Identification of Novel Transcripts and Potential Therapeutic Targets for Acute Leukemia
Bibliographic record
Abstract
Acute leukemia is a heterogenous disease with many genomic scars. Recent advances in targeted therapy and immunotherapy have improved the prognosis of this disease. However, prognosis still remains poor, and the identification of novel leukemia-specific transcripts may provide new strategies for leukemia therapy. We developed a novel long-read transcriptome method for sequencing full-length RNAs by directly capturing the 5′-end cap structures and the 3′-end poly(A)-tails of individual RNA molecules. We applied this method to bone marrow samples with various acute leukemias, including acute myeloid leukemia, acute lymphoid leukemia, and other rare types of acute leukemia. We covered full-length poly(A)+ RNAs with an average length distribution of over 3,000 bp, a much longer size distribution than previously reported. This not only led to the discovery of a wide array of uncharacterized transcript isoforms of known genes, but also to the discovery of 1,903 novel genes that are not annotated in the current human gene database. We also found that more than 60% of these new human genes were single exon genes and that many of them emerged from primates. This implicates that these new genes may contribute to human-specific leukemia biology. In addition, among these new human genes, 485 genes were predicted to have the potential to encode putative proteins using a GeneMarkS-T program. A fraction of new genes identified in this study were highly leukemia specific as shown by bulk CAGE-seq analysis of over 100 blood tumor samples and by single-cell RNA sequencing analysis of more than 300,000 bone marrow cells from leukemia patients, highlighting their potential as useful biomarkers and novel therapeutic targets. In sum, we constructed a comprehensive atlas of full-length RNA molecules in acute leukemia and identified a large number of uncharacterized ones. Our study provides a versatile framework for exploring novel transcripts and future therapeutic strategies in human diseases.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".