Differences in Variants in the Structural Domain of BCR-ABL1 Kinase between Chinese Han and Minority Patients with Chronic Myeloid Leukemia by Sanger Sequencing and Next-Generation Sequencing.

This study aimed to detect differences in BCR-ABL1 kinase domain (KD) variants in patients with chronic myeloid leukemia (CML) who have been warned and failed in tyrosine kinase inhibitor (TKI) treatment among Chinese Han and ethnic minorities through Sanger sequencing (SS) and next-generation sequencing (NGS), and analyze the difference between SS and NGS detection. Peripheral blood samples from 51 CML patients with warning and failure of TKI therapy were analyzed using SS and NGS, and the detection differences between both sequencing types were compared. BCR-ABL1 KD variants were found in 23.53% of the cohort, including 7 Han Chinese (58.33%) and 5 ethnic minority cases (41.67%). Y253H, F317L, M244V, D276G, F359I, L387F, E459K, E255K, T315I, M351V, and heterozygous insertional mutated genes (ABL1 c.1068_1070dup) were detected. Comparison of the two sequencing assays revealed that NGS could detect compound variants and low frequency variants that were not detected by SS. More compound variants were detected in Han patients than in ethnic minority patients. In conclusion, there is no significant difference in BCR-ABL1 KD mutations between Han and ethnic minority patients. NGS has a higher mutation detection rate than SS, and can detect compound variants and genes with lower mutation frequency that are not detected by SS.

Identification of key genes and miRNA-mRNA regulatory networks associated with bone marrow immune microenvironment regulations in multiple myeloma by integrative bioinformatics analysis.

The deregulation of microRNAs (miRNAs) and genes in the bone marrow microenvironment have been involved with the pathogenesis of multiple myeloma (MM). However, the exploration of miRNA-mRNA regulatory networks in MM remains lacking. We used GSE125363, GSE125361, GSE47552, GSE2658, GSE136324, GSE16558, and GSE13591 datasets for this bioinformatics study. We identified 156 downregulated and 13 upregulated differentially expressed miRNAs (DEmiRs) in MM. The DEmiRs are associated with the enrichment of pathways mainly involved with cancers, cellular signaling, and immune regulations. We identified 112 hub genes associated with five significant clusters in MM. Moreover, we identified 9 upregulated hub genes (such as IGF1, RPS28, UBA52, CDKN1A, and CDKN2A) and 52 downregulated hub genes (such as TP53, PCNA, BRCA1, CCNB1, and MSH2) in MM that is targeted by DEmiRs. The expression of DEmiRs targeted two hub genes (CDKN2A and TP53) are correlated with the survival prognosis of MM patients. Furthermore, the expression level of CDKN2A is correlated with immune signatures, including CD4+ Regulatory T cells, T cell exhaustion, MHC Class I, immune checkpoint genes, macrophages, neutrophils, and TH2 cells in the TME of MM. Finally, we revealed the consistently deregulated expression level of key gene CDKN2A and its co-regulatory DEmiRs, including hsa-mir-192, hsa-mir-10b, hsa-mir-492, and hsa-mir-24 in the independent cohorts of MM. Identifying key genes and miRNA-mRNA regulatory networks may provide new molecular insights into the tumor immune microenvironment in MM.

Bioinformatics analysis deciphering the transcriptomic signatures associated with signalling pathways and prognosis in the myelodysplastic syndromes.

BACKGROUND: Several studies scatteredly identified the myelodysplastic syndromes' transcriptomic profiles (MDS). However, the exploration of transcriptional signatures, key signalling pathways, and their association with prognosis and diagnosis in the integrated multiple datasets remains lacking. METHODS: We integrated the GSE4619, GSE19429, GSE30195, and GSE58831 microarray datasets of CD34 + cells for identifying the differentially expressed genes (DEGs) in the MDS. The series of bioinformatics methods are applied to identify the key hub genes, gene clusters, prognostic hub genes, and genes associated with diagnostic efficacy. Finally, we validated the expression differences of hub genes in the GSE114922 dataset. RESULTS: We explored the DEGs related to gene ontology enrichment and KEGG pathways. We identified significant hub genes, including 168 upregulated hub genes (such as STAT1, IFIH1, EPRS, GRB2, RAC2, MAPK14, CASP1, and SPI1) and 52 downregulated hub genes (such as CREBBP, HIF1A, PIK3CA, EZH2, PIK3R1, MDM2, IRF4, CXCR4, PCNA, and CD19) in the MDS. In addition, we identified six significant molecular complex detection (MCODE)-derived upregulated gene clusters and one downregulated gene cluster, respectively. Moreover, we found that the higher expression level of MX2, GBP2, PXN, IFI44, FDXR, PLCB2, ASS1, ERCC4, PML, and RRAGD and the lower expression level of CD19, PAX5, TCF3, LEF1, NUSAP1, and TIMELESS hub genes are significantly correlated with shorter survival times of MDS patients. Furthermore, the area value under the ROC curve (AUC) of PXN, FDXR, PLCB2, PML, CD19, PAX5, and LEF1 prognostic genes are more than 0.80, indicating that these genes could be effectively used for the diagnostic efficacy of MDS patients. CONCLUSIONS: Identifying key hub genes and their association with the prognosis and diagnostic efficacy may provide substantial clues for the treatment and diagnosis of MDS patients.

Synergistic inhibitory effect of Smo inhibitor jervine and its combination with decitabine can target Hedgehog signaling pathway to inhibit myelodysplastic syndrome cell line.

OBJECTIVE: Hypomethylating agents (HMAs) have been reported to target the Sonic Hedgehog (Shh) signaling pathway in myelodysplastic syndrome (MDS). However, the synergistic inhibitory effect of Smo inhibitor jervine and its combination with decitabine in MUTZ-1 cell lines remains lacking. METHODS: We used a CCK-8 assay to detect the in-vitro proliferation rate of MUTZ-1 cell lines. Besides, the Annexin V-FITC/PI double staining flow cytometry was utilized to detect the apoptosis rate and cell cycle changes. The expression levels of mRNA were quantified by using qRT-PCR, and the western blot was employed to detect the expression of proteins. RESULTS: We found that the single-agent jervine or decitabine can significantly inhibit the proliferation rate of MUTZ-1 cell lines, and this inhibitory effect is time-dependent and concentration-dependent. The combined intervention of the jervine and decitabine can more significantly inhibit cell proliferation, induce cell apoptosis, and block the G1 phase of the cell cycle. The combined intervention of the two drugs significantly reduced Smo and G1i-1 mRNA expression in MUTZ-1 cells. Furthermore, after combining both of the drug treatments, the proteins levels of Smo, G1i-1, PI3K, p-AKT, Bcl2, and Cyclin Dl were significantly downregulated, and Caspase-3 is upregulated, indicating that jervine with its combination of decitabine might be effective for controlling the proliferation, apoptosis, and cell cycle. CONCLUSION: The Smo inhibitor jervine and its combination with decitabine have a synergistic effect on the proliferation, cell cycle, and apoptosis of MUTZ-1 cells, and its mechanism may be achieved by interfering with the Shh signaling pathway.