Cytokine gene expression signature in ovarian clear cell carcinoma
Bibliographic record
Abstract
Cytokine expression in a tumor microenvironment can impact both host defense against the tumor and tumor cell survival. In this study, we sought to clarify whether the cytokine gene expression profile could have clinical associations with ovarian cancer. We analyzed the expression of 16 cytokine genes (IL-1α, IL-1β, IL-2, IL-4, IL-5, IL-8, IL-10, IL-12p35, IL-12p40, IL-15, IFN-γ, TNF-α, IL-6, HLA-DRA, HLA-DPA1 and CSF1) in 50 ovarian carcinomas. Hierarchical clustering analysis of these tumors was carried out using Cluster software and differentially expressed genes were examined between clear cell carcinoma (CCC) and other subtypes. Following this examination we evaluated the biological significance of IL-6 knockdown in CCC. Unsupervised hierarchical clustering analysis of cytokine gene expression revealed two distinct clusters. The relationship between the two clusters and clinical parameters showed statistically significant differences in CCC compared to other histologies. CCC showed a dominant Th-2 cytokine expression pattern driven largely by IL-6 expression. Inhibition of IL-6 in CCC cells suppressed Stat3 signaling and rendered cells sensitive to cytotoxic agents. The unique cytokine expression pattern found in CCC may be involved in the pathogenesis of this subtype. In particular, high IL-6 expression appears likely to be driven by the tumor cells, fueling an autocrine pathway involving IL-6 expression and Stat3 activation and may influence survival when exposed to cytotoxic chemotherapy. Modulation of IL-6 expression or its related signaling pathway may be a promising strategy of treatment for CCC.
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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".