Spatial colocalization and molecular crosstalk of myofibroblastic CAFs and tumor cells shape lymph node metastasis in oral squamous cell carcinoma
Bibliographic record
Abstract
Lymph node metastasis (LNM) is a critical prognostic factor for patients with oral squamous cell carcinoma (OSCC). Previous research has implicated the partial epithelial-to-mesenchymal transition of tumor cells and myofibroblastic cancer-associated fibroblasts (myCAFs) in the LNM process. However, the underlying molecular mechanisms remain poorly understood. Here, we conducted a comprehensive molecular analysis integrating original and publicly available OSCC data from bulk genome and transcriptome, single-cell transcriptome, and spatial transcriptome analyses. We found that myCAFs were quantitatively and functionally activated in LNM-positive samples and spatially colocalized with OSCC cells within the invasive tumor front (ITF), providing a niche that may facilitate LNM. Immunohistochemical validation in 90 ITF samples confirmed significantly higher myCAF density in LNM-positive samples than in LNM-negative samples, and this density remained an independent predictor of LNM when adjusted for pathological grade and the pattern of invasion. In LNM-positive samples, myCAFs provided increased extracellular matrix (ECM) signals, upregulating stemness-related genes such as CD44 in OSCC cells. The functional importance of this myCAF-driven ECM-CD44 axis was further supported by our validation analysis of expanded, publicly available spatial transcriptome and experimental in vitro coculture data. We also extracted a spatially resolved, 23-gene signature from the metastatic ITF where OSCC and myCAFs colocalize. This signature predicted LNM status and poor overall survival in patients with OSCC. Our findings provide novel insight into the molecular myCAF/OSCC crosstalk that facilitates LNM and identify potential prognostic biomarkers and therapeutic targets for patients with OSCC.
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 | 0.001 | 0.001 |
| 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".