Background Gliomas are the most common primary malignant tumours of the central nervous system (CNS). To improve the prognosis of glioma, it is necessary to identify molecular markers that may be useful for glioma therapy. HOXC6, an important transcription factor, is involved in multiple cancers. However, the role of HOXC6 in gliomas is not clear. Methods Bioinformatic and IHC analyses of collected samples (n = 299) were performed to detect HOXC6 expression and the correlation between HOXC6 expression and clinicopathological features of gliomas. We collected clinical information from 177 to 299 patient samples and estimated the prognostic value of HOXC6. Moreover, cell proliferation assays were performed. We performed Gene Ontology (GO) analysis and gene set enrichment analysis (GSEA) based on ChIP-seq and public datasets to explore the biological characteristics of HOXC6 in gliomas. RNA-seq was conducted to verify the relationship between HOXC6 expression levels and epithelial-mesenchymal transition (EMT) biomarkers. Furthermore, the tumour purity, stromal and immune scores were evaluated. The relationship between HOXC6 expression and infiltrating immune cell populations and immune checkpoint proteins was also researched. Results HOXC6 was overexpressed and related to the clinicopathological features of gliomas. In addition, knockdown of HOXC6 inhibited the proliferation of glioma cells. Furthermore, increased HOXC6 expression was associated with clinical progression. The biological role of HOXC6 in gliomas was primarily associated with EMT and the immune microenvironment in gliomas. High HOXC6 expression was related to high infiltration by immune cells, a low tumour purity score, a high stromal score, a high immune score and the expression of a variety of immune checkpoint genes, including PD-L1, B7-H3 and CLTA-4. Conclusions These results indicated that HOXC6 might be a key factor in promoting tumorigenesis and glioma progression by regulating the EMT signalling pathway and might represent a novel immune therapeutic target in gliomas.
The main aim of this study was to investigate the therapeutic effect of endovascular interventional therapy on cerebral venous sinus thrombosis (CVST). 137 patients with CVST were included, 92 patients were treated with interventional therapy, and 45 patients were treated with conventional anticoagulant therapy. Through endovascular therapy (EVT) combined with therapy, the patients were treated with EVT in combination with conventional anticoagulant therapy, and the prognosis of the two groups of patients was evaluated. The results showed that 26 patients were complicated with female-specific infections in the combined EVT group, and 7 patients had female-specific infections in the simple anticoagulant therapy (LMWH) group. In terms of central nervous system infections, the EVT group was significantly lower than the LMWH group, P < 0.001 , and the difference was statistically significant. There were 2 cases of EVT involving the inferior sagittal sinus and 12 cases of LMWH involving the inferior sagittal sinus, P < 0.001 , and the difference had statistical significance. Through the RANKIN scale (mRS) score, it was classified as complete recovery and good prognosis (dependent variable). The patients receiving EVT with good prognosis (96.7%) were more than those receiving simple anticoagulant therapy (84.4%), and 78.3% were completely recovered after EVT, and 77.5% were completely recovered after anticoagulant therapy. Therefore, it can be concluded that gender, malignant tumors, thrombosis, and sinuses are all risk factors affecting the prognosis of patients; both endovascular interventional therapy and anticoagulant therapy can significantly improve the prognosis of patients.
Background:Glioblastoma multiforme (GBM) is one of the most common malignancies in the world and many studies have used traditional high-throughput RNA sequencing (bulk RNA-seq) data to explore its potential prognostic markers. However, it is unable to detect specific cellular and molecular changes in tumour cells. The aim of this study was to use single cell RNA-seq (scRNA-seq) to discover marker genes in endothelial cells and to construct a prognostic model for GBM patients in combination with traditional RNA-seq data.Methods:Bulk RNA-seq data were 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) was used for downscaling and cluster identification. Key modules and differentially expressed genes (DEGs) were identified by weighted gene correlation network analysis (WGCNA). Non-negative matrix decomposition (NMF) algorithm was used to identify different subtypes based on DEGS. And Cox regression analysis was used to build prognostic models. Finally, differences in mutational landscape, immune cell abundance, immune checkpoint inhibitors (ICIs)-associated genes, immunotherapy effects and enriched pathways were also investigated between different risk groups.RESULT:The analysis of scRNA-seq data from eight samples revealed 13 clusters and four cell types. After applying differential analysis, endothelial cells were identified as the most important significant cell type. In addition, we explored potential neoangiogenic pathways in GBM after controlling for phenotypic differences between GBM and normal brain tissue ECs at the single cell level. Overall, through differential analysis, WGCNA and screening of endothelial cell marker genes, we identified 157 DEGs for the construction of prognostic models. In addition, based on DEGs,the NMF algorithm identified two clusters with different prognostic and immunological features observed. We finally built a prognostic model based on the expression levels of four of these key genes. Higher risk scores were significantly associated with poorer survival outcomes and were associated with low mutation rates in IDH genes and upregulation of immune checkpoints such as PD-L1 and CD276. The CGGA cohort served as an external validation cohort for our findings.Conclusion:We built and validated a 4-gene signature for GBM using 10 scRNA-seq and bulk RNA-seq data in this work. We believe our findings will provide greater insight into the characteristics of ECs in GBM and provide potential prognostic biomarkers to design rational treatment plans.
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