RNA biomarkers from proximal liquid biopsy for diagnosis of ovarian cancer
Bibliographic record
Abstract
BACKGROUND: Most ovarian cancer patients are diagnosed at an advanced stage and have a high mortality rate. Current screening strategies fail to improve prognosis because markers that are sensitive for early stage disease are lacking. This medical need justifies the search for novel approaches using utero-tubal lavage as a proximal liquid biopsy. METHODS: In this study, we explore the extracellular transcriptome of utero-tubal lavage fluid obtained from 26 ovarian cancer patients and 48 controls using messenger RNA (mRNA) capture and small RNA sequencing. RESULTS: We observed an enrichment of ovarian and fallopian tube specific messenger RNAs in utero-tubal lavage fluid compared to other human biofluids. Over 300 mRNAs and 41 miRNAs were upregulated in ovarian cancer samples compared with controls. Upregulated genes were enriched for genes involved in cell cycle activation and proliferation, hinting at a tumor-derived signal. CONCLUSION: This is a proof-of-principle that mRNA capture sequencing of utero-tubal lavage fluid is technically feasible, and that the extracellular transcriptome of utero-tubal lavage should be further explored in larger cohorts to assess the diagnostic value of the biomarkers identified in this study. IMPACT: Proximal liquid biopsy from the gynecologic tract is a promising source for mRNA and miRNA biomarkers for diagnosis of early-stage 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".