As a substantial part of the brain tumor microenvironment (TME), glioma-associated microglia/macrophages (GAMs) have an emerging role in tumor progression and in controlling anti-tumor immune responses. We review challenges and improvements of cell models and highlight the contribution of this highly plastic cell population to an immunosuppressive TME, besides their well-known functional role regarding glioma cell invasion and angiogenesis. Finally, we summarize first therapeutic interventions to target GAMs and their effect on the immunobiology of gliomas, focusing on their interaction with T cells.

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Kinesins play an important role in many physiological functions including intracellular vesicle transport and mitosis. The emerging role of kinesins in different cancers led us to investigate the expression and functional role of kinesins in meningioma. Therefore, we re-analyzed our previous microarray dataset of benign, atypical, and anaplastic meningiomas (n = 62) and got evidence for differential expression of five kinesins (KIFC1, KIF4A, KIF11, KIF14 and KIF20A). Further validation in an extended study sample (n = 208) revealed a significant upregulation of these genes in WHO°I to °III meningiomas (WHO°I n = 61, WHO°II n = 88, and WHO°III n = 59), which was most pronounced in clinically more aggressive tumors of the same WHO grade. Immunohistochemical staining confirmed a WHO grade-associated upregulated protein expression in meningioma tissues. Furthermore, high mRNA expression levels of KIFC1, KIF11, KIF14 and KIF20A were associated with shorter progression-free survival. On a functional level, knockdown of kinesins in Ben-Men-1 cells and in the newly established anaplastic meningioma cell line NCH93 resulted in a significantly inhibited tumor cell proliferation upon siRNA-mediated downregulation of KIF11 in both cell lines by up to 95% and 71%, respectively. Taken together, in this study we were able to identify the prognostic and functional role of several kinesin family members of which KIF11 exhibits the most promising properties as a novel prognostic marker and therapeutic target, which may offer new treatment options for aggressive meningiomas.

SummaryComparative metabolomics comes of age through commercial vendors offering metabolomics for translational researchers outside the mass spectrometry field. The MetaboDiff packages aims to provide a low-level entry to differential metabolomic analysis with R by starting off with the table of metabolite measurements. As a key functionality, MetaboDiffs offers the exploration of sample traits in a data-derived metabolic correlation network.Availability and implementationThe MetaboDiff R package is platform-independent, available at http://github.com/andreasmock/MetaboDiff/ and released under the MIT licence. The package documentation comprises a step-by-step markdown tutorial.Supplementary information Supplementary data are available at Bioinformatics online.