The application of an extracellular vesicle-based biosensor in early diagnosis and prediction of chemoresponsiveness in ovarian cancer
Bibliographic record
Abstract
ABSTRACT Ovarian cancer (OVCA) is the most fatal gynecological cancer with late diagnosis and chemoresistance being the main obstacles of treatment success. Since there is no reliable approach to diagnosing patients at an early stage as well as predicting chemoresponsiveness, there is the urgent need to develop a diagnostic platform for such purposes. Extracellular vesicles (EVs) present as an attractive biomarker given their potential specificity and sensitivity to tumor sites. We have developed a novel sensor which utilizes cysteine functionalized gold nanoparticles to simultaneously bind to cisplatin (CDDP) and EVs affording us the advantage of predicting OVCA chemoresponsiveness, histologic subtypes, and early diagnosis using surface enhanced Raman spectroscopy. EVs were isolated and characterized from chemosensitive and resistant OVCA cells lines as well as pre-operative patient blood samples. The mechanistic role of plasma gelsolin (pGSN) in EV-mediated CDDP secretion in OVCA chemoresistance was investigated using standard cellular and molecular techniques. We determined that chemoresistant cells secrete significantly higher levels of small EVs (sEVs) and EVs containing CDDP (sEV-CDDP) compared with their sensitive counterparts. pGSN interacted with cortactin (CTTN) and both markers were significantly upregulated in chemoresistant patients’ tumors compared with the sensitive patients. Silencing pGSN decreased EV and EV-CDDP secretions in the resistant cells whereas its over-expression in sensitive cells upregulated EV and EV-CDDP secretion, suggesting the potential role of pGSN in EV-mediated CDDP export. sEV/CA125 ratio outperformed CA125 and sEV individually in predicting early stage, chemoresistance, residual disease, tumor recurrence, and patient survival. These findings highlight pGSN as a potential therapeutic target as well as providing a potential diagnostic platform to detect OVCA earlier and predict chemoresistance; an intervention that will positively impact patients’ survival.
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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 | 0.001 | 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".