RUNX3 Transcript Variants Have Distinct Roles in Ovarian Carcinoma and Differently Influence Platinum Sensitivity and Angiogenesis
Bibliographic record
Abstract
The prognosis of late-stage epithelial ovarian cancer (EOC) patients is affected by chemotherapy response and the malignant potential of the tumor cells. In earlier work, we identified hypermethylation of the runt-related transcription factor 3 gene (RUNX3) as a prognostic biomarker and contrary functions of transcript variants (TV1 and TV2) in A2780 and SKOV3 cells. The aim of the study was to further validate these results and to increase the knowledge about RUNX3 function in EOC. New RUNX3 overexpression models of high-grade serous ovarian cancer (HGSOC) were established and analyzed for phenotypic (IC50 determination, migration, proliferation and angiogenesis assay, DNA damage analysis) and transcriptomic consequences (NGS) of RUNX3 TV1 and TV2 overexpression. Platinum sensitivity was affected by a specific transcript variant depending on BRCA background. RUNX3 TV2 induced an increased sensitivity in BRCA1wt cells (OVCAR3), whereas TV1 increased the sensitivity and induced a G2/M arrest under treatment in BRCA1mut cells (A13-2-12). These different phenotypes relate to differences in DNA repair: homologous recombination deficient A13-2-12 cells show less γH2AX foci despite higher levels of Pt-DNA adducts. RNA-Seq analyses prove transcript variant and cell-line-specific RUNX3 effects. Pathway analyses revealed another clinically important function of RUNX3—regulation of angiogenesis. This was confirmed by thrombospondin1 analyses, HUVEC spheroid sprouting assays and proteomic profiling. Importantly, conditioned media (CM) from RUNX3 TV1 overexpressing A13-2-12 cells induced an increased HUVEC sprouting. Altogether, the presented data support the hypothesis of different functions of RUNX3 transcript variants related to the clinically relevant processes—platinum resistance and angiogenesis.
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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) | 0.002 | 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".