PXD071225 is an
original dataset announced via ProteomeXchange.
Dataset Summary
| Title | Cellular senescence escape and antiviral response discriminate glioblastoma from lower grade gliomas – a bioinformatic meta-analysis |
| Description | Cellular senescence plays a negative prognostic role in glioblastoma (GB). However, the specific cell types with senescent features and molecular mechanisms by which these senescent cells contribute to the pathogenesis of GB are unknown. To identify specific senescent cell subpopulations in GB and their functional impact on surrounding cells, we performed bioinformatic analyses of publicly available omics datasets, specifically transcriptome, single-cell transcriptome, and proteome data, obtained directly from patients with GB. Markers of cellular senescence were defined based on publicly available transcriptome data comparing senescent and proliferating cells. They were subsequently used to verify the presence of senescent cells in GB datasets. By applying this systematically curated set of common senescence markers together with the expression of senescence-associated secretory phenotype (SASP), extracellular matrix (ECM) components, and cyclin-dependent kinase inhibitors, we identified GB-associated senescent cells in clusters phenotypically corresponding to subpopulations of the developing brain, including radial glia, endothelial cells, and immature astrocytes. We characterized the communication network of senescent cells with other cells in the tumor tissue, showing that senescent cells communicate primarily with tumor-associated macrophages to suppress immune response. Notably, processes that distinguish GB from less malignant types of gliomas include cellular senescence and pathways associated with antiviral response. To investigate the effect of chemotherapy on the development of secondary therapy-induced senescence, we performed a proteomic analysis of an in vitro 3D model of GB cell spheroids induced to senescence by temozolomide. Results indicate that primary and secondary senescence in GB shares similar phenotypic features. Pathways that are associated with unique GB aggressiveness and therapy resistance are upregulated after therapy, which can promote even more aggressive behavior of the recurrent tumor. The data underscores that senescent cells contribute significantly to the malignant manifestations of GB, supporting the emerging idea that senescent cells are a promising target for adjuvant senolytic therapy of GB. |
| HostingRepository | PRIDE |
| AnnounceDate | 2026-06-08 |
| AnnouncementXML | Submission_2026-06-07_18:13:26.523.xml |
| DigitalObjectIdentifier | |
| ReviewLevel | Peer-reviewed dataset |
| DatasetOrigin | Original dataset |
| RepositorySupport | Unsupported dataset by repository |
| PrimarySubmitter | Michaela Sadibolova |
| SpeciesList | scientific name: Homo sapiens (Human); NCBI TaxID: NEWT:9606; |
| ModificationList | TMT6plex-126 reporter+balance reagent acylated residue; methylthiolated residue |
| Instrument | Orbitrap Exploris 480 |
Dataset History
| Revision | Datetime | Status | ChangeLog Entry |
| 0 | 2025-11-25 15:47:36 | ID requested | |
| ⏵ 1 | 2026-06-07 18:13:27 | announced | |
Publication List
| Maurencova D, Sadibolova M, Zarska M, Liblova Z, Novak J, Kroupova J, Vasicova P, Bartek J, Hodny Z, Cellular senescence escape and antiviral response discriminate glioblastoma from lower-grade gliomas. Neurooncol Adv, 8(1):vdag122(2026) [pubmed] |
| 10.1093/noajnl/vdag122; |
Keyword List
Contact List
| Zdenek Hodny |
| contact affiliation | Institute of Molecular Genetics of the Czech Academy of Sciences |
| contact email | zdenek.hodny@img.cas.cz |
| lab head | |
| Michaela Sadibolova |
| contact affiliation | University Hospital Hradec Kralove |
| contact email | michaela.sadibolova@gmail.com |
| dataset submitter | |
Full Dataset Link List
Dataset FTP location
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| PRIDE project URI |
Repository Record List
[ + ]
[ - ]
- PRIDE
- PXD071225
- Label: PRIDE project
- Name: Cellular senescence escape and antiviral response discriminate glioblastoma from lower grade gliomas – a bioinformatic meta-analysis