miRNA-95 Mediates Radioresistance in Tumors by Targeting the Sphingolipid Phosphatase SGPP1
Bibliographic record
Abstract
Radiation resistance poses a major clinical challenge in cancer treatment, but little is known about how microRNA (miR) may regulate this phenomenon. In this study, we used next-generation sequencing to perform an unbiased comparison of miR expression in PC3 prostate cancer cells rendered resistant to fractionated radiation treatment. One miR candidate found to be upregulated by ionizing radiation was miR-95, the enforced expression of which promoted radiation resistance in a variety of cancer cells. miR-95 overexpression recapitulated an aggressive phenotype including increased cellular proliferation, deregulated G2-M checkpoint following ionizing radiation, and increased invasive potential. Using combined in silico prediction and microarray expression analyses, we identified and validated the sphingolipid phosphatase SGPP1, an antagonist of sphingosine-1-phosphate signaling, as a target of miR-95 that promotes radiation resistance. Consistent with this finding, cell treatment with FTY720, a clinically approved small molecule inhibitor of S1P signaling, sensitized miR-95 overexpressing cells to radiation treatment. In vivo assays extended the significance of these results, showing that miR-95 overexpression increased tumor growth and resistance to radiation treatment in tumor xenografts. Furthermore, reduced tumor necrosis and increased cellular proliferation were seen after radiation treatment of miR-95 overexpressing tumors compared with control tumors. Finally, miR-95 expression was increased in human prostate and breast cancer specimens compared with normal tissue. Together, our work reveals miR-95 expression as a critical determinant of radiation resistance in cancer cells.
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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 | 0.001 | 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".