Four <scp>PTEN</scp>‐targeting co‐expressed mi<scp>RNA</scp>s and <scp>ACTN</scp>4‐ targeting mi<scp>R</scp>‐548b are independent prognostic biomarkers in human squamous cell carcinoma of the oral tongue
Bibliographic record
Abstract
The purpose of this study was to determine the prognostic value and oncogenic pathways associated to miRNA expression in squamous cell carcinoma of the oral tongue and to link these miRNA candidates with potential gene targets. We performed a miRNA screening within our institutional cohort (n = 58 patients) and reported five prognostic targets including a cluster of four co-expressed miRNAs (miR-18a, miR-92a, miR-103, and miR-205). Multivariate analysis showed that expression of miR-548b (p = 0.007) and miR-18a (p = 0.004, representative of co-expressed miRNAs) are independent prognostic markers for squamous cell carcinoma of the oral tongue. These findings were validated in The Cancer Genome Atlas (TCGA) cohort (n = 131) for both miRNAs (miR-548b: p = 0.027; miR-18a: p = 0.001). Bioinformatics analysis identified PTEN and ACTN4 as direct targets of the four co-expressed miRNAs and miR-548b, respectively. Correlations between the five identified miRNAs and their respective targeted genes were validated in the two merged cohorts and were concordantly significant (miR-18a/PTEN: p < 0.0001; miR-92a/PTEN: p = 0.0008; miR-103/PTEN: p = 0.008; miR-203/PTEN: p = 0.019; miR-548b/ACTN4: p = 0.009).
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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 | 0.001 |
| 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 | 0.001 | 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".