Claudin 1 Mediates TNFα-Induced Gene Expression and Cell Migration in Human Lung Carcinoma Cells
Bibliographic record
Abstract
Epithelial-mesenchymal transition (EMT) is an important mechanism in carcinogenesis. To determine the mechanisms that are involved in the regulation of EMT, it is crucial to develop new biomarkers and therapeutic targets towards cancers. In this study, when TGFβ1 and TNFα were used to induce EMT in human lung carcinoma A549 cells, we found an increase in an epithelial cell tight junction marker, Claudin 1. We further identified that it was the TNFα and not the TGFβ1 that induced the fibroblast-like morphology changes. TNFα also caused the increase in Claudin-1 gene expression and protein levels in Triton X-100 soluble cytoplasm fraction. Down-regulation of Claudin-1, using small interfering RNA (siRNA), inhibited 75% of TNFα-induced gene expression changes. Claudin-1 siRNA effectively blocked TNFα-induced molecular functional networks related to inflammation and cell movement. Claudin-1 siRNA was able to significantly reduce TNF-enhanced cell migration and fibroblast-like morphology. Furthermore, over expression of Claudin 1 with a Claudin 1-pcDNA3.1/V5-His vector enhanced cell migration. In conclusion, these observations indicate that Claudin 1 acts as a critical signal mediator in TNFα-induced gene expression and cell migration in human lung cancer cells. Further analyses of these cellular processes may be helpful in developing novel therapeutic strategies.
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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 | 0.001 |
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".