The altered transcriptome of pediatric myelodysplastic syndrome revealed by RNA sequencing
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LETTER TO THE EDITOR
Open Access
The altered transcriptome of pediatric myelodysplastic syndrome revealed by RNA sequencing Lorena Zubovic1*, Silvano Piazza2, Toma Tebaldi3, Luca Cozzuto4, Giuliana Palazzo1, Viktoryia Sidarovich5, Veronica De Sanctis6, Roberto Bertorelli6, Tim Lammens7, Mattias Hofmans7, Barbara De Moerloose7, Julia Ponomarenko4,8, Martina Pigazzi9, Riccardo Masetti10, Cristina Mecucci11, Giuseppe Basso12,13 and Paolo Macchi1*
Abstract Pediatric myelodysplastic syndrome (PMDS) is a very rare and still poorly characterized disorder. In this work, we identified novel potential targets of PMDS by determining genes with aberrant expression, which can be correlated with PMDS pathogenesis. We identified 291 differentially expressed genes (DEGs) in PMDS patients, comprising genes involved in the regulation of apoptosis and the cell cycle, ribosome biogenesis, inflammation and adaptive immunity. Ten selected DEGs were then validated, confirming the sequencing data. These DEGs will potentially represent new molecular biomarkers and therapeutic targets for PMDS. Keywords: Differentially expressed genes, Transcriptome, Pediatrics, Myelodysplastic syndrome To the Editor MDSs are a heterogeneous group of clonal hematopoietic neoplasms. Although recent studies have shown that MDS and AML patients had different gene mutation patterns [1–4], the molecular underpinnings remain unknown [5–10]. To identify DEGs related to the PMDS, we performed RNA-seq in 4 patients with primary PMDS and in 2 control pediatric samples (Additional file 1: Figures S1A-B). Because of the limited number of samples and to limit the false positives, we used two independent bioinformatics pipelines, STAR + DESeq2 and SALMON + edgeR, and considered only genes differentially expressed in both pipelines. Hierarchical clustering showed that PMDS patients and controls *Correspondence: [email protected]; [email protected] 1 Laboratory of Molecular and Cellular Neurobiology, Department of Cellular, Computational and Integrative Biology ‑ CIBIO, University of Trento, Trento, Italy Full list of author information is available at the end of the article
clustered in two distinct groups (Fig. 1a). In total, 651 DEGs were identified by STAR + DESeq2 and 616 DEGs by SALMON + edgeR (Fig. 1B; Additional file 1: Figures S1C-D). 291 DEGs were identified by both pipelines among which 136 genes were upregulated and 155 downregulated in patients (Additional file 1: Table 1). As a further validation, we used the LPEseq method. The concordance of the genes in the ranks of the differential gene lists was remarkably high (Additional file 1: Figures S1EG). We then used GSEA to identify altered pathways from the Reactome database (Web reference 1) (Fig. 1c). The Enrichr enrichment analysis tool revealed that DEGs in PMDS are mainly related to pathways associated with the cell abnormal activity, immune and inflammatory systems and erythropoiesis (Additional file 1: Figure S2A). Further, we compared our data with the transcriptomic profiles from T
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