Additional file 9 of Identification and validation of pyroptosis-related gene landscape in prognosis and immunotherapy of ovarian cancer
Bibliographic record
Abstract
Additional file 9: Table S1. Basic characteristics of datasets enrolled in this study for identifying pyroptosis-associated genes (PYAGs) signatures. Table S2. Demographic and clinical characteristics of 1334 ovarian cancer patients from TCGA-OV and five GEO cohorts. Table S3. Summary of 51 recognized PYAGs. Table S4. Summary of DNA methylation modifications of 51 PYAGs with DiseaseMeth 2.0. Table S5. The prognostic values of 49 PYAGs in OC patients of TCGA-OV and five GEO cohorts. Table S6. Spearman correlation analysis of the 49 PYAGs in OC. Table S7. The activation states of biological pathways in distinct pyroptosis-associated clusters (PACs) by GSVA enrichment analysis. Table S8. The TME score and tumor purity of OC samples were analyzed with ESTIMATETable S9. Functional annotation of the differentially expressed genes（DEGs） between the two PACs.. Table S9. Functional annotation of the differentially expressed genes（DEGs） between the two PACs.Table S9. Functional annotation of the differentially expressed genes（DEGs） between the two PACs. Table S9. Functional annotation of the differentially expressed genes（DEGs） between the two PACs. Table S10. Prognostic analysis of 889 DEGs using a univariate Cox analysis. Table S11. Distribution of PYAG scores in the different pyroptosis subtypes (Kruskal-Wallis H test, P<0.01). Table S12. Relationships between the expressions of CD8, GSDMD, GZMB and clinicopathological parameters of 65 ovarian cancer patients. Table S13. Correlation between Pyrsig score and expressions of GSDMD, GZMB in 65 ovarian cancer patients.
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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 | 0.002 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.800 | 0.122 |
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".