The role of visfatin and resistin in an in vitro model of obesity-induced invasive liver cancer
Bibliographic record
Abstract
Obesity is associated with the development of liver disease and its progression to hepatocellular carcinoma. This link may be attributed to adipocytokines such as visfatin and resistin which have been shown to promote liver cancer incidence and progression. Studies have yet to determine the role of visfatin and resistin in liver cancer, specifically in the context of obesity. The objective of this study was to investigate the effect of neutralizing visfatin and resistin in obese (OB) or normal weight (NW) sera to determine the contribution of these proteins in obesity-induced invasive liver cancer. Sera from OB or NW males was used to determine the efficacy of neutralizing visfatin and resistin to reduce the obesity-induced liver cancer phenotype. HepG2 and SNU-449 cells were exposed to OB and NW sera ± antibodies for visfatin or resistin. The neutralizing antibodies differentially suppressed invasion, reactive oxygen species production, and matrix metalloproteinase-9 secretion. These changes corresponded with a decrease in phosphorylated extracellular signal-regulated kinases and protein kinase B in HepG2 cells, but differences were not observed in CAP1 or β-catenin. In conclusion, visfatin and resistin have differential roles in obesity-associated liver cancer and may be potential targets to reverse the impact of obesity on liver cancer progression.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".