Essential gene networks in acute myeloid leukemia identified using a <i>microRNA</i> -knockout CRISPR library screen
Bibliographic record
Abstract
Abstract MicroRNAs (miRNA) are small RNAs that function as key modulators of gene expression. Due to their promiscuity of binding, a single miRNA may regulate several genes and hence, multiple pathways simultaneously. In addition, the 3’-UTR of mRNA can be recognized by several miRNA for suppression or degradation. We built a mi cro R NA-only K nock- o ut (miRKo) CRISPR/Cas-9 library to identify essential miRNA in Acute Myeloid Leukemia (AML) using OCI-AML2, OCI-AML3 and U937 cell lines as in vitro models. 10 miRNA were identified to be essential in our screen among all three cell lines: miR-19b-1, -19b-2, 29b-2, -302a, -3678, -3713, -3910-1, -4447, -4718 and -6795 . By using weighted degrees of association, we identified pathway hubs that uniquely affect all 3 cell lines by integrating miRNA:mRNA networks using mirDIP and pathway analysis using pathDIP. Through the miRKo screen, network membership analyses and biological anticorrelation scoring through patient data analysis, we identified RRP2CA, RPS6KB-1, CREB1, RPM1A, MAPK10, MAP3K2, ITCH, FBX-W7, NR3C1 and XIAP as likely targets of the essential miRNA in AML; and signal transduction, apoptosis, TGF-beta signalling and MAPK signalling as candidate essential pathways in AML.
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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 | 0.001 | 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) | 0.002 | 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".