Immunohistochemical Expression and Cluster Analysis of Mesenchymal and Neural Stem Cell–Associated Proteins in Pediatric Soft Tissue Sarcomas
Bibliographic record
Abstract
Pediatric undifferentiated soft tissue sarcomas (USTSs) are a group of malignancies composed predominantly of primitive round cell sarcomas, the histogenesis of which is uncertain. Thus, diagnosis and therapy remain a challenge. The aims of the current study were to determine whether differential expression of stem cell-associated proteins could be used to aid in determining the histogenesis of pediatric USTSs and to determine whether pediatric USTSs expressed a unique panel of stem cell-associated proteins to aid diagnosis. Tumors included 28 Ewing sarcoma/primitive neuroectodermal tumors (ESs), 22 embryonal rhabdomyosarcomas (ERMSs), 8 alveolar rhabdomyosarcomas (ARMSs), 5 synovial sarcomas (SSs), 5 malignant peripheral nerve sheath tumors (MPNSTs), and 13 USTSs. Stem cell antibodies included 3 mesenchymal stem cell markers (CD44, CD105, and CD166) and 5 neural stem cell markers (CD15, CD29, CD56, CD133, and nestin). Sections were scored followed by statistical analysis, clustering analysis, and visualizations using Partek Genomic Suite Software. The Euclidean clustering divided the tumors into 2 major groups. ESs and USTSs formed the majority of the 1st group, whereas ERMSs, ARMSs, MPNSTs, and SSs formed the 2nd group. Reduced expression of CD56 was strongly associated with the ES/USTS cluster (P < 0.0001). ESs and USTSs were further separated by CD166 staining, wherein increased expression was associated with ES (P < 0.0001). The 2nd group included the majority of other sarcomas, with no consistent separation between subtypes. The current study demonstrates the usefulness of applying stem cell markers to pediatric sarcomas and indicates that USTSs and ESs are closely related and may share a common histogenesis.
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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 | 0.001 |
| 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".