MiR-126 in Hepatocellular Carcinoma and Cholangiocellular Carcinoma: A Reappraisal with an <i>in situ</i> Detection of miR-126.
Bibliographic record
Abstract
OBJECTIVE: Hepatocellular carcinoma (HCC) and cholangiocellular carcinoma (CCA) represent the most common malignant tumors of the liver. MicroRNAs (miRNAs) are small non-coding RNAs that play a role in regulating gene expression post-transcriptionally. The altered expression of miRNAs has been observed in many malignancies, including liver cancer. However, the expression level of miR-126 in HCC and CCA and its role in carcinogenesis show debatable data currently. METHODS: In this study, we investigate the expression level, localization, and biological significance of miRNA-126 in HCC and CCA. RESULTS: hybridization analysis showed a significant reduction in miR-126 levels in HCC and CCA tissues relative to their corresponding healthy tissues. Conversely, miR-126 was expressed in the normal hepatocytes, blood vessels, and sinusoidal cells. Also, we detected a low expression level of miR-126 in HCC and CCA cell lines. The overexpression of miR-126 in HepG2 and HuCCT1 using miRNA mimics significantly inhibited cell proliferation, growth, and migration. CONCLUSION: This study further suggests that miR-126 plays a critical role in hepatic carcinogenesis. In our opinion, miR-126 warrants further investigations because this marker may have both diagnostic and prognostic implications in hepatocarcinogenesis.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".