NDRG1 deficiency is associated with regional metastasis in oral cancer by inducing epithelial–mesenchymal transition
Bibliographic record
Abstract
Regional metastasis is the single most important prognostic factor in oral squamous cell carcinoma (OSCC). Abnormal expression of N-myc downstream-regulated genes (NDRGs) has been identified to occur in several tumor types and to predict poor prognosis. In OSCC, the clinical significance of deregulated NDRG expression has not been fully established. In this study, NDRG1 relevance was assessed at gene and protein levels in 100 OSCC patients followed up by at least 10 years. Survival outcome was analyzed using a multivariable analysis. Tumor progression and metastasis was investigated in preclinical model using oral cancer cell lines (HSC3 and SCC25) treated with epidermal growth factor (EGF) and orthotopic mouse model of metastatic murine OSCC (AT84). We identified NDRG1 expression levels to be significantly lower in patients with metastatic tumors compared with patients with local disease only (P = 0.001). NDRG1 expression was associated with MMP-2, -9, -10 (P = 0.022, P = 0.002, P = 0.042, respectively) and BCL2 (P = 0.035). NDRG1 lower expression was able to predict recurrence and metastasis (log-rank test, P = 0.001). In multivariable analysis, the expression of NDRG1 was an independent prognostic factor (Cox regression, P = 0.013). In invasive OSCC cells, NDRG1 expression is diminished in response to EGF and this was associated with a potent induction of epithelial-mesenchymal transition phenotype. This result was further confirmed in an orthotopic OSCC mouse model. Together, this data support that NDRG1 downregulation is a potential predictor of metastasis and approaches aimed at NDRG1 signaling rescue can serve as potential therapeutic strategy to prevent oral cancer progression to metastasis.
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".