Distinguishing Tumor and Stromal Sources of MicroRNAs Linked to Metastasis in Cutaneous Melanoma
Bibliographic record
Abstract
MicroRNA (miRNA) dysregulation in cancer causes changes in gene expression programs regulating tumor progression and metastasis. Candidate metastasis suppressor miRNA are often identified by differential expression in primary tumors compared to metastases. Here, we performed comprehensive analysis of miRNA expression in The Cancer Genome Atlas (TCGA) skin cutaneous melanoma (SKCM) tumors (97 primary, 350 metastatic), and identified candidate metastasis-suppressor miRNAs. Differential expression analysis revealed miRNA significantly downregulated in metastatic tumors, including miR-205, miR-203, miR-200a-c, and miR-141. Furthermore, sequential feature selection and classification analysis identified miR-205 and miR-203 as the miRNA best able to discriminate between primary and metastatic tumors. However, cell-type enrichment analysis revealed that gene expression signatures for epithelial cells, including keratinocytes and sebocytes, were present in primary tumors and significantly correlated with expression of the candidate metastasis-suppressor miRNA. Examination of miRNA expression in cell lines revealed that candidate metastasis-suppressor miRNA identified in the SKCM tumors, were largely absent in melanoma cells or melanocytes, and highly restricted to keratinocytes and other epithelial cell types. Indeed, the differences in stromal cell composition between primary and metastatic tumor tissues is the main basis for identification of differential miRNA that were previously classified as metastasis-suppressor miRNAs. We conclude that future studies must consider tumor-intrinsic and stromal sources of miRNA in their workflow to identify bone fide metastasis-suppressor miRNA in cutaneous melanoma and other cancers.
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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".