Role of Epiregulin on Lipopolysaccharide-Induced Hepatocarcinogenesis as a Mediator via EGFR Signaling in the Cancer Microenvironment
Bibliographic record
Abstract
Lipopolysaccharides (LPSs) have been reported to be important factors in promoting the progression of hepatocellular carcinoma (HCC), but the corresponding molecular mechanisms remain to be elucidated. We hypothesize that epiregulin (EREG), an epidermal growth factor (EGF) family member derived from hepatic stellate cells (HSCs) and activated by LPS stimulation, is a crucial mediator of HCC progression with epidermal growth factor receptor (EGFR) expression in the tumor microenvironment. We used a mouse xenograft model of Huh7 cells mixed with half the number of LX-2 cells, with/without intraperitoneal LPS injection, to elucidate the role of EREG in LPS-induced HCC. In the mouse model, LPS administration significantly enlarged the size of xenografted tumors and elevated the expression of EREG in tumor tissues compared with those in negative controls. Moreover, CD34 immunostaining and the gene expressions of angiogenic markers by a reverse transcription polymerase chain reaction revealed higher vascularization, with increased interleukin-8 (IL-8) expression in the tumors of the mice group treated with LPS compared to those without LPS. Our data collectively suggested that EREG plays an important role in the cancer microenvironment under the influence of LPS to increase not only the tumor cell growth and migration/invasion of EGFR-positive HCC cells but also tumor neovascularization via IL-8 signaling.
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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".