differences in the levels of neovascularization-related plasma factors. A strong positive correlation between plasma MMP9 levels and expression of CXCR4 in the CAC subset of HPCs was found in GBM patients.

Conclusions

Our findings indicate that CAC-mediated neovascularization in GBM is characterized by more efficient CAC homing to target tissue and a more potent proangiogenic response than in physiologic tissue repair in MI. Our findings can aid in selecting targets for therapeutic strategies acting against GBM-specific CACs.