Tissue microRNA expression profiling in hepatic and pulmonary metastatic melanoma
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it