E‐cadherin regulates mitochondrial metabolism and induces cell growth through NF‐κB in E‐cadherin deficient AGS cells
Bibliographic record
Abstract
In this study the interrogation of Wnt regulator β‐catenin and an emerging cancer control molecule NF‐κB was explored in E‐cadherin expressed AGS gastric cancer cells (EC96 cells). It was found that E‐cadherin can increase cell proliferation and energy metabolism through activation of NF‐κB. EC96 cells treated with NF‐κB inhibitors showed a decrease in cell survival. Knockdown of E‐cadherin expression by shRNA could clearly reverse the E‐cadherin‐induced energy metabolism potential in EC96 cells. The application of NF‐kB inhibitors supported the evidence that NF‐kB act as an upstream signal of c‐myc and Glut1. Normally NF‐kB is present in the cytosol of resting cells. Our results showed that E‐cadherin expression led NF‐κB to translocate from cytosol to nucleus in AGS cells. The treatment of NF‐kB inhibitors or knockdown with E‐cadherin shRNA abolished the ability of E‐cadherin to translocate NF‐kB into nucleus in EC96 cells, which was accompanied with the reduction of cell growth. In conclusion, E‐cadherin expression generates cell proliferation signals through NF‐κB signal pathway as Wnt signal pathway was suppressed. Support or Funding Information [This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF‐2015R1D1A1A09056775)]
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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) | 0.002 | 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".