Leukemia-Related Gene Expression of Bone Marrow Cells from Patients with Shwachman-Diamond Syndrome at the Pre-Leukemic Phase.
Notice bibliographique
Résumé
Abstract Background: Shwachman-Diamond syndrome (SDS) is an inherited bone marrow failure disorder characterized by varying degrees of cytopenia and a high propensity for myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML) in up to 36% of the patients by the age of 30 years. Although the gene associated with SDS, SBDS, has recently been identified, its function, the link with MDS/AML and the mechanism for the development of MDS/AML in SDS is unclear and the molecular events occurring during transformation haven’t been yet identified. It is likely that several events occur many years before overt transformation occurs, and might be identifiable by comprehensive analysis. Objectives: To use oligonucleotide microarray to identify leukemogenic gene expression before overt transformation, which can explain a propensity for MDS/AML. Methods: Total RNA from marrow cells from 9 SDS patients and 7 healthy age-matched donors of bone marrows for transplantation was extracted, labeled and hybridized to Affymetrix HG_U133_Plus2.0 GeneChip. Data were pre-processed using robust multichip analysis (RMA) and differentially expressed genes were identified with permutation-based methods. False discovery rate (FDR)-adjusted p-values were used to rank genes and cluster analysis grouped genes and samples. Real-time PCR was performed to confirm differential expression of genes found by microarray. Results: Of the 38,500 genes on the HG_133_Plus2.0 we analyzed 52 known leukemia-related genes. We identified several genes with small FDR-adjusted p-values. Clustering of arrays resulted in two clusters that clearly separated patients from controls. Interestingly among the leukemia-related genes, the most differentially expressed gene (T=4.2) was ARHGEF12, a member of the Rho GEF family. Rho GEFs are oncogenes; many of them can transform NIH 3T3 cells into a malignant phenotype by altering expression and activation of Rho GTPases. ARHGEF12 is mapped at 11q23, telomeric to MLL, and is a novel MLL fusion partner in acute myeloid leukemia. Real time PCR after normalization against beta-actin confirmed statistically higher expression of the ARHGEF12 (p=0.03) in SDS marrow cells. In addition to ARHGEF12, we have found striking expression changes in several other genes, related to MDS/AML including TAL1, whose differential expression was also confirmed by real-time PCR. Conclusions: SDS marrow cells exhibit abnormal gene expression pattern, which might results in continuous stimulation favoring evolution or progression of malignant clones. Additional molecular and cytogenetic events are likely necessary for the malignant process to be irreversible and complete. Although analysis of whole marrow cells may not enable the detection of genes with lower differential expression between SDS and normal, it may still assist identifying molecular pathways involved in leukemogenesis. This is critically important when studying marrow failure disorders as obtaining sufficient amount of RNA from purified cell population is largely impossible.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».