Gene expression analysis of matched ovarian primary tumors and peritoneal metastasis
Bibliographic record
Abstract
BACKGROUND: Ovarian cancer is the most deadly gynecological cancer due to late diagnosis at advanced stage with major peritoneal involvement. To date most research has focused on primary tumor. However the prognosis is directly related to residual disease at the end of the treatment. Therefore it is mandatory to focus and study the biology of metastatic disease that is most frequently localized to the peritoneal cavity in ovarian cancer. METHODS: We used high-density gene expression arrays to investigate gene expression changes between matched primary and metastatic (peritoneal) lesions. RESULTS: Here we show that gene expression profiles in peritoneal metastasis are significantly different than their matched primary tumor and these changes are affected by underlying copy number variation differences among other causes. We show that differentially expressed genes are enriched in specific pathways including JAK/STAT pathway, cytokine signaling and other immune related pathways. We show that underlying copy number variations significantly affect gene expression. Indeed patients with important differences in copy number variation displayed greater gene expression differences between their primary and matched metastatic lesions. CONCLUSIONS: Our analysis shows a very specific targeting at both the genomic and transcriptomic level to upregulate certain pathways in the peritoneal metastasis of ovarian cancer. Moreover, while primary tumors use certain pathways we identify distinct differences with metastatic lesions. The variation between primary and metastatic lesions should be considered in personalized treatment of ovarian cancer.
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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 | 0.000 | 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".