The lncRNA KTN1-AS1 co-regulates a variety of Myc-target genes and enhances proliferation of Burkitt lymphoma cells
Bibliographic record
Abstract
Long non-coding RNAs (lncRNAs) are involved in many normal and oncogenic pathways through a diverse repertoire of transcriptional and posttranscriptional regulatory mechanisms. LncRNAs that are under tight regulation of well-known oncogenic transcription factors such as c-Myc (Myc) are likely to be functionally involved in their disease-promoting mechanisms. Myc is a major driver of many subsets of B cell lymphoma and to date remains an undruggable target. We identified three Myc-induced and four Myc-repressed lncRNAs by use of multiple in vitro models of Myc-driven Burkitt lymphoma and detailed analysis of Myc binding profiles. We show that the top Myc-induced lncRNA KTN1-AS1 is strongly upregulated in different types of B cell lymphoma compared with their normal counterparts. We used CRISPR-mediated genome editing to confirm that the direct induction of KTN1-AS1 by Myc is dependent on the presence of a Myc E-box-binding motif. Knockdown of KTN1-AS1 revealed a strong negative effect on the growth of three BL cell lines. Global gene expression analysis upon KTN1-AS1 depletion shows a strong enrichment of key genes in the cholesterol biosynthesis pathway as well as co-regulation of many Myc-target genes, including a moderate negative effect on the levels of Myc itself. Our study suggests a critical role for KTN1-AS1 in supporting BL cell growth by mediating co-regulation of a variety of Myc-target genes and co-activating key genes involved in cholesterol biosynthesis. Therefore, KTN1-AS1 may represent a putative novel therapeutic target in lymphoma.
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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".