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Bmi1, the main component of the Polycomb repressive complex 1, plays a key role in self-renewal of stem cells as well as in proliferation of progenitor cells and senescence, at least in part through inhibition of the Cdkn2a locus. Bmi1 is highly expressed in the developing cerebellum, where it contributes to Shh-mediated expansion of granule cell precursors. Overexpression of Bmi1 has been described in medulloblastoma, highly aggressive brain neoplasms of childhood, which are thought to originate from deregulated proliferation of granule cell precursors. Here, we dissected the molecular mechanisms mediating the role of Bmi1 in granule cell development by means of transcriptome analysis in loss of function mouse models in vitro and in vivo. We demonstrate that lack of Bmi1 causes significant shift in gene expression levels in Shh stimulated cerebellar granule progenitors. Our results revealed differences in the expression of a number of genes involved in TGF-beta signal transduction pathway, ECM remodeling and cell adhesion, and particularly, in cell cycle control, not only the well known cell cycle inhibitors p16(Ink4a), p19(Arf) but also Cdkn1a (p21(Waf1/Cip1)). Finally, we demonstrate that Bmi1 directly regulates p21(Waf1/Cip1) expression through direct binding to its promoter and may therefore represent a key mechanism mediating the role of Shh in postnatal cerebellar neurogenesis.
Paediatric postoperative cerebellar mutism syndrome (ppCMS) is a common complication following the resection of a cerebellar tumour in children. It is hypothesized that loss of integrity of the cerebellar output tracts results in a cerebello-cerebral "diaschisis" and reduced function of supratentorial areas of the brain.
Hemangioblastomas (HBs) of the central nervous system are highly vascular neoplasms that occur sporadically or as a manifestation of von Hippel-Lindau (VHL) disease. Despite their benign nature, HBs are clinically heterogeneous and can be associated with significant morbidity due to mass effects of peritumoral cysts or tumor progression. Underlying molecular factors involved in HB tumor biology remain elusive. We investigated genome-wide DNA methylation profiles and clinical and histopathological features in a series of 47 HBs from 42 patients, including 28 individuals with VHL disease. Thirty tumors occurred in the cerebellum, 8 in the brainstem and 8 HBs were of spinal location, while 1 HB was located in the cerebrum. Histologically, 12 HBs (26%) belonged to the cellular subtype and exclusively occurred in the cerebellum, whereas 35 HBs were reticular (74%). Unsupervised clustering and dimensionality reduction of DNA methylation profiles revealed two distinct subgroups. Methylation cluster 1 comprised 30 HBs of mainly cerebellar location (29/30, 97%), whereas methylation cluster 2 contained 17 HBs predominantly located in non-cerebellar compartments (16/17, 94%). The sum of chromosomal regions being affected by copy-number alterations was significantly higher in methylation cluster 1 compared to cluster 2 (mean 262 vs. 109 Mb, p = 0.001). Of note, loss of chromosome 6 occurred in 9/30 tumors (30%) of methylation cluster 1 and was not observed in cluster 2 tumors (p = 0.01). No relevant methylation differences between sporadic and VHL-related HBs or cystic and non-cystic HBs could be detected. Deconvolution of the bulk DNA methylation profiles revealed four methylation components that were associated with the two methylation clusters suggesting cluster-specific cell-type compositions. In conclusion, methylation profiling of HBs reveals 2 distinct subgroups that mainly associate with anatomical location, cytogenetic profiles and differences in cell type composition, potentially reflecting different cells of origin.
Cerebellar degeneration-related protein 1 antisense (CDR1as) is an important member of the circRNAs family, also known as cirs-7. Its main function in vivo is to act as a mir-7 sponge. Accumulated studies show that CDR1as is closely related to various diseases, especially cancer. Our analysis show that CDR1as expression in human cancer is significantly associated with poor overall survival (hazard ratio [HR] = 2.50, 95% confidence interval [CI] = 2.06-3.04; p < 0.00001) and that high CDR1as expression is associated with the tumor node metastasis stage (odds ratio [OR] = 2.13, 95% CI = 1.63-2.78; p < 0.00001), and distant metastasis (OR = 3.50, 95% CI = 1.90-6.64; p < 0.00001). Furthermore, the results reveal the prognostic significance of CDR1as in neoplasms of the digestive system (HR = 1.69, 95% CI = 2.14-2.71; p < 0.001), colorectal cancer (HR = 1.34, 95% CI = 1.96-2.85; p < 0.001), and non-small cell lung cancer (HR = 2.40, 95% CI = 3.42-4.83; p = 0.008). In this study, we summarize in detail the latest research findings and demonstrate the function and regulatory mechanism of CDR1as in various cancer processes, and its potential as a biomarker for cancer prevention and prognosis.
Ten patients with meningeal carcinomatosis associated with nonhaematological neoplasms were examined: six with breast, two with gastrointestinal and one with lung cancer, plus one with a tumour of unknown origin. Cytology was positive in all but one. The patients were classified into four groups according to the gadolinium-enhanced MRI (Gd-MRI) appearances: group 1 had pure leptomeningeal carcinomatosis, group 2 dural carcinomatosis, group 3 spinal leptomeningeal carcinomatosis, and group 4 had normal Gd-MRI except for hydrocephalus. In group 1, Gd-MRI showed diffuse enhancement of the subarachnoid space, including the cisterns around the midbrain, the sylvian fissures, or cerebellar and cerebral sulci. In group 2, Gd-MRI showed diffuse, thick, partially nodular enhancement of the dura mater. No leptomeningeal or subependymal enhancement was evident. In group 3, nodular masses were seen only in the spinal canal. In group 4, no definite evidence of meningeal carcinomatosis was demonstrated on contrast-enhanced CT (CE-CT) or Gd-MRI. The median survival time was 2.0 months in group 1, 1.0 month in group 3, and 4.5 months in group 4, but the two patients in group 2 were alive 10 and 15 months after a definite diagnosis of meningeal carcinomatosis was made. In all patients examined by both CE-CT and Gd-MRI, the latter was superior for identification of meningeal carcinomatosis. Hydrocephalus in an important indirect sign of leptomeningeal carcinomatosis, but was not seen in patients with dural carcinomatosis despite the presence of increased intracranial pressure.
Glioblastoma is the most frequent and aggressive primary brain tumor, characterized by extensive brain invasion and rarely, systemic metastases. The pathogenesis of metastatic glioblastoma is largely unknown. We present the first integrated clinical/histologic/genetic analysis of 5 distinct brain and lung foci from a unique case of recurrent, multifocal, multicentric and metastatic glioblastoma. The initial right frontotemporal gliosarcoma received standard surgical/chemoradiation therapy and recurred 1.5 years later, co-occurring with three additional masses localized to the ipsilateral temporal lobe, cerebellum and lung. Synchronous metastatic lung carcinoma was suspected in this long-term smoker patient with family history of cancer. However, glioblastoma was confirmed in all tumors, although with different morphologic patterns, including ependymomatous and epithelioid. Genomic profiling revealed a germline FANCD2 variant of unknown significance, and a 4-gene somatic mutation signature shared by all tumors, consisting of TERT promoter and PTEN, RB1 and TP53 tumor suppressor mutations. Additional GRIN2A and ATM heterozygous mutations were selected in the cerebellar and lung foci, but were variably present in the supratentorial foci, indicating reduced post-therapeutic genetic evolution in brain foci despite morphologic variability. Significant genetic drift characterized the lung metastasis, likely explaining the known resistance of circulating glioblastoma cells to systemic seeding. MET overexpression was detected in the initial gliosarcoma and lung metastasis, possibly contributing to invasiveness. This comprehensive analysis sheds light on the temporospatial evolution of glioblastoma and underscores the importance of genetic testing for diagnosis and personalized therapy.
Protein-protein interactions integrated with disease-gene associations represent important information for revealing protein functions under disease conditions to improve the prevention, diagnosis, and treatment of complex diseases. Although several studies have attempted to identify disease-gene associations, the number of possible disease-gene associations is very small. High-throughput technologies have been established experimentally to identify the association between genes and diseases. However, these techniques are still quite expensive, time consuming, and even difficult to perform. Thus, based on currently available data and knowledge, computational methods have served as alternatives to provide more possible associations to increase our understanding of disease mechanisms. Here, a new network-based algorithm, namely, Disease-Gene Association (DGA), was developed to calculate the association score of a query gene to a new possible set of diseases. First, a large-scale protein interaction network was constructed, and the relationship between two interacting proteins was calculated with regard to the disease relationship. Novel plausible disease-gene pairs were identified and statistically scored by our algorithm using neighboring protein information. The results yielded high performance for disease-gene prediction, with an F-measure of 0.78 and an AUC of 0.86. To identify promising candidates of disease-gene associations, the association coverage of genes and diseases were calculated and used with the association score to perform gene and disease selection. Based on gene selection, we identified promising pairs that exhibited evidence related to several important diseases, e.g., inflammation, lipid metabolism, inborn errors, xanthomatosis, cerebellar ataxia, cognitive deterioration, malignant neoplasms of the skin and malignant tumors of the cervix. Focusing on disease selection, we identified target genes that were important to blistering skin diseases and muscular dystrophy. In summary, our developed algorithm is simple, efficiently identifies disease-gene associations in the protein-protein interaction network and provides additional knowledge regarding disease-gene associations. This method can be generalized to other association studies to further advance biomedical science.
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