BRMS1

The metastatic suppressor BRMS1 interacts with critical steps of the metastatic cascade in many cancer entities. As gliomas rarely metastasize, BRMS1 has mainly been neglected in glioma research. However, its interaction partners, such as NFκB, VEGF, or MMPs, are old acquaintances in neurooncology. The steps regulated by BRMS1, such as invasion, migration, and apoptosis, are commonly dysregulated in gliomas. Therefore, BRMS1 shows potential as a regulator of glioma behavior. By bioinformatic analysis, in addition to our cohort of 118 specimens, we determined BRMS1 mRNA and protein expression as well as its correlation with the clinical course in astrocytomas IDH mutant, CNS WHO grade 2/3, and glioblastoma IDH wild-type, CNS WHO grade 4. Interestingly, we found BRMS1 protein expression to be significantly decreased in the aforementioned gliomas, while BRMS1 mRNA appeared to be overexpressed throughout. This dysregulation was independent of patients' characteristics or survival. The protein and mRNA expression differences cannot be finally explained at this stage. However, they suggest a post-transcriptional dysregulation that has been previously described in other cancer entities. Our analyses present the first data on BRMS1 expression in gliomas that can provide a starting point for further investigations.

Ischemic stroke (IS) is a type of stroke that leads to high mortality and disability. Anoikis is a form of programmed cell death. When cells detach from the correct extracellular matrix, anoikis disrupts integrin junctions, thus preventing abnormal proliferating cells from growing or attaching to an inappropriate matrix. Although there is growing evidence that anoikis regulates the immune response, which makes a great contribution to the development of IS, the role of anoikis in the pathogenesis of IS is rarely explored.

First, we downloaded GSE58294 set and GSE16561 set from the NCBI GEO database. And 35 anoikis-related genes (ARGs) were obtained from GSEA website. The CIBERSORT algorithm was used to estimate the relative proportions of 22 infiltrating immune cell types. Next, consensus clustering method was used to classify ischemic stroke samples. In addition, we used least absolute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF) algorithms to screen the key ARGs in ischemic stroke. Next, we performed receiver operating characteristics (ROC) analysis to assess the accuracy of each diagnostic gene. At the same time, the nomogram was constructed to diagnose IS by integrating trait genes. Then, we analyzed the correlation between gene expression and immune cell infiltration of the diagnostic genes in the combined database. And gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) analysis were performed on these genes to explore differential signaling pathways and potential functions, as well as the construction and visualization of regulatory networks using NetworkAnalyst and Cytoscape. Finally, we investigated the expression pattern of ARGs in IS patients across age or gender.

Our study comprehensively analyzed the role of ARGs in IS for the first time. We revealed the expression profile of ARGs in IS and the correlation with infiltrating immune cells. And The results of consensus clustering analysis suggested that we can classify IS patients into two clusters. The machine learning analysis screened five signature genes, including AKT1, BRMS1, PTRH2, TFDP1 and TLE1. We also constructed nomogram models based on the five risk genes and evaluated the immune infiltration correlation, gene-miRNA, gene-TF and drug-gene interaction regulatory networks of these signature genes. The expression of ARGs did not differ by sex or age.

This study may provide a beneficial reference for further elucidating the pathogenesis of IS, and render new ideas for drug screening, individualized therapy and immunotherapy of IS.

Glioblastoma (GBM) is the most malignant subtype of astrocytic tumors with the worst prognosis in all its progressive forms. Breast cancer metastasis suppressor 1 (BRMS1) is a metastasis suppressor gene that controls malignancy in multiple tumors. As yet, however, its clinical and functional significance in mutant P53 GBM remains inconclusive. Here, we attempted to study the importance of BRMS1 in mutant P53 GBM.

BRMS1 expression was evaluated in 74 human astrocytoma tissues by qRT-PCR, Western blotting and immunohistochemistry. BRMS1 expression in the astrocytoma tissues was correlated with clinicopathological parameters, the P53 mutation status and BRMS1 downstream targets, and compared with TCGA and NCI-60 datasets. siRNA-mediated knockdown of BRMS1 was performed in selected GBM cell lines to evaluate the functional role of BRMS1.

Our study revealed an enhanced expression of BRMS1 in GBM which was associated with a poor patient survival, and this observation was corroborated by the TCGA dataset. We also found a positive correlation between BRMS1 expression and a mutant P53 status in GBM which was associated with a poor prognosis. In vitro BRMS1 silencing reduced the growth of mutant P53 GBM cells and repressed their colonization and migration/invasion by modulating EGFR-AKT/NF-κB signaling. Transcriptional profiling revealed a positive and negative correlation of uPA and ING4 expression with BRMS1 expression, respectively.

Our data indicate upregulation of BRMS1 in high grade astrocytomas which correlates positively with mutant P53 and a poor patient survival. Silencing of BRMS1 in mutant P53 GBM cell lines resulted in a reduced cellular growth and migration/invasion by suppressing the EGFR-AKT/NF-kB signaling pathway. BRMS1 may serve as a predictive biomarker and therapeutic target in mutant P53 GBM.