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Glioma stem cells (GSCs) exhibit diverse molecular subtypes with the mesenchymal (MES) population representing the most malignant variant. The oncogenic potential of Salmonella pathogenicity island 1 (SPI1), an oncogenic transcription factor, has been established across various human malignancies. In this study, we explored the association between the SPI1 pathway and the MES GSC phenotype. Through comprehensive analysis of the Cancer Genome Atlas and Chinese Glioma Genome Atlas glioma databases, along with patient-derived GSC cultures, we analyzed SPI1 expression. Using genetic knockdown and overexpression techniques, we assessed the functional impact of SPI1 on GSC MES marker expression, invasion, proliferation, self-renewal, and sensitivity to radiation in vitro, as well as its influence on tumor formation in vivo. Additionally, we investigated the downstream signaling cascades activated by SPI1. Our findings revealed a positive correlation between elevated SPI1 expression and the MES phenotype, which in turn, correlated with poor survival. SPI1 enhanced GSC MES differentiation, self-renewal, and radioresistance in vitro, promoting tumorigenicity in vivo. Mechanistically, SPI1 augmented the transcriptional activity of both TGF-β1 and FKBP12 while activating the non-canonical PI3K/Akt pathway. Notably, inhibition of TGF-β1/PI3K/Akt signaling partially attenuated SPI1-induced GSC MES differentiation and its associated malignant phenotype. Collectively, our results underscore SPI1's role in activating TGF-β1/PI3K/Akt signaling through transcriptional upregulation of FKBP12, thereby supporting the aggressive MES phenotype of GSCs. Therefore, SPI1 emerges as a potential therapeutic target in glioma treatment.
Pubmed ID: 37865975
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Project exploring the spectrum of genomic changes involved in more than 20 types of human cancer that provides a platform for researchers to search, download, and analyze data sets generated. As a pilot project it confirmed that an atlas of changes could be created for specific cancer types. It also showed that a national network of research and technology teams working on distinct but related projects could pool the results of their efforts, create an economy of scale and develop an infrastructure for making the data publicly accessible. Its success committed resources to collect and characterize more than 20 additional tumor types. Components of the TCGA Research Network: * Biospecimen Core Resource (BCR); Tissue samples are carefully cataloged, processed, checked for quality and stored, complete with important medical information about the patient. * Genome Characterization Centers (GCCs); Several technologies will be used to analyze genomic changes involved in cancer. The genomic changes that are identified will be further studied by the Genome Sequencing Centers. * Genome Sequencing Centers (GSCs); High-throughput Genome Sequencing Centers will identify the changes in DNA sequences that are associated with specific types of cancer. * Proteome Characterization Centers (PCCs); The centers, a component of NCI's Clinical Proteomic Tumor Analysis Consortium, will ascertain and analyze the total proteomic content of a subset of TCGA samples. * Data Coordinating Center (DCC); The information that is generated by TCGA will be centrally managed at the DCC and entered into the TCGA Data Portal and Cancer Genomics Hub as it becomes available. Centralization of data facilitates data transfer between the network and the research community, and makes data analysis more efficient. The DCC manages the TCGA Data Portal. * Cancer Genomics Hub (CGHub); Lower level sequence data will be deposited into a secure repository. This database stores cancer genome sequences and alignments. * Genome Data Analysis Centers (GDACs) - Immense amounts of data from array and second-generation sequencing technologies must be integrated across thousands of samples. These centers will provide novel informatics tools to the entire research community to facilitate broader use of TCGA data. TCGA is actively developing a network of collaborators who are able to provide samples that are collected retrospectively (tissues that had already been collected and stored) or prospectively (tissues that will be collected in the future).
View all literature mentionsSoftware package for interpreting gene expression data. Used for interpretation of a large-scale experiment by identifying pathways and processes.
View all literature mentionsCommercial organism provider selling mice, rats and other model animals. American corporation specializing in a variety of pre-clinical and clinical laboratory services for the pharmaceutical, medical device and biotechnology industries. It also supplies assorted biomedical products and research and development outsourcing services for use in the pharmaceutical industry. (Wikipedia)
View all literature mentionsProteintech Europe Ltd is an ISO 9001:2008 certified company
View all literature mentionsWeb server for cancer and normal gene expression profiling and interactive analyses. Interactive web server for analyzing RNA sequencing expression data of tumors and normal samples from TCGA and GTEx projects, using standard processing pipeline. Provides customizable functions such as tumor or normal differential expression analysis, profiling according to cancer types or pathological stages, patient survival analysis, similar gene detection, correlation analysis and dimensionality reduction analysis.
View all literature mentionsWeb application for data storage and analysis to explore brain tumors datasets from Chinese cohorts. Data portal for storage and interactive exploration of multi-dimensional functional genomic data that includes primary and recurrent glioma samples from Chinese cohorts. Allows users to browse DNA mutation profile, mRNA/microRNA expression profile and methylation profile, and to do correlation and survival analysis in specific glioma subtype.
View all literature mentionsMus musculus with name BALB/cAnN-Foxn1nu/nu/Rj from IMSR.
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