Prognostic significance of promyelocytic leukemia expression in gastrointestinal stromal tumor; integrated proteomic and transcriptomic analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it