Uncovering a Distinct Gene Signature in Endothelial Cells Associated With Contrast Enhancement in Glioblastoma

Single-Cell Transcriptomics Reveals Evidence of Endothelial Dysfunction in the Brains of COVID-19 Patients with Implications for Glioblastoma Progression

BACKGROUND

Endothelial dysfunction is implicated in various inflammatory diseases such as ischemic stroke, heart attack, organ failure, and COVID-19. Recent studies have shown that endothelial dysfunction in the brain is attributed to excessive inflammatory responses caused by the SARS-CoV-2 infection, leading to increased permeability of the blood-brain barrier and consequently neurological damage. Here, we aim to examine the single-cell transcriptomic landscape of endothelial dysfunction in COVID-19 and its implications for glioblastoma (GBM) progression.

METHODS

Single-cell transcriptome data GSE131928 and GSE159812 were obtained from the gene expression omnibus (GEO) to analyze the expression profiles of key players in innate immunity and inflammation between brain endothelial dysfunction caused by COVID-19 and GBM progression.

RESULTS

Single-cell transcriptomic analysis of the brain of COVID-19 patients revealed that endothelial cells had undergone significant transcriptomic changes, with several genes involved in immune responses and inflammation upregulated. Moreover, transcription factors were observed to modulate this inflammation, including interferon-regulated genes.

CONCLUSIONS

The results indicate a significant overlap between COVID-19 and GBM in the context of endothelial dysfunction, suggesting that there may be an endothelial dysfunction link connecting severe SARS-CoV-2 infection in the brain to GBM progression.

The combination of radiomics features and VASARI standard to predict glioma grade

Background and Purpose

Radiomics features and The Visually AcceSAble Rembrandt Images (VASARI) standard appear to be quantitative and qualitative evaluations utilized to determine glioma grade. This study developed a preoperative model to predict glioma grade and improve the efficacy of clinical strategies by combining these two assessment methods.

Materials and Methods

Patients diagnosed with glioma between March 2017 and September 2018 who underwent surgery and histopathology were enrolled in this study. A total of 3840 radiomic features were calculated; however, using the least absolute shrinkage and selection operator (LASSO) method, only 16 features were chosen to generate a radiomic signature. Three predictive models were developed using radiomic features and VASARI standard. The performance and validity of models were evaluated using decision curve analysis and 10-fold nested cross-validation.

Results

Our study included 102 patients: 35 with low-grade glioma (LGG) and 67 with high-grade glioma (HGG). Model 1 utilized both radiomics and the VASARI standard, which included radiomic signatures, proportion of edema, and deep white matter invasion. Models 2 and 3 were constructed with radiomics or VASARI, respectively, with an area under the receiver operating characteristic curve (AUC) of 0.937 and 0.831, respectively, which was less than that of Model 1, with an AUC of 0.966.

Conclusion

The combination of radiomics features and the VASARI standard is a robust model for predicting glioma grades.

Expression of hub genes of endothelial cells in glioblastoma-A prognostic model for GBM patients integrating single-cell RNA sequencing and bulk RNA sequencing

BACKGROUND

This study aimed to use single-cell RNA-seq (scRNA-seq) to discover marker genes in endothelial cells (ECs) and construct a prognostic model for glioblastoma multiforme (GBM) patients in combination with traditional high-throughput RNA sequencing (bulk RNA-seq).

METHODS

Bulk RNA-seq data was downloaded from The Cancer Genome Atlas (TCGA) and The China Glioma Genome Atlas (CGGA) databases. 10x scRNA-seq data for GBM were obtained from the Gene Expression Omnibus (GEO) database. The uniform manifold approximation and projection (UMAP) were used for downscaling and cluster identification. Key modules and differentially expressed genes (DEGs) were identified by weighted gene correlation network analysis (WGCNA). A non-negative matrix decomposition (NMF) algorithm was used to identify the different subtypes based on DEGs, and multivariate cox regression analysis to model the prognosis. Finally, differences in mutational landscape, immune cell abundance, immune checkpoint inhibitors (ICIs)-associated genes, immunotherapy effects, and enriched pathways were investigated between different risk groups.

RESULTS

The analysis of scRNA-seq data from eight samples revealed 13 clusters and four cell types. After applying Fisher's exact test, ECs were identified as the most important cell type. The NMF algorithm identified two clusters with different prognostic and immunological features based on DEGs. We finally built a prognostic model based on the expression levels of four key genes. Higher risk scores were significantly associated with poorer survival outcomes, low mutation rates in IDH genes, and upregulation of immune checkpoints such as PD-L1 and CD276.

CONCLUSION

We built and validated a 4-gene signature for GBM using 10 scRNA-seq and bulk RNA-seq data in this work.

PLVAP is associated with glioma-associated malignant processes and immunosuppressive cell infiltration as a promising marker for prognosis

Previous reports have confirmed the significance of plasmalemma vesicle-associated protein (PLVAP) in the progression of multiple tumors; however, there are few studies examining its immune properties in the context of gliomas. Here, we methodically investigated the pathophysiological characteristics and clinical manifestations of gliomas. A total of 699 patients diagnosed with gliomas in the cancer genome atlas along with 325 glioma patients in the Chinese glioma genome atlas were collected for the training and validation sets. We analyzed and visualized the total statistics using RStudio. PLVAP was markedly upregulated among high grade gliomas, O⁶-methylguanine-DNA methyltransferase promoter unmethylated subforms, isocitrate dehydrogenase wild forms, 1p19q non-codeletion subforms, and mesenchyme type gliomas. The receiver operating characteristics analysis illustrated the favorable applicability of PLVAP in regard to estimating mesenchyme subform gliomas. Subsequent Kaplan–Meier curves together with multivariable Cox analyses upon survival identified high-expression PLVAP as a distinct prognostic variable for patients with gliomas. Gene ontology analysis of PLVAP among gliomas has documented the predominant role of this protein in glioma-associated immunobiological processes and also in inflammatory responses. We consequently examined the associations of PLVAP with immune-related meta-genes, and PLVAP was positively correlated with hematopoietic cell kinase, lymphocyte-specific protein tyrosine kinase, major histocompatibility complex (MHC) I, MHC II, signal transducer and activator of transcription 1, and interferon and was negatively correlated with immunoglobulin G. Moreover, association analyses between PLVAP and glioma-infiltrating immunocytes indicated that the infiltrating degrees of most immune cells exhibited positive correlations with PLVAP expression, particularly immunosuppressive subsets such as tumor-related macrophages, myeloid-derived suppressor cells, and regulatory T lymphocytes. In summary, we originally demonstrated that PLVAP is markedly associated with immunosuppressive immune cell infiltration degrees, unfavorable survival, and adverse pathology types among gliomas, thus identifying PLVAP as a practicable marker and a promising target for glioma-based precise diagnosis and therapeutic strategies.

Uncovering a Key Role of ETS1 on Vascular Abnormality in Glioblastoma

Glioblastoma (GBM) is the most aggressive type of brain tumor. Microvascular proliferation and abnormal vasculature are the hallmarks of the GBM, aggravating disease progression and increasing patient morbidity. Here, we uncovered a key role of ETS1 on vascular abnormality in glioblastoma. ETS1 was upregulated in endothelial cells from human tumors compared to endothelial cells from paired control brain tissue. Knockdown of Ets1 in mouse brain endothelial cells inhibited cell migration and proliferation, and suppressed expression of genes associated with vascular abnormality in GBM. ETS1 upregulation in tumor ECs was dependent on TGFβ signaling, and targeting TGFβ signaling by inhibitor decreased tumor angiogenesis and vascular abnormality in CT-2A glioma model. Our results identified ETS1 as a key factor regulating tumor angiogenesis, and suggested that TGFβ inhibition may suppress the vascular abnormality driven by ETS1.

Preoperative Magnetic Resonance Imaging Radiomics for Predicting Early Recurrence of Glioblastoma

Purpose

Early recurrence of glioblastoma after standard treatment makes patient care challenging. This study aimed to assess preoperative magnetic resonance imaging (MRI) radiomics for predicting early recurrence of glioblastoma.

Patients and Methods

A total of 122 patients (training cohort: n = 86; validation cohort: n = 36) with pathologically confirmed glioblastoma were included in this retrospective study. Preoperative brain MRI images were analyzed for both radiomics and the Visually Accessible Rembrandt Image (VASARI) features of glioblastoma. Models incorporating MRI radiomics, the VASARI parameters, and clinical variables were developed and presented in a nomogram. Performance was assessed based on calibration, discrimination, and clinical usefulness.

Results

The nomogram consisting of the radiomic signatures, the VASARI parameters, and blood urea nitrogen (BUN) values showed good discrimination between the patients with early recurrence and those with later recurrence, with an area under the curve of 0.85 (95% CI, 0.77-0.94) in the training cohort and 0.84 [95% CI, 0.71-0.97] in the validation cohort. Decision curve analysis demonstrated favorable clinical application of the nomogram.

Conclusion

This study showed the potential usefulness of preoperative brain MRI radiomics in predicting the early recurrence of glioblastoma, which should be helpful in personalized management of glioblastoma.