The Dynamically Evolving Cell States and Ecosystem from Benign Nevi to Melanoma
Bibliographic record
Abstract
PURPOSE: Approximately 30% of nonchronically sun-damaged melanomas originate from nevi, yet the dynamic changes and crucial mechanisms driving the transition from benign nevi to melanoma remain elusive. EXPERIMENTAL DESIGN: In this study, we performed single-cell transcriptome sequencing on multiple paired tissue sites from five patients diagnosed with melanoma arising in congenital melanocytic nevi, identifying four distinct states of melanocyte subpopulations during the progression from nevi to melanoma, characterized by dynamic changes in their functions and regulatory pathways. RESULTS: In the nevi state, IFN regulatory factor 1 was specifically upregulated in melanocytes, fibroblasts, and endothelial cells, potentially activating immune surveillance in the microenvironment. Conversely, the critical inhibitory checkpoint HLA-E for NK cells exhibited high expression in a cluster of malignant melanocytes and fibroblasts enriched in melanoma. This interaction with ligands expressed in NK cells could potentially serve as a key factor, leading to immune evasion. In malignant melanoma samples, we detected high expression of midkine in melanocytes. It is a pivotal factor that facilitates melanoma invasion and malignant transformation, potentially through interaction with endothelial cells to stimulate angiogenesis. The targets identified in our study are crucial factors in detecting the malignant transformation of nevi. Ultimately, we developed a malignant progression model capable of predicting patient prognosis and malignant progression status using bulk RNA sequencing data. CONCLUSIONS: Our study provides a high-resolution atlas of the malignant transformation of melanoma from nevi and highlights potential targets for further investigation.
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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".