UCSD GBM Data Set
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Chen Clark
PROVIDER: S-ECPF-GEOD-60184 | biostudies-other |
REPOSITORIES: biostudies-other
Similar Datasets
Project description:Total RNA microarray data from Fresh-Frozen Glioblastoma tumor samples. Untreated, newly diagnosed patients.
2014-08-08 | E-GEOD-60184 | biostudies-arrayexpress
Project description:Total RNA microarray data from Fresh-Frozen Glioblastoma tumor samples.
2014-08-08 | GSE60184 | GEO
Project description:BACKGROUND: Most patients affected by Glioblastoma multiforme (GBM, grade IV glioma) experience a recurrence of the disease because of the spreading of tumor cells beyond surgical boundaries. Unveiling mechanisms causing this process is a logic goal to impair the killing capacity of GBM cells by molecular targeting.We noticed that our long-term GBM cultures, established from different patients, may display two categories/types of growth behavior in an orthotopic xenograft model: expansion of the tumor mass and formation of tumor branches/nodules (nodular like, NL-type) or highly diffuse single tumor cell infiltration (HD-type). METHODS: We determined by DNA microarrays the gene expression profiles of three NL-type and three HD-type long-term GBM cultures. Subsequently, individual genes with different expression levels between the two groups were identified using Significance Analysis of Microarrays (SAM). Real time RT-PCR, immunofluorescence and immunoblot analyses, were performed for a selected subgroup of regulated gene products to confirm the results obtained by the expression analysis. RESULTS: Here, we report the identification of a set of 34 differentially expressed genes in the two types of GBM cultures. Twenty-three of these genes encode for proteins localized to the plasma membrane and 9 of these for proteins are involved in the process of cell adhesion. CONCLUSIONS: This study suggests the participation in the diffuse infiltrative/invasive process of GBM cells within the CNS of a novel set of genes coding for membrane-associated proteins, which should be thus susceptible to an inhibition strategy by specific targeting.Massimiliano Monticone and Antonio Daga contributed equally to this work.
| S-EPMC3502598 | biostudies-literature
Project description:Glioblastoma is the most aggressive primary brain tumor in adults and due to the invasive nature it cannot be completely removed. We have recently shown that the WNT inhibitory factor 1 (WIF1), a secreted inhibitor of WNTs, is downregulated in glioblastoma and acts as strong tumor suppressor. In search of a mediator for this function differential gene expression profiles of WIF1-expressing cells were performed. MALAT1, a long non-coding RNA and key positive regulator of invasion, emerged as the top downregulated gene. Indeed, knock-down of MALAT1 reduced migration in glioblastoma cells, without effect on proliferation. LN-229 cells induced with Doxocyclin to express WIF1 were compared to the non-induced control (two biological replicates each)
| S-ECPF-GEOD-58525 | biostudies-other
Project description:We introduce a simple MODelability Index (MODI) that estimates the feasibility of obtaining predictive QSAR models (correct classification rate above 0.7) for a binary data set of bioactive compounds. MODI is defined as an activity class-weighted ratio of the number of nearest-neighbor pairs of compounds with the same activity class versus the total number of pairs. The MODI values were calculated for more than 100 data sets, and the threshold of 0.65 was found to separate the nonmodelable and modelable data sets.
| S-EPMC3984298 | biostudies-other
Project description:There is a growing body of knowledge on network intrusion detection, and several open data sets with network traffic and cyber-security threats have been released in the past decades. However, many data sets have aged, were not collected in a contemporary industrial communication system, or do not easily support research focusing on distributed anomaly detection. This paper presents the Westermo network traffic data set, 1.8 million network packets recorded in over 90 minutes in a network built up of twelve hardware devices. In addition to the raw data in PCAP format, the data set also contains pre-processed data in the form of network flows in CSV files. This data set can support the research community for topics such as intrusion detection, anomaly detection, misconfiguration detection, distributed or federated artificial intelligence, and attack classification. In particular, we aim to use the data set to continue work on resource-constrained distributed artificial intelligence in edge devices. The data set contains six types of events: harmless SSH, bad SSH, misconfigured IP address, duplicated IP address, port scan, and man in the middle attack.
| S-EPMC10482735 | biostudies-literature
Project description:The use of wireless signals for the purposes of localization enables a host of applications relating to the determination and verification of the positions of network participants ranging from radar to satellite navigation. Consequently, this has been a longstanding interest of theoretical and practical research in mobile networks and many solutions have been proposed in the scientific literature. However, it is hard to assess the performance of these in the real world and, more importantly, to compare their advantages and disadvantages in a controlled scientific manner. With this work, we attempt to improve the current state of art methodology in localization research and to place it on a solid scientific grounding for future investigations. Concretely, we developed LocaRDS, an open reference data set of real-world crowdsourced flight data featuring more than 222 million measurements from over 50 million transmissions recorded by 323 sensors. We demonstrate how we can verify the quality of LocaRDS measurements so that it can be used to test, analyze and directly compare different localization methods. Finally, we provide an example implementation for the aircraft localization problem and a discussion of possible metrics for use with LocaRDS.
| S-EPMC8402290 | biostudies-literature
Project description:Rapid Sequencing of 100kb input S cerevisiae DNA
| PRJEB31782 | ENA
Project description:Sex differences in the incidence and outcome of human disease are broadly recognized but, in most cases, not sufficiently understood to enable sex-specific approaches to treatment. Glioblastoma (GBM), the most common malignant brain tumor, provides a case in point. Despite well-established differences in incidence and emerging indications of differences in outcome, there are few insights that distinguish male and female GBM at the molecular level or allow specific targeting of these biological differences. Here, using a quantitative imaging-based measure of response, we found that standard therapy is more effective in female compared with male patients with GBM. We then applied a computational algorithm to linked GBM transcriptome and outcome data and identified sex-specific molecular subtypes of GBM in which cell cycle and integrin signaling are the critical determinants of survival for male and female patients, respectively. The clinical relevance of cell cycle and integrin signaling pathway signatures was further established through correlations between gene expression and in vitro chemotherapy sensitivity in a panel of male and female patient-derived GBM cell lines. Together, these results suggest that greater precision in GBM molecular subtyping can be achieved through sex-specific analyses and that improved outcomes for all patients might be accomplished by tailoring treatment to sex differences in molecular mechanisms.
| S-EPMC6502224 | biostudies-literature