Epigenetic Regulation of <i>NAMPT</i> by <i>NAMPT-AS</i> Drives Metastatic Progression in Triple-Negative Breast Cancer
Bibliographic record
Abstract
Abstract Triple-negative breast cancer (TNBC) is highly heterogeneous and has a poor prognosis. It is therefore important to identify the underlying molecular mechanisms in order to develop novel therapeutic strategies. Although emerging research has revealed long noncoding RNAs (lncRNA) as vital to carcinogenesis and cancer progression, their functional involvement in TNBC has not been well defined. In this study, we utilized the The Cancer Genome Atlas (TCGA) database and analyzed clinical samples to show that the long noncoding antisense transcript of nicotinamide phosphoribosyltransferase (NAMPT), NAMPT-AS, is upregulated in TNBC and is associated with poor prognosis, lymph node involvement, metastasis, and advanced stage. NAMPT-AS was cotranscribed with NAMPT from a bidirectional promoter, where the distributions of H3K4me3 and H3K27Ac chromatin modifications were enriched based on ENCODE and FANTOM5, suggesting the potential enhancer-RNA characteristics of NAMPT-AS. NAMPT-AS epigenetically regulated the expression of NAMPT in two divergent ways: NAMPT-AS recruited POU2F2 to activate the transcription of NAMPT, and NAMPT-AS acted as a competing endogenous RNA to rescue NAMPT degradation from miR-548b-3p. NAMPT-AS/NAMPT promoted tumor progression and regulated autophagy through the mTOR pathway in vitro and in vivo. In a cohort of 480 breast cancer patients, NAMPT was associated with breast cancer–specific survival and overall survival. These results demonstrate that NAMPT-AS is an oncogenic lncRNA in TNBC that epigenetically activates NAMPT to promote tumor progression and metastasis. Furthermore, these data identify NAMPT-AS/NAMPT as promising therapeutic targets in patients with TNBC. Significance: Upregulation of the long noncoding antisense RNA of NAMPT gene (NAMPT-AS) is associated with metastasis and poor prognosis in TNBC.
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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".