Identification of latent core genes and pathways associated with myelodysplastic syndromes based on integrated bioinformatics analysis

Abstract

Background:: Myelodysplastic syndromes (MDS) are relatively common hematological malignancies characterized by dysplastic hematopoiesis in one or more of the lineages of the bone marrow. This study aimed to identify critical pathogenic biomarkers associated with the carcinogenesis and progression of MDS. Methods:: To explore the candidate genes, the expression profiles of GSE2779, GSE4619, and GSE19429 were downloaded from the Gene Expression Omnibus (GEO) database, which contained CD34+ cells isolated from MDS patients and normal controls. The three microarray datasets were integrated to obtain differentially expressed genes (DEGs) and were deeply analyzed by bioinformatics methods. The construction of protein-protein interaction (PPI) network together with module analysis was performed based on Cytoscape software and the Search Tool for the Retrieval of Interacting Genes (STRING) database. Results:: Our study identified 114 DEGs, which were highly enriched in various key pathways, including forkhead box protein O (FoxO) signaling pathway, the primary immunodeficiency, and hematopoietic cell lineage. Twelve core genes, such as FOXO1, PAX5 and CXCR4 were identified with a high degree of connectivity. It is plausible that FoxO signaling pathway plays an important role in MDS, and the dysregulation of FOXO1 was significantly associated with TGFβ, IL2/STAT5, Notch signaling and apoptosis pathways. Conclusion:: The current study for the first time identified twelve latent indicators and their downstream targets, which might become significant biomarkers for worse clinical characteristics in MDS.

Noncoding RNAs and Their Response Predictive Value in Azacitidine-treated Patients With Myelodysplastic Syndrome and Acute Myeloid Leukemia With Myelodysplasia-related Changes

“…In patients, 331 PCGs were up-regulated (based on logFC, the 12 most up-regulated genes were COL4A5, DSC2, CASS4, SLITRK5, LGALSL, CD5L, TPSB2, LOXL4, HBA1, MFAP3L, HBA2, and CANCB4) and 126 PCGs were down-regulated (the 12 most down-regulated genes were ARPP21, PAX5, EBF1, AKAP12, POU2AF1, RAG1, MME, AREG, CD79A, NEIL1, CD24, and LEF1). These differences were comparable to those published in earlier gene expression profiling studies (34)(35)(36)(37). For example, down-regulation of LEF1 significantly correlated with clinical outcome and might be promising for assessing prognosis in MDS (37).…”

“…GSEA output is summarized in Figure 2. Because PCG profiling has repeatedly been performed in MDS and many signaling pathways associated with disease pathophysiology have been described (34)(35)(36)(37), the expression of various classes of noncoding transcripts was a focus of this study. Among the lncRNAs identified by standard RNA-seq data analysis, 80 differentially expressed transcripts (|logFC|>1, FDR<0.05; 64 up-regulated/ 16 down-regulated, Figure 3A) were found.…”

Expression profile analysis reveals hub genes that are associated with immune system dysregulation in primary myelofibrosis

“…In this regard, exploring and elucidating the in-depth mechanisms of immune responses may offer some inspiration to reduce the occurrence of complications, ensure the quality of prognosis and develop targeted medicine for PMF. Rapidly developed omics and bioinformatics technologies have offered comprehensive insight into the molecular mechanisms of various clinical issues, and some satisfactory results may be obtained through bioinformatics analysis based on public databases [14].…”