High-throughput 3D spheroid screens identify microRNA sensitizers for improved thermoradiotherapy in locally advanced cancers
Bibliographic record
Abstract
Chemoradiotherapy is the standard of care for many locally advanced cancers, including cervical and head and neck cancers, but many patients cannot tolerate chemotherapy. Clinical trials have shown that radiotherapy combined with hyperthermia (thermoradiotherapy) may be equally effective, yet it yields a suboptimal overall survival of patients, emphasizing the need for improvement. MicroRNAs (miRNAs), short non-coding RNA sequences, are often dysregulated in cancer and exhibit significant potential as radiosensitizers by targeting genes associated with the DNA damage response. In this study, high-throughput miRNA screening of four cervical cancer cell lines identified 55 miRNAs with significant sensitizing potential, with 18 validated across 10 additional cancer cell lines (6 cervical and 4 head and neck). Functional studies of 6 miRNAs, including miR-16, miR-27a, miR-181c, miR-221, miR-224, and miR-1293, showed that they reduced DNA damage repair by downregulating ATM, DNA-PKcs, Ku70/80, and RAD51. Additionally, differential expression of miR-27a, miR-221, and miR-224 in treatment-sensitive versus treatment-resistant patients indicated their predictive biomarker potential for treatment response of cervical cancer patients. Conclusively, this study has identified 18 promising miRNAs for the development of sensitizers for thermoradiotherapy and may provide potential biomarkers for predicting treatment response in locally advanced cancers.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".