microRNA Profile Associated with Positive Lymph Node Metastasis in Early-Stage Cervical Cancer
Bibliographic record
Abstract
Lymph node metastasis (LNM) is an important prognostic factor in cervical cancer (CC). In early stages, the risk of LNM is approximately 3.7 to 21.7%, and the 5-year overall survival decreases from 80% to 53% when metastatic disease is identified in the lymph nodes. Few reports have analyzed the relationship between miRNA expression and the presence of LNM. The aim of this study was to identify a subset of miRNAs related to LNM in early-stage CC patients. Formalin-fixed paraffin-embedded tissue blocks were collected from patients with early-stage CC treated by radical hysterectomy with lymphadenectomy. We analyzed samples from two groups of patients—one group with LNM and the other without LNM. Global miRNA expression was identified by microarray analysis, and cluster analysis was used to determine a subset of miRNAs associated with LNM. Microarray expression profiling identified a subset of 36 differentially expressed miRNAs in the two groups (fold change (FC) ≥ 1.5 and p < 0.01). We validated the expression of seven miRNAs; miR-487b, miR-29b-2-5p, and miR-195 were underexpressed, and miR-92b-5p, miR-483-5p, miR-4534, and miR-548ac were overexpressed according to the microarray experiments. This signature exhibited prognostic value for identifying early-stage CC patients with LNM. These findings may help detect LNM that cannot be observed in imaging studies.
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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) | 0.001 | 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".