Molecular Classification Based on the Gene Expression Profiles in Canine Histiocytic Sarcoma Cells
Bibliographic record
Abstract
The molecular abnormalities of canine histiocytic sarcoma (CHS) remain to be elucidated. We previously revealed that the sensitivities to dasatinib and trametinib were significantly various among CHS cell lines, indicating the differences in underlying molecular abnormalities. In the present study, we performed RNA sequencing analysis using 11 CHS cell lines to investigate molecular classifications based on the gene expression profiles (GEPs). The clustering analysis showed that CHS cell lines were divided into two distinct clusters. The comparisons of GEPs between the clusters extracted 675 differentially expressed genes (DEGs), and these DEGs were enriched with those related to the regulations of inflammatory responses. Among these DEGs, differences in the expressions of CCL3, CCL4, CCL7, CLEC7A, and TLR4 genes between the two groups were confirmed by RT-qPCR. Since no significant difference in the activation status of Akt and ERK pathways was observed between the two groups, the NF-κB pathway was focused on and its activation status was examined in the cell lines. As a result, cell lines belonging to one cluster showed nuclear translocation of the p65 protein together with increased release of CCL5 protein, which is a target molecule of the NF-κB pathway, in a cell culture supernatant. These results suggested that the molecular pathology of CHS cells might be divided into two categories depending on the activation status of the NF-κB pathway, and it is necessary to establish precision medicine for each molecular subtype of CHS.
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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) | 0.000 | 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".