Berberine Inhibits Breast Cancer Stem Cell Development and Decreases Inflammation: Involvement of miRNAs and IL-6
Bibliographic record
Abstract
Background: Breast cancer (BC) is a health concern worldwide and is often accompanied by depressive symptoms in patients. In BC, elevated interleukin-6 (IL-6) levels contribute to an inflammatory signature linked to cancer stem cell (CSC) stemness and depressive behaviors. Bioactive food components, such as berberine (BBR), have preventative effects against BC by targeting CSCs. Objectives: This study aimed to investigate the effects of BBR on breast CSC proliferation, on levels of specific micro (mi)RNAs and IL-6 in vitro and in vivo, and in alleviating depressive-like behaviors in mice with BC. Methods: Mammosphere formation assays were conducted by treating murine 4T1 and human MDA-MB-231 BC cell lines with BBR. qPCR analysis of miRNAs miR-let-7c and miR-34a-5p was performed on 4T1 CSCs exposed to BBR. BBR was administered orally to female BALB/c, followed by injection with mammary carcinoma cells to induce BC. Behavioral tests were conducted to assess depressive-like behaviors. Tumor tissues were collected for ex vivo mammosphere assays, miRNA expression analysis, and IL-6 detection by ELISA. Serum was also collected for IL-6 analysis. Results: > 0.05). However, no significant differences were observed in depressive-like behaviors between control and treatment groups. Conclusions: BBR may have the potential to be used as an "Epi-Natural Compound" to prevent cancer by reducing inflammation and affecting epigenetics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".