Single-cell RNA-Seq reveals the heterogeneity of fibroblasts within the tympanojugular paraganglioma microenvironment
Bibliographic record
Abstract
Tympanojugular paragangliomas (TJP) originate from the parasympathetic ganglia in the lateral base of the skull. Although the cellular composition and oncogenic mechanisms of paragangliomas have been evaluated, a comprehensive transcriptomic atlas specific to TJP remains to be established to facilitate further investigations. In this study, single-cell RNA sequencing and whole-exome sequencing were conducted on six surgically excised TJP samples to determine their cellular composition and intratumoral heterogeneity. Fibroblasts were sub-classified into two distinct groups: myofibroblasts and fibroblasts associated with bone remodeling. Additionally, an elaborate regulatory and cell-cell communication network was determined, highlighting the multifaceted role of fibroblasts, which varies depending on expression transitions. The Kit receptor (KIT) signaling pathway mediated interactions between fibroblasts and mast cells, whereas robust connections with endothelial and Schwann cell-like cells were facilitated through the platelet-derived growth factor signaling pathway. These findings establish a foundation for studying the mechanisms underlying protumor angiogenesis and the specific contributions of fibroblasts within the TJP microenvironment. IL6 signaling pathway of fibroblasts interacting with macrophages and endothelial cells may be involved in tumor regrowth. These results enhance our understanding of fibroblast functionality and provide a resource for future therapeutic targeting of TJP.
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".