Signatures of EMT, immunosuppression, and inflammation in primary and recurrent human cutaneous squamous cell carcinoma at single-cell resolution
Bibliographic record
Abstract
Abstract The recurrence of cutaneous squamous cell carcinoma (cSCC) after surgery remains a key factor affecting cSCC outcomes, which is related to the reprogramming of the tumour microenvironment (TME). Herein, we utilized single-cell RNA sequencing (scRNA-seq) to examine the dynamic changes in epithelial cells, T cells, myeloid cells and fibroblasts between primary and recurrent cSCC. We uncovered the immunosuppressed microenvironment in recurrent cSCC, which exhibited a T-cell- excluded and SPP1 + TAM-enriched status. In recurrent cSCC, CD8 + T cells showed high exhaustion and low inflammatory features, while SPP1 + TAMs displayed global protumour characteristics, including decreased phagocytosis and inflammation as well as increased angiogenesis. Furthermore, we found that the subgroups of SPP1 + tumour- associated macrophages (TAMs) harboured distinct functions. SPP1 + CD209 high TAMs showed obvious features of phagocytosis, while SPP1 + CD209 low TAMs tended to have a high angiogenic ability. A subpopulation of tumour-specific keratinocytes (TSKs) showed significant epithelial–mesenchymal transition (EMT) features in recurrent cSCC, which might be due to their active communication with IL7R + cancer-associated fibroblasts (CAFs). In addition, we found that MDK could provoke different cell–cell interactions in cSCCs with distinctive staging. In primary cSCC, MDK was highly expressed in fibroblasts and could promote their proliferation and block the migration of tumour cells, while in recurrent cSCC, the high expression of MDK in TSKs promotes their proliferation and metastasis. Overall, our study provides insights into the critical mechanisms of cSCC progression, which might facilitate the development of a powerful system for the prevention and treatment of cSCC recurrence.
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.
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 | 0.000 | 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) | 0.001 | 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".