Tumor burden and cisplatin treatment alters the expression levels of microRNA-146a and -155 in spleen and cancer cells in an experimental mouse model of Ehrlich ascite carcinoma
Bibliographic record
Abstract
Background: MicroRNAs (miRNAs) are small noncoding RNAs that typically inhibit translation and stability of messengerRNAs (mRNAs) and as such control expression of genes involved in different cellular processes. Several miRNAs have beenlinked to cancer and immune cells but whether there is a correlation between their expressions in both cells is still indistinct. Aim: In this study, we aimed to analyze miRNAs in tumor and immune cells in tumor bearing mice treated with or withoutchemotherapy. Methods: CD1 mice were inoculated with intraperitoneal (i.p.) injection of 106 viable cells from Ehrlich ascetic carcinoma (EAC)cell line to form ascities. Mice were then i.p. treated with PBS ascontrol or with cispaltin (10 or 40 μg/mouse). Semiquantificationof miRNAs 146a and 155 in spleen and tumor cells was done after 1 or 2 weeks of treatments using RT-PCR. Results: The transcription level of miRNA-146a decreased in EAC and spleen cells, while miRNA-155 expression level increased.In EAC-bearing mice treated with CIS for 1 week, miRNA-155 expression level considerably increased in spleen and tumor cellsas compared to tumor-non-bearing and untreated tumor-bearing mice. In contrast, the miRNA-146a transcription level decreasedin both spleen and tumor cells as compared to its expression level in naive and untreated EAC- bearing mice. Two weeks postCIS treatment of EAC-bearing mice, spleen cells still show low expression levels of miRNA-146a, while theexpression levels ofmiRNA-155 increased in both spleen and tumor cells. Taken together, these results suggest that miRNA expression is significantlyaltered by tumor progression and by chemotherapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".