Transcriptome modulation following administration of luteolin to bleomycin‐etoposide‐cisplatin chemotherapy on rat LC540 tumor Leydig cells
Bibliographic record
Abstract
Leydig cell tumours represent 1%-3% of all cases of testicular tumours in men. Such tumours respond poorly to radiation or chemotherapy, including bleomycin-etoposide-cisplatin (BEP) combinatorial therapy. In this study, we investigated an alternative approach involving luteolin to improve the efficacy of chemotherapy. LC540 tumour Leydig cells were treated with BEP (bleomycin 40 µg/ml, etoposide 4 µg/ml, cisplatin 8 µg/ml) and/or luteolin 10 µM for comparison with DMSO-treated cells. We performed a transcriptome analysis using RNA-Seq to characterise changes in biological processes and signalling pathways. Treatments of LC540 tumour Leydig cells with luteolin significantly decreased the expression of genes involved in cholesterol biosynthesis, while increasing the expression of genes related to glutathione conjugation (p < .05). Genes being significantly upregulated in response to BEP treatment were involved in the response to toxic substances and transcriptional regulation. Oppositely, genes being significantly downregulated by BEP treatment were enriched for intracellular signal transduction, cell migration, cell adhesion, reproductive system development and cholesterol biosynthesis. BEP chemotherapy proved to be effective in increasing gene expression related to apoptosis of tumour Leydig cells. However, addition of luteolin to BEP treatment had no other effects on biological processes or pathways related to cancer treatment.
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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 | 0.000 | 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".