Unravelling the role of extracellular vesicles in cervical cancer: Mechanisms of progression, resistance, and emerging therapeutic strategies
Bibliographic record
Abstract
• Cervical cancer EVs export oncomiRs to support tumor growth. • Tumor-suppressor miRNAs (miR-142-3p, miR-100, miR-122-5p) are expelled via EVs. • miR-142-3p in EVs promotes angiogenesis, highlighting bevacizumab as a target. • High miR-21 promotes immune evasion; pembrolizumab may restore immunity. • EV miRNA targeting offers strategies to combat chemotherapy resistance. Cervical cancer remains a significant global health challenge, particularly in its advanced stages, where treatment resistance complicates effective management. Extracellular vesicles (EVs) are crucial mediators of tumor progression and resistance, primarily through the transfer of miRNA cargo. In cervical cancer, specific miRNAs, including oncogenic miRNAs such as miR-21, miR-221-3p, miR-486-5p, and miR-92a-3p are upregulated in both cells and EVs, promoting proliferation, migration, epithelial-to-mesenchymal transition (EMT), and immune evasion—all of which contribute to therapy resistance and an aggressive tumor phenotype. Conversely, tumor-suppressive miRNAs, such as miR-122-5p, miR-100, and miR-142-3p, are selectively exported from cancer cells via EVs, suggesting a protective mechanism by which cancer cells eliminate these tumor suppressors. This review focuses on the role of oncogenic and tumor-suppressive miRNAs within EVs and their implications for cervical cancer progression and treatment resistance. Additionally, it examines the dynamic interactions between the tumor microenvironment (TME) and EV cargo, as well as emerging EV-based therapeutic strategies. These include the encapsulation of chemotherapeutic agents within EVs, the use of anti-miRs to silence oncogenic miRNAs, the delivery of tumor-suppressive miRNAs, the inhibition of EV release, and the targeting of downstream miRNA-regulated proteins. While miRNA-based therapies remain in the early stages, they hold significant promise for overcoming treatment resistance and improving cervical cancer outcomes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".