Transcriptomic profiling of organoids derived from malignant effusions uncovers lncRNA MEG3 and target genes potentially involved in platinum resistance in serous ovarian carcinoma
Bibliographic record
Abstract
Serous ovarian carcinoma (SOC) is an aggressive disease, characterized by advanced-stage tumors that are often associated with relapse and poor outcomes. Although platinum-based chemotherapy is a cornerstone of the treatment, most of the relapsed tumors become resistant to these agents. We explored organoids derived from SOC malignant effusions to identify targets actionable by epigenetic drugs (epi-drugs) to enhance platinum response. Tumor-derived organoids (TDOs) were established using malignant effusions of SOC patients. Histological and transcriptomic (RNA-Seq) characterization (18 TDOs versus 7 normal ovarian samples) was performed, followed by cross-validation with external RNA-Seq datasets (337 SOC samples, 4 TDOs, and 180 normal tissues). Predicted interactions between long noncoding RNAs (lncRNAs) and epigenetic effectors were investigated. We selected the epi-drugs decitabine and tazemetostat, whose targets were overexpressed in SOC, to treat carboplatin-resistant SOC cell lines and TDOs. Subsequently, these models were challenged with carboplatin. Twelve lncRNAs and 168 protein-coding genes differentially expressed were involved in epigenetic regulation. Abnormal expression levels of lncRNA MEG3 and epigenetic effectors DNMT3B and EZH2 were confirmed in external datasets. Increased carboplatin sensitivity and MEG3 upregulation were observed after treating the cell lines and TDOs with epi-drugs. Altogether, our findings provide novel insights into using organoids derived from malignant effusions as preclinical models and hint at potential targets for overcoming platinum resistance in SOC.
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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".