Mesothelioma cell heterogeneity identified by single cell RNA sequencing
Bibliographic record
Abstract
Mesothelioma cell heterogeneity encompasses diverse morphological and molecular characteristics observed within tumors, significantly impacting disease progression, treatment outcomes, and the development of targeted therapies. This heterogeneity has long posed challenges for accurate diagnosis and effective treatment, but understanding its complexities offers the potential for novel diagnostic modalities and therapeutic interventions. This study employed single-cell RNA sequencing (scRNA-seq) to investigate mesothelioma cell heterogeneity from various sources, including cell culture (CC), peritoneal lavage (Lav) from the tumor microenvironment, and circulating tumor cells (CTC) in murine models. Gene set enrichment analysis was used to identify distinct gene signatures for each subpopulation. The results revealed unique characteristics for mesothelioma cells depending on their origin. In the CC group, up-regulated genes were primarily involved in tumor cell cycle control, proliferation, and apoptosis. In the CTC group, up-regulated genes were associated with cancer cell stemness. The Lav group showed up-regulated genes facilitating interactions between tumor cells and the microenvironment, such as epithelial-mesenchymal transition and immune responses mediated by IFN-α and IFN-γ. Some pathways were shared among all tumor cells, suggesting the potential for transitioning between functional states under specific conditions. This may be the first study to explore circulating mesothelioma cell heterogeneity using scRNA-seq. The distinct gene signatures identified in each mesothelioma cell subpopulation likely play critical roles in tumor initiation and progression, offering potential novel targets for therapeutic intervention. These findings could help inform the development of more effective, personalized treatments for mesothelioma, ultimately improving patient outcomes.
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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 | 0.001 | 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".