MicroRNA Expression in Squamous Cell Carcinoma and Adenocarcinoma of the Esophagus: Associations with Survival
Bibliographic record
Abstract
PURPOSE: The dismal outcome of esophageal cancer patients highlights the need for novel prognostic biomarkers, such as microRNAs (miRNA). Although recent studies have established the role of miRNAs in esophageal carcinoma, a comprehensive multicenter study investigating different histologic types, including squamous cell carcinoma (SCC) and adenocarcinoma with or without Barrett's, is still lacking. EXPERIMENTAL DESIGN: miRNA expression was measured in cancerous and adjacent noncancerous tissue pairs collected from 100 adenocarcinoma and 70 SCC patients enrolled at four clinical centers from the United States, Canada, and Japan. Microarray-based expression was measured in a subset of samples in two cohorts and was validated in all available samples. RESULTS: In adenocarcinoma patients, miR-21, miR-223, miR-192, and miR-194 expression was elevated, whereas miR-203 expression was reduced in cancerous compared with noncancerous tissue. In SCC patients, we found elevated miR-21 and reduced miR-375 expression levels in cancerous compared with noncancerous tissue. When comparing cancerous tissue expression between adenocarcinoma and SCC patients, miR-194 and miR-375 were elevated in adenocarcinoma patients. Significantly, elevated miR-21 expression in noncancerous tissue of SCC patients and reduced levels of miR-375 in cancerous tissue of adenocarcinoma patients with Barrett's were strongly associated with worse prognosis. Associations with prognosis were independent of tumor stage or nodal status, cohort type, and chemoradiation therapy. CONCLUSIONS: Our multicenter-based results highlight miRNAs involved in major histologic types of esophageal carcinoma and uncover significant associations with prognosis. Elucidating miRNAs relevant to esophageal carcinogenesis is potentially clinically useful for developing prognostic biomarkers and identifying novel drug targets and therapies.
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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 | 0.001 | 0.001 |
| 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".