Identification of New microRNA Biomarkers and Candidate Target Genes in Primitive CML Cells Using Global Comparative RNA analyses
Notice bibliographique
Résumé
Abstract Imatinib mesylate (IM) and other ABL tyrosine kinase inhibitors (TKIs) have had a major impact on early phase CML patient outcomes. However, they are rarely curative and initial and acquired TKI resistance remain challenges. This is attributed to the finding that chronic phase CML stem cells are innately more resistant to IM and other TKIs than the bulk of the more mature cells they generate. To identify differentially expressed and new miRNAs in CD34+ CML stem/progenitor cells that could be potential biomarkers and therapeutic targets, we used Illumina Deep Sequencing to obtain absolute miRNA expression profiles of highly purified CD34+ cells obtained at diagnosis from three CML patients who were classified retrospectively, after IM therapy, as IM-responders and three as IM-nonresponders. CD34+ cells isolated from five normal bone marrow (NBM) samples were similarly analyzed as controls. Bioconductor DESeq analysis revealed 63 differentially expressed miRNAs in the CD34+ cells from CML and NBM samples (P<0.05). Interestingly, 12 of these were differentially expressed in CD34+ cells from the IM-responders and nonresponders. Most of the 63 differentially expressed miRNAs identified were present at reduced levels in the CD34+ CML cells as compared to NBM, but 17 were increased. In addition, 34 novel miRNAs were identified in the CD34+ CML stem/progenitor cells. We next validated sequencing data in CD34+ cells from IM-responders (n=12), IM-nonresponders (n=10) and normal individuals (n=11) using a high-throughput quantitative microfluidic device. These studies confirmed the differential expression in CD34+ CML cells of 32 of the 63 identified miRNAs (P<0.05), including an increased level of oncomirs miR-155 and miR-17-92, and a decreased level of the tumor suppressors, miR-145, miR-151, and miR-452. Importantly, we detected significant changes in some of these miRNAs in CD34+ cells from CML patients after three months of nilotinib (NL) treatment (23 normalized after three months of NL treatment, whereas 10 showed little change). To further correlate miRNA profiles with corresponding mRNA expression changes, and to identify potential target genes, RNA-seq was performed on the same RNA samples. Bioconductor RmiR analysis was performed to match miRNA target genes whose expression was inversely correlated with the expression of the deregulated miRNAs based on three of six prediction algorithms (mirBase, TargetScan, miRanda, tarBase, mirTarget2, and PicTar). We identified 1,210 differentially expressed mRNAs that are predicted targets of the deregulated miRNAs in the comparison of CML and NBM data. Strikingly, only seven differentially expressed mRNAs were predicted targets of the deregulated miRNAs identified from a comparison of the IM-responders and nonresponders. Most of these are predicted to have roles in regulation of the cell cycle, MAPK signaling and TGF-beta signaling pathways by DAVID Bioinformatics Resources, which clusters predicted target genes to known KEGG pathways. Thus, aberrant, differentially expressed miRNAs and target genes identified in primitive CML stem/progenitor cells may serve as useful biomarkers to predict clinical response of CML patients to TKI therapy, and may ultimately lead to identification of potential therapeutic targets for improved treatment of CML patients. Disclosures No relevant conflicts of interest to declare.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,001 | 0,001 |
| 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) | 0,001 | 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 ».