Tissue microRNA expression profiling in hepatic and pulmonary metastatic melanoma
Bibliographic record
Abstract
Malignant melanoma has a propensity for the development of hepatic and pulmonary metastases. MicroRNAs (miRs) are small, noncoding RNA molecules containing about 22 nucleotides that mediate protein expression and can contribute to cancer progression. We aim to identify clinically useful differences in miR expression in metastatic melanoma tissue. RNA was extracted from formalin-fixed, paraffin-embedded samples of hepatic and pulmonary metastatic melanoma, benign, nevi, and primary cutaneous melanoma. Assessment of miR expression was performed on purified RNA using the NanoString nCounter miRNA assay. miRs with greater than twofold change in expression when compared to other tumor sites (P value ≤ 0.05, modified t-test) were identified as dysregulated. Common gene targets were then identified among dysregulated miRs unique to each metastatic site. Melanoma metastatic to the liver had differential expression of 26 miRs compared to benign nevi and 16 miRs compared to primary melanoma (P < 0.048). Melanoma metastatic to the lung had differential expression of 19 miRs compared to benign nevi and 10 miRs compared to primary melanoma (P < 0.024). Compared to lung metastases, liver metastases had greater than twofold upregulation of four miRs, and 4.2-fold downregulation of miR-200c-3p (P < 0.0081). These findings indicate that sites of metastatic melanoma have unique miR profiles that may contribute to their development and localization. Further investigation of the utility of these miRs as diagnostic and prognostic biomarkers and their impact on the development of metastatic melanoma is warranted.
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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 | 0.000 |
| 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".