Glioportal: a comprehensive transcriptomic resource unveiling ligand-mediated mesenchymal transition in glioblastoma
Bibliographic record
Abstract
BACKGROUND: Multi-omics profiling of glioblastoma (GBM) has unraveled two aspects fundamental to its aggressiveness and lethality that is molecular heterogeneity inherent to the tumor and cellular plasticity modulated by the microenvironment. Yet, empirical validation to identify causal factors for these complex mechanisms is rather scarce. Here, we report our endeavor in establishing Glioportal, a GBM tumor biobank with derivative preclinical models and molecular information that we leverage for basic and translational research on precision therapies. METHODS: Bulk transcriptome and single-cell-based deconvolution analyses highlighted key features of distinct GBM subtypes and ligand-receptor pairs predicted to regulate malignant cell state plasticity. Synthetic genetic tracing tool and target genes/proteins expression analyses validated ligands-induced mesenchymal transition. This was further corroborated with phenotypic invasion/migration assays and cell-based assays using inhibitors, functional antibodies, and gene silencing approaches. A proof-of-concept animal experiment was conducted using orthotopic xenograft carrying gene knockdown. Clinical relevance was assessed through immunohistochemical assay. RESULTS: Our transcriptomic analysis highlights the integral roles of STAT3 and NF-κB pathways in maintaining intrinsic mesenchymal identity and enabling myeloid-induced plasticity towards mesenchymal phenotype. One critical ligand, TNF, confers mesenchymal adaptation and cellular invasiveness that is mitigated by TNFRSF1A, but not TNFRSF1B, loss of function. TNFRSF1A silencing significantly improves survival in vivo. CONCLUSION: Glioportal makes a valuable resource for identifying therapeutic vulnerabilities in molecularly stratified GBM. Here, we underscore GBM dependency on myeloid-derived ligands to acquire mesenchymal traits that have clinical implications in therapeutic response and recurrence. Such reliance warrants treatment strategies targeting ligand-receptor pairs to mitigate interactions with the tumor ecosystem.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".