Aberrant Co-Expression of Micrornas and Host Genes in Leukemia: Evidence for a Common Transcript from Meta-Analysis and Microarray Data.
Bibliographic record
Abstract
Abstract In leukemia, the integrity of the transcriptome is altered by chromosomal translocations, deletions, duplications, as well as by epigenetic changes in chromatin structure. By targeting mRNAs for translational repression or RNase-dependent hydrolysis (AU-rich miRNAs or shRNA-like effects), the micro RNA (miRNA) component of the transcriptome is estimated to regulate expression of up to 30% of all proteins. Yet the causes and role of deregulated miRNA expression in malignancy are largely unknown, in part because promoter events are not characterized. Since more than one-third of all known mammalian miRNA genes are encoded in the introns of protein-coding genes they may be regulated by the same promoter events that regulate host-gene mRNA expression. To provide experimental validation for coordinated expression of miRNAs and their host genes we compared Affymetrix U133A gene expression data for the promyelocytic NB4 and acute myelogenous leukemia AML2 cell lines with the expression of miRNA precursors. We found similar patterns of host gene expression in the two cell lines and a good correlation with the expression of miRNA precursors in NB4 cells (r=0.464, N=30 miRNAs, p<0.016). To further demonstrate that host gene mRNAs and miRNAs are expressed from common transcripts, we activated promoter events by enforcing the expression of Lyl1 a basic helix-loop-helix transcription factor that is often over-expressed in AML. This resulted in a greater than 2-fold increase in hsa-mir-126-1, 032-2, 107-1, 026a, -023b, -103-2, and 009-3-1 intronic miRNA precursors and a corresponding increase in host gene expression. Meta-analysis of microarray data across many experiments and platforms (available through Oncomine.org) has been used to study the cancer transcriptome. To help determine if intronic miRNAs play a substantial role in malignancy, we correlated host gene expression data with the expression of predicted miRNA targets. Less than 20% of all differentially expressed genes in leukemia and lymphoma were predicted targets, compared to 68% in breast cancer. Since the Gene Ontology term “ion binding” is most commonly associated with miRNA host genes, the data suggest that this cancer module is relatively inactive in leukemia and lymphoma, compared to breast cancer. Gene cluster analysis of a leukemia data set using only miRNA host gene expression was able to classify patients into similar (but not identical) subsets as did an analysis based on over 20,000 transcripts. To further demonstrate that miRNAs and their host genes are expressed from the same transcription unit, we correlated the expression of miRNA targets with that of genes that are either hosts for miRNAs or are situated several kilobases downstream of a miRNA, and thus belong to different transcription units. We applied this analysis to a subset of 81 AML patients that presented a normal karyotype and found significant negative correlations (p<0.01) between the levels of host genes for hsa-mir-15b, -103-1, and -128 and the expression ranks of their predicted gene targets, but no statistically significant correlation between non-host genes and targets for up-stream miRNAs. These data demonstrate co-regulated expression of host genes and intronic miRNAs and the usefulness of intronic miRNAs in cancer profiling.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".