Ergosterol Peroxide Isolated from Ganoderma lucidum Abolishes MicroRNA miR-378-Mediated Tumor Cells on Chemoresistance
Bibliographic record
Abstract
Due to an altered expression of oncogenic factors and tumor suppressors, aggressive cancer cells have an intrinsic or acquired resistance to chemotherapeutic agents. This typically contributes to cancer recurrence after chemotherapy. microRNAs are short non-coding RNAs that are involved in both cell self-renewal and cancer development. Here we report that tumor cells transfected with miR-378 acquired properties of aggressive cancer cells. Overexpression of miR-378 enhanced both cell survival and colony formation, and contributed to multiple drug resistance. Higher concentrations of chemotherapeutic drugs were needed to induce death of miR-378-transfected cells than to induce death of control cells. We found that the biologically active component isolated from Ganoderma lucidum could overcome the drug-resistance conferred by miR-378. We purified and identified the biologically active component of Ganoderma lucidum as ergosterol peroxide. We demonstrated that ergosterol peroxide produced greater activity in inducing death of miR-378 cells than the GFP cells. Lower concentrations of ergosterol peroxide were needed to induce death of the miR-378-transfected cells than in the control cells. With further clinical development, ergosterol peroxide represents a promising new reagent that can overcome the drug-resistance of tumor 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) | 0.001 | 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 | 0.000 | 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".