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.

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.

Chronic Myeloid Leukemia (CML) is particularly challenging to treat due to the T315I BCR::ABL1 mutation. Although fungal metabolites are known for their pharmaceutical potential, none are approved for CML. Our study screened approximately 2000 fungal secondary metabolites to discover inhibitors targeting the T315I- BCR::ABL1 mutant protein. We conducted comprehensive analyses to elucidate the interactions between the T315I-BCR::ABL1 mutant protein and selected fungal metabolites. These analyses included molecular docking, ADMET assessment, molecular dynamics simulations, principal components analysis, exploration of free energy landscapes, and per-residue decomposition. We identified a range of binding affinities for fungal secondary metabolites, from −11.2 kcal/mol to −2.90 kcal/mol, with the co-crystal ponatinib showing a binding affinity of −9.9 kcal/mol. Notably, twenty seven fungal metabolites had affinities ≤ -10.0 kcal/mol, surpassing ponatinib. Eight compounds, including Phellifuropyranone A and Meshimakobnol B, showed favorable drug-likeness. Molecular dynamics parameters, including RMSD, RMSF, Rg, and SASA, confirmed that Phellifuropyranone A and Meshimakobnol B bind stably to the T315I-BCR::ABL1 mutant protein. Additionally, PCA, DCCM, and free energy landscapes analyses validated the consistency of the molecular dynamics parameters. MM/PBSA analysis indicated that Phellifuropyranone A (–22.88 ± 4.28 kcal/mol) and Meshimakobnol B (−25.86 ± 3.51 kcal/mol) bind similarly to ponatinib (−25.54 ± 6.31 kcal/mol). Per-residue decomposition explored residues MET290, VAL299, ILE315, and PHE359 as crucial for binding to the T315I-BCR::ABL1 mutant protein. Phellifuropyranone A and Meshimakobnol B show significant potency as inhibitors of the T315I-BCR::ABL1 mutant protein, comparable to ponatinib. These compounds may serve as effective alternatives or synergistic agents with ponatinib, potentially overcoming drug resistance and improving treatment outcomes in Chronic Myeloid Leukemia.

Acute myeloid leukemia (AML) is a type of hematological malignancy with diverse genetic pathogenesis. Identification of the miR-93-5p targeted pathogenic markers could be useful for AML diagnosis and potential therapy. We collected 751 miR-93-5p targeted and AML-related genes by integrating the results of multiple databases and then used the expression profile of TCGA-LAML to construct a coexpression function network of AML WGCNA. Based on the clinical phenotype and module trait relationship, we identified two modules (brown and yellow) as interesting dysfunction modules, which have a significant association with cytogenetics risk and FAB classification systems. GO enrichment and KEGG analysis showed that these modules are mainly involved with cancer-associated pathways, including MAPK signal pathway, p53 signal pathway, JAK-STAT signal pathway, TGF-beta signaling pathway, mTOR signaling pathway, VEGF signaling pathway, both associated with the occurrence of AML. Besides, using the STRING database, we discovered the top 10 hub genes in each module, including MAPK1, ACTB, RAC1, GRB2, MDM2, ACTR2, IGF1R, CDKN1A, YWHAZ, and YWHAB in the brown module and VEGFA, FGF2, CCND1, FOXO3, IGFBP3, GSF1, IGF2, SLC2A4, PDGFBM, and PIK3R2 in the yellow module. The prognosis analysis result showed that six key pathogens have significantly affected the overall survival and prognosis in AML. Interestingly, VEGF with the most significant regulatory relationship in the yellow modules significantly positively correlated with the clinical phenotype of AML. We used qPCR and ELISA to verify miR-93-5p and VEGF expression in our clinical samples. The results exhibited that miR-93-5p and VEGF were both highly expressed in AML.

Acquired pure red cell aplasia (aPRCA) is a rare hematological syndrome characterized by anemia and a significant reduction in erythroid progenitor cells. Immunosuppressive therapy (IST), including Corticosteroids (CS), Cyclosporine (CsA), and cyclophosphamide (CYC), is the primary treatment. However, variations in clinical efficacy and limited comparative studies have created uncertainty in therapeutic choices. This study aims to evaluate the efficacy of IST and the factors influencing treatment outcomes. A systematic search was conducted using PubMed, Embase, Cochrane Library, and Web of Science. Two researchers independently screened studies and extracted data. The quality of studies was assessed using the MINORS scale. Meta-analysis was performed using STATA/MP16, and effect size (ES) was calculated using fixed- or random-effects models based on heterogeneity. A total of 33 studies involving 1,193 patients were included. The overall efficacy of IST was significant, with a pooled ES of 0.656 (95% CI: 0.600–0.710). CsA demonstrated the highest efficacy (ES = 0.699; 95% CI: 0.615–0.779), followed by CYC (ES = 0.592; 95% CI: 0.423–0.752) and CS (ES = 0.568; 95% CI: 0.457–0.676). Subgroup analyses revealed that factors such as etiology, combination therapies, first- vs. second-line treatment, and genetic characteristics significantly influenced outcomes. Notably, the response to IST was higher in primary aPRCA (ES = 0.667; 95% CI: 0.598–0.733) compared to LGLL-associated (ES = 0.515; 95% CI: 0.393–0.637) and thymoma-associated (ES = 0.690; 95% CI: 0.492–0.864) aPRCA. The combination of CS and CsA yielded superior efficacy (ES = 0.761; 95% CI: 0.658–0.853) compared to combination of CS and CsA and monotherapy. First-line treatment demonstrated better efficacy than second-line treatment (ES = 0.659; 95% CI: 0.596–0.720) vs. (ES = 0.452; 95% CI: 0.199–0.715). The important finding was that (ES = 0.861; 95% CI: 0.595–1.000) in the STAT3 mutation (+) group and (ES = 0.375; 95% CI: 0.034–0.801) in the STAT3 mutation (-) group. IST demonstrates overall efficacy in aPRCA, with variations influenced by etiology, drug combinations, and genetic mutations such as STAT3. These findings highlight the need for personalized treatment strategies and further research to validate and optimize IST efficacy.

Large granular lymphocyte leukemia (LGLL) is a rare lymphoproliferative disorder where somatic STAT3 mutation is common. Although LGLL has been described as an underlying condition associated with pure red cell aplasia (PRCA), the clinical characteristics and therapeutic response of LGLL − associated PRCA are largely unclear. We evaluated a set of 81 patients with LGLL − associated PRCA. Comparative analysis was performed on the clinical characteristics, responses to immunosuppressive therapy, and survival outcomes in patients with STAT3 mutation. Among the 81 LGLL − associated PRCA patients, 21 cases (26%) were STAT3 mutant, and 60 were wild − type. Of 21 patients with STAT3 mutation, 15 cases (71%) were positive for exon 21 mutation, 4 cases (19%) for exon 20 mutation, one for dual mutation in exon 20 and 21, and one for exon 13 mutation. The Y640F was the most commonly detected mutation (42.9%). Patients with STAT3 mutations had a higher percentage of reticulocytes (0.88% vs. 0.28%, P = 0.039) and red cell distribution width − coefficient of variation (18.8% vs. 15.8%, P = 0.008) compared to wild − type. Those with the STAT3 Y640F mutation had a younger median age at onset (44 years vs. 65 years, P = 0.007) and a higher peripheral blood lymphocyte ratio (63.7% vs. 34.4%, P = 0.033). The complete response rate (CRR) and overall response rate (ORR) of STAT3 mutated patients treated with cyclosporine (CsA) were 31.3% (5/16) and 56.3% (9/16), respectively, with no difference compared to the STAT3 wild − type (32.8%, 50%) (P = 0.909; P = 0.658). Although no statistical significance was found, the CRR and ORR of the CP regimen (consisted of cyclophosphamide and prednisone) were higher than CsA among STAT3 mutated individuals (53.8% vs. 31.3%, P = 0.274; 84.6% vs. 56.3%, P = 0.130). Reduction or discontinuation of immunosuppressive agents was the main cause of relapse. The relapse rate of the CP regimen was lower than CsA in this whole cohort (24.0% vs. 68.4%, P = 0.001), as well as in the STAT3 mutant group (18.2% vs. 77.8%, P = 0.022). STAT3 Y640F was the most common hotspot mutation in LGLL − associated PRCA. Patients with STAT3 mutation treated with CsA showed comparable responses to wild − type. CP regimen had a lower relapse rate and could be considered as a salvage therapy after CsA failure.