RUNX1T1
Bibliographic record
Abstract
OBJECTIVES: Using gene expression profiling on frozen primary pancreatic endocrine tumors (PETs), we discovered RUNX1T1 as a leading candidate progression gene. This study was designed (1) to validate the differential expression of RUNX1T1 protein on independent test sets of metastatic and nonmetastatic PETs and (2) to determine if RUNX1T1 underexpression in primary tumors was predictive of liver metastases. METHODS: Immunohistochemical expression of RUNX1T1 protein was quantified using Allred scores on archival metastatic (n = 13) and nonmetastatic (n = 24) primary adult PET tissues using custom-designed tissue microarrays. Wilcoxon rank sum/Fisher exact tests and receiver operating characteristic curves were used in the data analysis. RESULTS: Median RUNX1T1 scores were 2 (2-7) and 6 (3-8) in metastatic versus nonmetastatic primaries (P < 0.0001). Eleven of 13 metastatic and 1 of 24 nonmetastatic primaries exhibited RUNX1T1-scores of 4 or less (P < 0.0001). Low RUNX1T1 expression was highly associated with hepatic metastases (P < 0.0001), whereas conventional histological criteria (Ki-67 index, mitotic rate, necrosis) were weakly associated with metastases (P = 0.08-0.15). Considering RUNX1T1 expression (Allred) score of 4 or less to be predictive, the sensitivity to predict hepatic metastases was 85%, with a specificity of 96%. CONCLUSIONS: RUNX1T1 protein is underexpressed in well-differentiated metastatic primary PETs relative to nonmetastatic primaries and emerges as a promising novel biomarker for prediction of liver metastases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".