Characterization of the Microrna Expression Profiles of Paired Primary and Relapsed Diffuse Large B-Cell Lymphoma (DLBCL) By Next-Generation Sequencing
Notice bibliographique
Résumé
Abstract Diffuse large B-cell lymphoma (DLBCL) is the most common lymphoma in adults. Although 60-70% of the patients can be cured with standard therapeutic regimens, a substantial number of patients die from the disease due to treatment resistance. In order to better understand the biological processes behind the resistance to treatment, we have characterized the microRNA (miRNA) expression profiles of matched primary and relapsed DLBCL. We performed next-generation miRNA sequencing of seven primary–relapse sample pairs. A total of 492 known miRNAs were found to be expressed in the DLBCL samples. In addition, we identified 223 potentially novel, previously uncharacterized miRNAs. A majority of the detected miRNAs showed similar expression across primary and relapse samples; we identified 24 high-expressed miRNAs and 177 low-expressed miRNAs in the DLBCL samples as compared to a reference control set of non-malignant cells downloaded from the Gene Expression Omnibus (GSE15229). Interestingly, only 13 miRNAs had differential expression between the primary–relapse sample pairs. Of these, five miRNAs had higher expression and eight miRNAs had lower expression in the relapse samples as compared to the primary samples. In order to identify potential targets for the differentially expressed miRNAs, we integrated the miRNA data with total RNA-sequencing data from the same samples (n=10, or 5 pairs) as well as with the miRNA target predictions from four prediction programs (TargetScan, microCosm, PITA, DIANA microT) and with filtered data of functionally validated miRNA targets from the miRTarBase. This analysis resulted in 1,088 miRNA-transcript pairs representing 787 individual genes inversely correlated with at least one of the 13 miRNAs (r<-0.7, p<0.05), and whose regulatory pairings were supported by at least one prediction program or mirTarBase. Further Gene Ontology annotation and pathway enrichment analyses revealed the putative targets of differentially expressed miRNAs to be significantly enriched for several cancer-associated pathways that include phosphatidyl-inositol signaling (e.g. PIP5K1A, PIK3C2A, PIK3CG, PIK3R1), JAK-STAT signaling (e.g. STAT5A, STAT5B), and B-cell receptor signaling (e.g.SYK, MAPK1), suggesting activation of these pathways in the relapsed DLBCL. In line with this, Kaplan-Meier survival analyses indicated higher expression of genes from the phosphatidyl-inositol signaling and B-cell receptor signaling, such as phosphatidylinositol 4-phosphate 5-kinase (PIP5K1A) and spleen tyrosine kinase (SYK), to be associated with shorter progression-free and overall survival (p<0.001 for both genes) in immunochemotherapy-treated patients (n=92).Validations of the findings are currently ongoing. In conclusion, our study on the comparison of paired primary and relapsed DLBCL demonstrates that the miRNA expression profile remains relatively constant during the disease progression. However, a small set of differentially expressed miRNAs may contribute to the relapse by regulating key cell survival pathways, thus representing potential novel therapeutic targets. 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 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 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) | 0,001 | 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 ».