Comprehensive MicroRNA Profiling for Head and Neck Squamous Cell Carcinomas
Bibliographic record
Abstract
PURPOSE: The objective of this study is to investigate the significance of microRNAs (miRNA) in patients with locally advanced head and neck squamous cell carcinoma (HNSCC). EXPERIMENTAL DESIGN: A global miRNA profiling was done on 51 formalin-fixed archival HNSCC samples using quantitative reverse transcription-PCR approach, correlated with patients' clinical parameters. Functional characterization of HNSCC-associated miRNAs was conducted on three HNSCC cell lines. Cell viability and proliferation were investigated using MTS and clonogenic assays, respectively; cell cycle analyses were assessed using flow cytometry. RESULTS: Thirty-eight of the 117 (33%) consistently detected miRNAs were significantly differentially expressed between malignant versus normal tissues. Concordant with previous reports, overexpression of miR-21, miR-155, let-7i, and miR-142-3p and underexpression of miR-125b and miR-375 were detected. Upregulation of miR-423, miR-106b, miR-20a, and miR-16 as well as downregulation of miR-10a were newly observed. Exogenous overexpression of miR-375 in HNSCC cell lines reduced proliferation and clonogenicity and increased cells in sub-G(1). Similar cellular effects were observed in knockdown studies of the miR-106b-25 cluster but with accumulation of cells in G(1) arrest. No major difference was detected in miRNA profiles among laryngeal, oropharyngeal, or hypopharyngeal cancers. miR-451 was found to be the only significantly overexpressed miRNA by 4.7-fold between nonrelapsed and relapsed patients. CONCLUSION: We have identified a group of aberrantly expressed miRNAs in HNSCC and showed that underexpression of miR-375 and overexpression of miR-106b-25 cluster might play oncogenic roles in this disease. Further detailed examinations of miRNAs will provide opportunities to dissect the complex molecular abnormalities driving HNSCC progression.
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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".