Does Immunohistochemistry for Discovered on GIST1 and Minichromosome Maintenance Protein7 Provide Additional Clinicopathological Value in Gastrointestinal Stromal Tumors?
Bibliographic record
Abstract
BACKGROUND: The aim of the study was to investigate the expression of discovered on GIST 1 (DOG1) and minichromosome maintenance protein 7 (MCM7) in addition to the traditional markers, C-KIT and Ki-67, in gastrointestinal stromal tumors (GISTs) to specify the diagnosis and to evaluate their clinicopathological significance in GIST patients. METHODS: Hematoxylin and eosin sections of 43 GISTs were re-examined to review histopathological criteria and risk stratification of these tumors. Immunohistochemistry for DOG1, C-KIT, MCM7, Ki-67 antibodies was performed. RESULTS: Positive DOG1 and C-KIT expressions were found in 42 (97.7%) and 39 (90.7%) of cases, respectively. DOG1 and C-KIT expression scores were significantly correlated (P < 0.001). Among four C-KIT-negative GISTs, three cases were DOG1-positive. DOG1 was more sensitive and specific than C-KIT in the diagnosis of GISTs. High DOG1 expression scores were significantly associated with tumor size (P = 0.023) and risk (P = 0.037). Significant positive correlation was noted between MCM7 and Ki-67 labeling indices (LIs) (P < 0.001, r = 0.885). MCM7 demonstrated higher proliferation LIs than Ki-67. Significant associations were found between MCM7 and Ki-67 LIs and tumor size (P = 0.001 and 0.003 respectively), mitotic rate (P < 0.001 both) and risk stratification (P < 0.001 both) with a stepwise increase in MCM7 LIs with increasing tumor risk. CONCLUSION: DOG1 is an important diagnostic tool for GISTs particularly in C-KIT-negative tumors. It may have a role in GISTs tumorogenesis and progression. Despite the established clinicopathological value of Ki-67 in GISTs, detection of MCM7 expression is recommended as a prognostic adjunct, given its better sensitivity for cellular proliferation and stepwise association with tumor risk.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".