An Epigenetic Genome-Wide Screen Identifies <i>SPINT2</i> as a Novel Tumor Suppressor Gene in Pediatric Medulloblastoma
Bibliographic record
Abstract
Medulloblastoma (MB) is a malignant cerebellar tumor that occurs primarily in children. The hepatocyte growth factor (HGF)/MET pathway has an established role in both normal cerebellar development as well as the development and progression of human brain tumors, including MB. To identify novel tumor suppressor genes involved in MB pathogenesis, we performed an epigenome-wide screen in MB cell lines, using 5-aza-2'deoxycytidine to identify genes aberrantly silenced by promoter hypermethylation. Using this technique, we identified an inhibitor of HGF/MET signaling, serine protease inhibitor kunitz-type 2 (SPINT2/HAI-2), as a putative tumor suppressor silenced by promoter methylation in MB. In addition, based on single nucleotide polymorphism array analysis in primary MB samples, we identified hemizygous deletions targeting the SPINT2 locus in addition to gains on chromosome 7 encompassing the HGF and MET loci. SPINT2 gene expression was down-regulated and MET expression was up-regulated in 73.2% and 45.5% of tumors, respectively, by quantitative real-time PCR. SPINT2 promoter methylation was detected in 34.3% of primary MBs examined by methylation-specific PCR. SPINT2 reexpression in MB cell lines reduced proliferative capacity, anchorage independent growth, cell motility in vitro, and increased overall survival times in vivo in a xenograft model (P<0.0001). Taken together, these data support the role of SPINT2 as a putative tumor suppressor gene in MB, and further implicate dysregulation of the HGF/MET signaling pathway in the pathogenesis of MB.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".