Imatinib (IMA) and nilotinib are the first and second generations of BCR-ABL tyrosine kinase inhibitors, which widely applied in chronic myeloid leukemia (CML) treatment. Here we aimed to provide new targets for CML treatment by transcriptome analysis. Microarray data GSE19567 was downloaded and analyzed from Gene Expression Omnibus (GEO) to identify common genes, which are downregulated or upregulated in K562-imatinib and K562-nilotinib treated cells. The differentially expressed genes (DEGs) were assessed, and STRING and Cytoscape were used to create the protein-protein interaction (PPI) network. In imatinib and nilotinib treated groups' comparison, there were common 626 upregulated and 268 downregulated genes, which were differentially expressed. The GO analysis represented the enrichment of DEGs in iron ion binding, protein tyrosine kinase activity, transcription factor activity, ATP binding, sequence-specific DNA binding, cytokine activity, the mitochondrion, sequence-specific DNA binding, plasma membrane and cell-cell adherens junction. KEGG pathway analysis revealed that downregulated DEGs were associated with pathways including microRNAs in cancer and PI3K-Akt signaling pathway. Furthermore, upregulated DEGs were involved in hematopoietic cell lineage, lysosome and chemical carcinogenesis. Among the upregulated genes, MYH9, MYH14, MYL10, MYL7, MYL5, RXRA, CYP1A1, FECH, AKR1C3, ALAD, CAT, CITED2, CPT1A, CYP3A5, CYP3A7, FABP1, HBD, HMBS and PPOX genes were found as hub genes. Moreover, 20 downregulated genes, YARS, AARS, SARS, GARS, CARS, IARS, RRP79, CEBPB, RRP12, UTP14A, PNO1, CCND1, DDX10, MYC, WDR43, CEBPG, DDIT3, VEGFA, PIM1 and TRIB3 were identified as hub genes. These genes have the potential to become target genes for diagnosis and therapy of CML patients.

Chemotherapy resistance is a major obstacle in cancer therapy, and identifying novel druggable targets to reverse this phenomenon is essential. The exosome-mediated transmittance of drug resistance has been shown in various cancer models including ovarian and prostate cancer models. In this study, we aimed to investigate the role of exosomal miRNA transfer in chronic myeloid leukemia drug resistance. For this purpose, firstly exosomes were isolated from imatinib sensitive (K562S) and resistant (K562R) chronic myeloid leukemia (CML) cells and named as Sexo and Rexo, respectively. Then, miRNA microarray was used to compare miRNA profiles of K562S, K562R, Sexo, Rexo, and Rexo-treated K562S cells. According to our results, miR-125b-5p and miR-99a-5p exhibited increased expression in resistant cells, their exosomes, and Rexo-treated sensitive cells compared to their sensitive counterparts. On the other hand, miR-210-3p and miR-193b-3p were determined to be the two miRNAs which exhibited decreased expression profile in resistant cells and their exosomes compared to their sensitive counterparts. Gene targets, signaling pathways, and enrichment analysis were performed for these miRNAs by TargetScan, KEGG, and DAVID. Potential interactions between gene candidates at the protein level were analyzed via STRING and Cytoscape software. Our findings revealed CCR5, GRK2, EDN1, ARRB1, P2RY2, LAMC2, PAK3, PAK4, and GIT2 as novel gene targets that may play roles in exosomal imatinib resistance transfer as well as mTOR, STAT3, MCL1, LAMC1, and KRAS which are already linked to imatinib resistance. MDR1 mRNA exhibited higher expression in Rexo compared to Sexo as well as in K562S cells treated with Rexo compared to K562S cells which may suggest exosomal transfer of MDR1 mRNA. Graphical Abstract

Introduction: Pompe disease (PD) is a disease caused by pathogenic variations in the GAA gene known as glycogen storage disease type II, characterized by heart hypertrophy, respiratory failure, and muscle hypotonia, leading to premature death if not treated early. The only treatment option, enzyme replacement therapy (ERT), significantly improves the prognosis for some patients while failing to help others. In this study, the determination of key genes involved in the response to ERT and potential molecular mechanisms were investigated. Methods: Gene Expression Omnibus (GEO) data, accession number GSE38680, containing samples of biceps and quadriceps muscles was used. Expression array data were analyzed using BRB-Array Tools. Biceps group patients did not receive ERT, while quadriceps received treatment with rhGAA at 0, 12, and 52 weeks. Differentially expressed genes (DEGs) were deeply analyzed by DAVID, GO, KEGG and STRING online analyses, respectively. Results: A total of 1727 genes in the biceps group and 1198 genes in the quadriceps group are expressed differently. It was observed that DEGs were enriched in the group that responded poorly to ERT in the 52nd week. Genes frequently changed in the weak response group; the expression of 530 genes increased and 1245 genes decreased compared to 0 and 12 weeks. The GO analysis demonstrated that the DEGs were mainly involved in vascular smooth muscle contraction, lysosomes, autophagy, regulation of actin cytoskeleton, inflammatory response, and the WNT signaling pathway. We also discovered that the WNT signaling pathway is highly correlated with DEGs. Several DEGs, such as WNT11, WNT5A, CTNNB1, M6PR, MYL12A, VCL, TLN, FYN, YES1, and BCL2, may be important in elucidating the mechanisms underlying poor response to ERT. Conclusion: Early diagnosis and treatment of PD are very important for the clinic of the disease. As a result, it suggests that the enriched genes and new pathways emerging as a result of the analysis may help identify the group that responds poorly to treatment and the outcome of the treatment. Obtained genes and pathways in neonatal screening will guide diagnosis and treatment.

Background/aim: Photodynamic therapy (PDT) has received great attention over the past decade in the treatment of diseases such as leukemia which is a cancer of the blood and bone marrow cells that causes a significant number of deaths worldwide. In this study, it was aimed to investigate the effects of Nile blue-mediated PDT (NB-mediated PDT) on HL60 cells. Materials and methods: The effect of NB-mediated PDT on cell proliferation was evaluated with cell volume analysis using flow cytometry at 24 h. Cell apoptosis, ROS production, mitochondrial membrane potential, and cell cycle analysis were evaluated using annexin V-FITC, H2 DCFDA, JC-1, and PI staining, respectively, by flow cytometry and fluorescence microscopy. The morphological and ultrastructural analyses were examined by Giemsa staining and SEM. CD11b staining is used to determine the differentiation of leukemia cells. Results: NB-mediated PDT induced an apoptotic response at 12.5 µM in HL60 cells. When Giemsa staining and SEM images were evaluated, apoptotic bodies, holes, and occasional folds were detected on the surfaces of cells in the NB-mediated PDT group. Conclusion: The NB-mediated PDT had no effect on the differentiation of leukemia cells, but this therapy affects the growth of HL60 cells in vitro, which may provide a new idea for removing leukemic cells from bone marrow intended for autologous transplant.

Background/Aim: Sepsis is a major cause of morbidity, mortality, and healthcare utilization among children all over the world. Sepsis, characterized as life-threatening organ failure, results from a dysregulated host response to infection. When combined with critically low blood pressure, it causes septic shock, resulting in high mortality rates. The aim of this study was to perform a bioinformatic analysis of gene expression profiles to predict septic shock risk. Methods: Four datasets related to pediatric septic shock were retrieved from the Gene Expression Omnibus (GEO) database for a total of 240 patients and 83 controls. GEO2R tools based on R were used to find differentially expressed genes (DEGs). The Database for Annotation, Visualization and Integrated Discovery (DAVID) was used to examine the functional enrichment of DEGs. STRING was used to create a protein–protein interaction (PPI) network. After separately analyzing the four datasets, commonly affected genes were removed using the Venny program. Finally, human umbilical vein endothelial cells (HUVECs) were stimulated with supernatants of lipopolysaccharide (LPS)-stimulated RAW267.4 macrophage cells and expression of selected genes was confirmed by real-time reverse-transcriptase polymerase chain reaction (qRT-PCR) and used to construct an in vitro septic shock model. Results: Seven-hundred seventy-one common differentially expressed genes in the four groups were found. Of these, 433 genes showed increased expression, while 338 had reduced expression. In the DAVID analysis results, DEGs up-regulated according to gene ontology results were enriched in the regulation of innate and adaptive immune responses, complement receptor-mediated signaling, and cytokine secretion processes. Down-regulated DEGs were significantly enriched in the regulation of immune response, T-cell activation, antigen processing, and presentation and integral component of plasma membrane processes. According to The Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), Cystoscape Molecular Complex Detection (MCODE), nine down-regulated genes in the center of the PPI network, ZAP70, ITK, LAT, PRKCQ, LCK, IL2RB, FYN, CD8A, CD247 and four up-regulated genes, MMP9, TIMP1, LCN2, HGF, were associated with septic shock. Expressions of FYN and MMP9 genes in the in vitro septic shock model were consistent with the bioinformatic results. Conclusion: Comparative bioinformatics analysis of data from four different septic shock studies was performed. As a result, molecular processes and important signal networks and 13 genes that we think will play a role in the development and risk prediction of septic shock are proposed. Methods: Four datasets related to Pediatric septic shock were retrieved from the Gene Expression Omnibus (GEO) database for a total of 240 patients and 83 controls. GEO2R tools based on R were used to find differentially expressed genes (DEGs). DAVID was used to examine the functional enrichment of DEGs. STRING was used to create a protein-protein interaction (PPI) network. After separately analyzing the four datasets, commonly affected genes were removed using the Venny program. Finally, HUVECs were stimulated with supernatants of LPS-stimulated RAW267.4 macrophage cells and expression of selected genes was confirmed by qRT-PCR, constructing an in vitro septic shock model. Results: There were 771 common differentially expressed genes in the 4 groups. Of these, 433 genes showed increased expression, while 338 had reducing expression. In the DAVID analysis results, DEGs upregulated by gene ontology were enriched in the regulation of innate and adaptive immune responses, complement receptor-mediated signaling, and cytokine secretion processes. Downregulated DEGs are significantly enriched in the regulation of immune response, T cell activation, antigen processing, and presentation and integral component of plasma membrane processes. According to STRING, cystoscape MCODE, and cytohubba analysis, 9 downregulated genes in the center of the PPI network, ZAP70, ITK, LAT, PRKCQ, LCK, IL2RB, FYN, CD8A, CD247, and 4 upregulated genes, MMP9, TIMP1, LCN2, HGF, were associated with septic shock. Expressions of FYN and MMP9 genes in the in vitro septic shock model were consistent with bioinformatic results. Conclusion: Important signaling networks and 13 genes potentially indicating molecular processes for the incidence, development, and risk prediction in septic shock were found using bioinformatic analysis of gene expression profiles.