Differential Expression of Proteins and Genes at the Tumor‐Brain Interface in Invasive Meningioma
Bibliographic record
Abstract
Most meningiomas are dural-based extra-axial tumors in close contact with the brain. Expression of genes and proteins at the tumor-brain interface in brain-invasive meningioma is basically unknown. Using the NanoString pan-cancer panel, differential expression of genes in the invasive edge versus main tumor body was determined in 12 invasive meningiomas (comprising the discovery cohort), and 6 candidate genes: DTX1, RASGRF1, GRIN1, TNR, IL6, and NR4A1, were identified. By immunohistochemistry, DTX1 and RASGRF1 expression correlated with gene expression, and were studied in an expanded cohort of 21 invasive and 15 noninvasive meningiomas, together with Ki-67. Significantly higher expression of DTX1, RASGFR1, and Ki-67 was found in the invasive edge compared with the main tumor body. Increased expression of RASGRF1 and Ki-67 was more clearly associated with brain invasion. The situation with DTX1 was less definitive since increased expression was observed in meningiomas both at the invasive edge and when in close contact with brain but without invasion. Pathway analyses identified significant links between DTX1 and RASGRF1 and key biological processes, including cell-cell adhesion, and signaling pathways including Notch, RAS, MAPK, and Rho. Higher expression of DTX1, RASGRF1, and Ki-67 in the brain-invasive area of meningiomas suggests that these proteins play a role in the process of brain invasion.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".