Akut ve Kronik Miyeloid Lösemili Hastalarda Prognostik miRNA İmzasının Biyoinformatik Analiz ile Karşılaştırılması

Öz

Amaç: Bu çalışmada, terapötik yanıtta yer alan miRNA’ları belirlemek için AML ve KML hastalarının örneklerinden alınan yüksek verimli ekspresyon verilerini kullanarak diferansiyel miRNA ekspresyonunu analiz ettik. Materyal ve Metot: miRNA mikrodizi veri setleri GSE142699 ve GSE90773, GEO veritabanı aracılığıyla indirildi ve çevrimiçi analiz aracı GEO2R ile analizi yapıldı. GSE142699 nolu data, 24 kontrol ve 24 yeni teşhis edilmiş AML hastası, GSE90773 nolu data ise 8 kontrol ve 10 yeni tanı konmuş KML hastası ile yapılmıştır. Analiz sonrası kat değişimi (FC) değerlerine ve p<0.05’e göre gruplandırıldı. TargetScan ve miRDB veritabanları, diferansiyel olarak eksprese edilen miRNA’lar tarafından düzenlenen potansiyel hedef genlerini tahmin etmek için kullanıldı. Hedef genler DAVID programı kullanılarak, aday hedef genlerin zenginleştirme analizi GO fonksiyon ve KEGG yolu analizi gerçekleştirildi. Daha sonra hedef genler üzerinden düzenleyici ağ Cytoscape kullanılarak hub genler tesbit edildi. Bulgular: AML grubunda ekspresyonu artan 27 ve azalan 161 benzersiz miRNA bulundu. KML grubunda ekspresyonu artmış 52 ve ekspresyonu azalmış 122 benzersiz miRNA bulundu. AML ve KML grupları arasında kümeleme analizinden sonra ekspresyonu azalmış 11 miRNA ve ekspresyonu artmış 5 miRNA bulundu. Benzer olan ancak iki grupta farklı eksprese edilen 7 miRNA filtrelenmiştir. 7 miRNA’dan toplam 2525 tahmin edilen hedef gen bulundu. Farklı eksprese edilen miRNA’ların 22 ortak sinyal yolunu, özellikle kanserdeki yolları, PI3K-Akt sinyalini ve MAPK sinyalini etkilediği ortaya çıkmıştır. Sonuç: Bulgularımız, aynı miRNA’ların insan lösemi gelişiminde farklı düzenlenleyiciler olarak rol aldığını göstermiştir. Miyeloid gelişimindeki farklı miRNA imzaları, miyeloid lösemilerin klinik teşhisi ve ayrımı, prognozu ve tedavisi için kullanılabilecek birer biyobelirteç adayı olabilir.

Anahtar Kelimeler

• Biyoinformatik analiz
• GEO
• Lösemi
• AML
• CML
• MiRNA

Comparison of Prognostic miRNA Signature in Patients with Acute and Chronic Myeloid Leukemia by Bioinformatic Analysis

Abstract

Aim: In this study, differentially expressed miRNA profiles were determined using high-throughput expression data from samples of AML and CML patients to identify miRNAs involved in the therapeutic response. Material and Methods: miRNA microarray datasets GSE142699 and GSE90773 were downloaded via the GEO database and analysis was performed with the online analysis tool GEO2R. Data no. GSE142699 was made with 24 control and 24 newly diagnosed AML patients, data no. GSE90773 was made with 8 control and 10 newly diagnosed CML patients. After the analysis, they were grouped according to fold change (FC) values and p<0.05. Potential target genes regulated by differentially expressed miRNAs were predicted using the miRDB and TargetScan databases. Target genes enrichment analysis was performed GO function and KEGG pathway analysis using the DAVID program. Then, hub genes were detected using the regulatory network Cytoscape over the target genes. Results: There were 27 unique miRNAs whose expression increased and 161 decreased in the AML group. In the CML group, 52 unique miRNAs with increased expression and 122 unique miRNAs with decreased expression were found. After clustering analysis between the AML and CML groups, 11 miRNAs with decreased expression and 5 miRNAs with increased expression were found. 7 miRNAs that were similar but differently expressed in the two groups were filtered out. A total of 2525 predicted target genes were found from 7 miRNAs. It was revealed that differently expressed miRNAs affect 22 common signaling pathways, especially the pathways in cancer, MAPK signaling, and PI3K-Akt signaling. Conclusion: Our findings demonstrated that the same miRNAs are involved as different regulators in human leukemia development. Different miRNA signatures in myeloid development may be candidates for biomarkers for clinical diagnosis and differentiation, prognosis, and treatment of myeloid leukemias.

Keywords

• Bioinformatic analyses
• GEO
• Leukemia
• AML
• CML
• MiRNA

Kaynakça

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