Naringenin suppresses epithelial ovarian cancer by inhibiting proliferation and modulating gut microbiota
Bibliographic record
Abstract
BACKGROUND: Ovarian cancer has the highest mortality among all gynecological malignancies; currently, no effective therapeutics are available for its treatment. Naringenin has been shown to inhibit the progression of various cancers, but its inhibitory effect on ovarian cancer remains unknown. PURPOSE: This study aimed to evaluate the inhibitory effects of naringenin on ovarian cancer and elucidate the underlying mechanisms. METHODS: Cancer cell proliferation was detected by cell counting kit-8 and crystal violet assays, and the migration capability was determined by wound healing and transwell assays. Western blotting and immunohistochemistry assays were employed to determine the expression levels of the epidermal growth factor receptor, phosphatidylinositol 3-kinase (PI3K) and cyclin D1 in vitro and in vivo, respectively. An ES-2 xenograft nude mouse model was established for the in vivo experiments, and fecal samples were collected for intestinal microbiota analysis by 16S rDNA sequencing. RESULTS: Naringenin suppressed the proliferation and migration of A2780 and ES-2 cancer cell lines and downregulated PI3K in vitro. In animal experiments, naringenin treatment significantly decreased the tumor weight and volume, and oral administration exhibited greater effects than intraperitoneal injection. Additionally, naringenin treatment ameliorated the population composition of the microbiota in animals with ovarian cancer and significantly increased the abundances of Alistipes and Lactobacillus. CONCLUSION: Naringenin suppresses epithelial ovarian cancer by inhibiting PI3K pathway expression and ameliorating the gut microbiota, and the oral route is more effective than parenteral administration.
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) | 0.001 | 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".