Application of micro-RNA (miRNA) expression profiles for prognostication in low-risk endometrial carcinoma (LEC).
Bibliographic record
Abstract
5032 Background: Reliably predicting which LEC patients are most likely to recur is a challenge for the clinician with implications on adjuvant therapy. MiRNAs have been exploited for diagnosis and prognostication in a number of malignancies. We hypothesize that miRNA expression profiles differ in tumors from patients with recurrence compared to those without recurrence. Methods: The inclusion criteria for this study are informed consent, stage 1 disease, grade 1 or 2 tumors and endometrioid histology. RNA was extracted from formalin-fixed paraffin-embedded tissues and miRNA profiling was done using Agilent Human miRNA. Differentially expressed miRNAs were identified using GeneSpring GX software and the two groups were compared using the student t-test. Results: The expression levels of 866 miRNAs were determined from LEC patients with recurrence (n=15) and without recurrence (n=16). The mean follow-up interval was 61.5 months. The average age of cancer diagnosis for patients with and without recurrence was 60.2 (range 42-75) and 59.7 (range 44-86), respectively (p=0.91). Three of 15 patients with recurrence and 6 of 16 patients without recurrence received adjuvant brachytherapy following their primary surgery (p=0.43). 17 miRNAs were identified which can distinguish between the tumors with recurrence and those without recurrence (p<0.05). MiR-146a, miR-18a, miR-222, and miR-30a showed the highest fold change difference (>5 fold) in the tumors with recurrence compared to that did not recur. A decision tree prediction model for recurrent LEC was developed where a miRNA cutoff was used as a branch in the decision tree. This model identified those patients who were most likely to recur based on the expression of 4 dysregulated miRNAs (miR-222, miR-361-3p, miR-181c and miR-125b). Conclusions: These preliminary results show the miRNA expression profile differs among LEC and can be used to distinguish an aggressive sub-group. Should future validation studies confirm this result, this information would be valuable in the design of a biomarker study to help decide which patients would benefit most from extended adjuvant treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".