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Microrna Expression Profiling in Acute Myelogenous Leukemia.

2004· article· en· W2589917306 on OpenAlexaff
Fernando Suárez, Serban San‐Marina, Mark D. Minden

Bibliographic record

VenueBlood · 2004
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsBiologymicroRNAGene expression profilingHaematopoiesisLeukemiaGeneGene expressionStem cellBone marrowCellular differentiationCancer researchCell biologyGeneticsImmunology

Abstract

fetched live from OpenAlex

Abstract Acute myelogenous leukemia (AML) arises due to changes in gene expression that block or alter the normal differentiation program of hematopoietic stem cells. A variety of mutations in protein-encoding genes have been shown to contribute to the development of leukemia. Recently a new class of genes called microRNAs (miRNAs) have been identified. miRNAs are a subgroup of highly conserved, non-coding RNAs found only in eukaryotes. They do not encode proteins, and appear to have a significant effect on the proteome of a cell. Their conservation between species suggests their involvement in important biological functions, and in fact been shown to be involved in hematopoietic differentiation. While the function of most miRNAs is still unknown, it is believed that they regulate expression of target mRNAs by using the siRNA machinery either to promote degradation of the mRNA or to block its translation. To begin to understand the role of miRNAs in AML, we used Quantitative Polymerase Chain Reaction (QPCR) to measure the expression level of 20 miRNA precursors in the pro erythroid cell line K562, the pro-myelocytic cell line NB4, the myelomococytic cell line OCI/AML2, AML patients’ blasts and in normal bone marrow (NBM). The investigated miRNAs included some that are known to be specific for hematopoietic tissues or involved in hematopoietic differentiation, as well as all the miRNAs in chromosome 7, a hot spot for gene deletion in AML. Our findings indicate that miRNAs are differentially expressed in patients and cell lines when compared among themselves and against normal bone marrow. For example pre-miR-142 was expressed in NBM and K562 but was found to be elevated in OCI/AML2, NB4 and in all patient samples. Pre-miR-20 was found to be overexpressed in only a subset of patients. Other miRNAs like pre-miR-335 and pre-miR-148a were expressed in NBM and in some patients and not in the cell lines. In an effort to identify possible regulators of miRNA expression, we analyzed the upstream region of pre-miR-142 and found an LMO2 binding site. In AML, the LMO2 gene can be overexpressed relative to normal bone marrow and healthy lymphocytes. This transcription factor is involved in the regulation of genes important in the development of blood cells. To investigate if LMO2 could be involved in the regulation of miR-142 expression, we performed chromatin immunoprecipitation (ChIP) from K562 using an anti-LMO2 antibody. Only the LMO2 immunoprecipitation, and not those from the pre-immune control, were enriched in promoter DNA for pre-miR-142. This is consistent with the observation that miRNAs and coding RNAs can be regulated by the same environmental signals. Based on this observation we propose that oncogenes regulate in part the phenotype and biological behaviour of leukemia by affecting the expression of miRNAs. This further suggests that different forms of leukemia may be recognized based upon the spectrum of miRNAs they express.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.006
GPT teacher head0.228
Teacher spread0.221 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2004
Admission routes1
Has abstractyes

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