Lactotransferrin: A candidate tumor suppressor—Deficient expression in human nasopharyngeal carcinoma and inhibition of NPC cell proliferation by modulating the mitogen‐activated protein kinase pathway
Bibliographic record
Abstract
Lactotransferrin (LTF) has been shown to regulate tumorogenesis. However, little is known about the role of LTF in regulating the development of human nasopharyngeal carcinoma (NPC). The aim of our study was to investigate whether LTF could regulate the development of NPC by characterizing the pattern of LTF expression in human NPC tissues using cDNA and tissue microarrays. Loss of LTF expression was observed in a significantly higher frequency of NPC tissues compared to that in nontumor nasopharyngeal epithelial tissues. While 61.25% of NPC tissues at the T1/T2 stage were positive for LTF expression, only 40.82% of NPC at the T3/T4 stage were stained by anti-LTF. Similarly, 41.58% of NPC with local lymph node metastasis displayed LTF expression, a value significantly lower than the 46.36% in primary tumors (p < 0.05). These findings suggest that LTF may negatively regulate the development and metastasis of NPC in vivo. Furthermore, overexpression of or treatment with LTF inhibited the proliferation of NPC cells and promoted cell cycle arrest at the G(0)/G(1) phase in vitro. While LTF treatment downregulated expression of cyclin D1 and phosphorylation of retinoblastoma protein (Rb), expression of p21 and p27 in 5-8F NPC cells was enhanced. Moreover, LTF treatment modulated the mitogen-activated protein kinase (MAPK) pathway, but did not affect p53 and STAT3 expression in 5-8F NPC cells. Thus LTF is likely to be a candidate tumor suppressor and downregulates the development of NPC by inhibiting NPC proliferation through induction of cell cycle arrest and modulation of the MAPK signaling pathway. Therefore, our findings provide new insights in understanding the mechanism(s) underlying the action of LTF in regulating the development of human NPC.
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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".