Prognostic significance of promyelocytic leukemia expression in gastrointestinal stromal tumor; integrated proteomic and transcriptomic analysis
Bibliographic record
Abstract
Prognostic markers are urgently needed to optimize the postoperative treatment strategies for gastrointestinal stromal tumors (GIST). GIST of the small intestine (I-GIST) show more aggressive behavior than those of the stomach (S-GIST), and the molecular background of the malignancy in I-GIST may include potential prognostic biomarkers. We conducted integrated proteomic and transcriptomic analysis to identify genes showing differential expressions according to the tumor site. We generated protein expression profiles for four cases each of surgically resected I-GIST and S-GIST using label-free proteomic analysis. For proteins showing differential expressions, global mRNA expression was compared between 9 I-GIST and 23 S-GIST. Among the 2555 genes analyzed, we found that promyelocytic leukemia (PML), a tumor suppressor gene, was significantly downregulated in I-GIST at both the protein and mRNA levels (P < 0.01; fold difference ≥2.0). Immunohistochemistry of 254 additional cases from multiple clinical facilities showed that PML-negative cases were significantly frequent in the I-GIST group (P < 0.001). The 5-year recurrence-free survival rate was significantly lower in the PML-negative than in the PML-positive cases (60.1% vs 91.7%; P < 0.001). Multivariate analysis revealed that downregulation of PML was an independent unfavorable prognostic factor (hazard ratio = 2.739; P = 0.001). Our study indicated that prognostication based on PML expression may have potential for optimizing the treatment strategy for GIST patients. Further validation studies of PML for clinical application, and investigation for the mechanistic significance of PML to clarify the molecular backgrounds of malignancy in GIST are warranted.
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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) | 0.000 | 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".