High expression of LOC541471, GDAP1, SOD1, and STK25 is associated with poor overall survival of patients with acute myeloid leukemia

Delivery Aspects for Implementing siRNA Therapeutics for Blood Diseases

Hematological disorders result in significant health consequences, and traditional therapies frequently entail adverse reactions without addressing the root cause. A potential solution for hematological disorders characterized by gain-of-function mutations lies in the emergence of small interfering RNA (siRNA) molecules as a therapeutic option. siRNAs are a class of RNA molecules composed of double-stranded RNAs that can degrade specific mRNAs, thereby inhibiting the synthesis of underlying disease proteins. Therapeutic interventions utilizing siRNA can be tailored to selectively target genes implicated in diverse hematological disorders, including sickle cell anemia, β-thalassemia, and malignancies such as lymphoma, myeloma, and leukemia. The development of efficient siRNA silencers necessitates meticulous contemplation of variables such as the RNA backbone, stability, and specificity. Transportation of siRNA to specific cells poses a significant hurdle, prompting investigations of diverse delivery approaches, including chemically modified forms of siRNA and nanoparticle formulations with various biocompatible carriers. This review delves into the crucial role of siRNA technology in targeting and treating hematological malignancies and disorders. It sheds light on the latest research, development, and clinical trials, detailing how various pharmaceutical approaches leverage siRNA against blood disorders, mainly concentrating on cancers. It outlines the preferred molecular targets and physiological barriers to delivery while emphasizing the growing potential of various therapeutic delivery methods. The need for further research is articulated in the context of overcoming the shortcomings of siRNA in order to enrich discussions around siRNA's role in managing blood disorders and aiding the scientific community in advancing more targeted and effective treatments.

Epigenetic regulation of the human GDAP1 gene

Mutations in the ganglioside-induced differentiation-associated protein 1 (GDAP1) gene are linked to Charcot-Marie-Tooth (CMT) disease, a hereditary neurodegenerative condition. The protein encoded by this gene is involved in mitochondrial fission and calcium homeostasis. Recently, GDAP1 has also been implicated in the survival of patients with certain cancers. Despite its significant role in specific cellular processes and associated diseases, the mechanisms regulating GDAP1 expression are largely unknown. Here, we show for the first time that methylation of the CpG island in the proximal promoter of the GDAP1 gene inhibits its activity. Treating cells with low GDAP1 expression using methyltransferase and HDAC inhibitors induced the expression of this gene and its encoded protein. This induction was associated with promoter demethylation and increased association of acetylated histones with the GDAP1 promoter. Thus, we identified a mechanism that could be used to manipulate GDAP1 expression.

The role of long noncoding RNAs in the diagnosis, prognosis and therapeutic biomarkers of acute myeloid leukemia

Acute myeloid leukemia (AML) is the abnormal proliferation of immature myeloid blast cells in the bone marrow. Currently, there are no universally recognized biomarkers for the early diagnosis, prognosis and effective treatment of AML to improve the overall survival of patients. Recent studies, however, have demonstrated that long noncoding RNAs (lncRNAs) are promising targets for the early diagnosis, prognosis and treatment of AML. A critical review of available data would be important to identify study gaps and provide perspectives. In this review, we explored comprehensive information on the potential use of lncRNAs as targets for the diagnosis, prognosis, and treatment of AML. LncRNAs are nonprotein-coding RNAs that are approximately 200 nucleotides long and play important roles in the regulation, metabolism and differentiation of tissues. In addition, they play important roles in the diagnosis, prognosis and treatment of different cancers, including AML. LncRNAs play multifaceted roles as oncogenes or tumor suppressor genes. Recently, deregulated lncRNAs were identified as novel players in the development of AML, making them promising prognostic indicators. Given that lncRNAs could have potential diagnostic marker roles, the lack of sufficient evidence identifying specific lncRNAs expressed in specific cancers hampers the use of lncRNAs as diagnostic markers of AML. The complex roles of lncRNAs in the pathophysiology of AML require further scrutiny to identify specific lncRNAs. This review, despite the lack of sufficient literature, discusses the therapeutic, diagnostic and prognostic roles of lncRNAs in AML and provides future insights that will contribute to studies targeting lncRNAs in the diagnosis, treatment, and management of AML.

Exploring and clinical validation of prognostic significance and therapeutic implications of copper homeostasis-related gene dysregulation in acute myeloid leukemia

The patterns and biological functions of copper homeostasis-related genes (CHRGs) in acute myeloid leukemia (AML) remain unclear. We explored the patterns and biological functions of CHRGs in AML. Using independent cohorts, including TCGA-GTEx, GSE114868, GSE37642, and clinical samples, we identified 826 common differentially expressed genes. Specifically, 12 cuproptosis-related genes (e.g., ATP7A, ATP7B) were upregulated, while 17 cuproplasia-associated genes (e.g., ATOX1, ATP7A) were downregulated in AML. We used LASSO-Cox, Kaplan-Meier, and Nomogram analyses to establish prognostic risk models, effectively stratifying patients with AML into high- and low-risk groups. Subgroup analysis revealed that high-risk patients exhibited poorer overall survival and involvement in fatty acid metabolism, apoptosis, and glycolysis. Immune infiltration analysis indicated differences in immune cell composition, with notable increases in B cells, cytotoxic T cells, and memory T cells in the low-risk group, and increased monocytes and neutrophils in the high-risk group. Single-cell sequencing analysis corroborated the expression characteristics of critical CHRGs, such as MAPK1 and ATOX1, associated with the function of T, B, and NK cells. Drug sensitivity analysis suggested potential therapeutic agents targeting copper homeostasis, including Bicalutamide and Sorafenib. PCR validation confirmed the differential expression of 4 cuproptosis-related genes (LIPT1, SLC31A1, GCSH, and PDHA1) and 9 cuproplasia-associated genes (ATOX1, CCS, CP, MAPK1, SOD1, COA6, PDK1, DBH, and PDE3B) in AML cell line. Importantly, these genes serve as potential biomarkers for patient stratification and treatment. In conclusion, we shed light on the expression patterns and biological functions of CHRGs in AML. The developed risk models provided prognostic implications for patient survival, offering valuable information on the regulatory characteristics of CHRGs and potential avenues for personalized treatment in AML.

Identifying key genes and functionally enriched pathways in acute myeloid leukemia by weighted gene co-expression network analysis

Acute myeloid leukemia (AML) is characterized by the uncontrolled proliferation of myeloid leukemia cells in the bone marrow and other hematopoietic tissues and is highly heterogeneous. While with the progress of sequencing technology, understanding of the AML-related biomarkers is still incomplete. The purpose of this study is to identify potential biomarkers for prognosis of AML. Based on WGCNA analysis of gene mutation expression, methylation level distribution, mRNA expression, and AML-related genes in public databases were employed for investigating potential biomarkers for the prognosis of AML. This study screened a total of 6153 genes by analyzing various changes in 103 acute myeloid leukemia (AML) samples, including gene mutation expression, methylation level distribution, mRNA expression, and AML-related genes in public databases. Moreover, seven AML-related co-expression modules were mined by WGCNA analysis, and twelve biomarkers associated with the AML prognosis were identified from each top 10 genes of the seven co-expression modules. The AML samples were then classified into two subgroups, the prognosis of which is significantly different, based on the expression of these twelve genes. The differentially expressed 7 genes of two subgroups (HOXB-AS3, HOXB3, SLC9C2, CPNE8, MEG8, S1PR5, MIR196B) are mainly involved in glucose metabolism, glutathione biosynthesis, small G protein-mediated signal transduction, and the Rap1 signaling pathway. With the utilization of WGCNA mining, seven gene co-expression modules were identified from the TCGA database, and there are unreported genes that may be potential driver genes of AML and may be the direction to identify the possible molecular signatures to predict survival of AML patients and help guide experiments for potential clinical drug targets.

Identification of two key biomarkers CD93 and FGL2 associated with survival of acute myeloid leukaemia by weighted gene co-expression network analysis

Acute myeloid leukaemia (AML) is a biologically heterogeneous haematological malignancy. This study was performed to identify the potential biomarkers for the prognosis and treatment of AML. We applied weighted gene co-expression network analysis to identify key modules and hub genes related to the prognosis of AML using data from The Cancer Genome Atlas (TCGA). In total, 1581 differentially expressed genes (1096 upregulated and 485 downregulated) were identified between AML patients and healthy controls, with the blue module being the most significant among 14 modules associated with AML morphology. Through functional enrichment analysis, we identified 217 genes in the blue module significantly enriched in 'neutrophil degranulation' and 'neutrophil activation involved in immune response' pathways. The survival analysis revealed six genes (S100A9, S100A8, HK3, CD93, CXCR2 and FGL2) located in the significantly enriched pathway that were notably related to AML survival. We validated the expression of these six genes at gene and single-cell levels and identified methylation loci of each gene, except for S100A8. Finally, in vitro experiments were performed to demonstrate whether the identified hub genes were associated with AML survival. After knockdown of CD93 and FGL2, cell proliferation was significantly reduced in U937 cell line over 5 days. In summary, we identified CD93 and FGL2 as key hub genes related to AML survival, with FGL2 being a novel biomarker for the prognosis and treatment of AML.

Development and validation of a cuproptosis-related prognostic model for acute myeloid leukemia patients using machine learning with stacking

Our objective is to develop a prognostic model focused on cuproptosis, aimed at predicting overall survival (OS) outcomes among Acute myeloid leukemia (AML) patients. The model utilized machine learning algorithms incorporating stacking. The GSE37642 dataset was used as the training data, and the GSE12417 and TCGA-LAML cohorts were used as the validation data. Stacking was used to merge the three prediction models, subsequently using a random survival forests algorithm to refit the final model using the stacking linear predictor and clinical factors. The prediction model, featuring stacking linear predictor and clinical factors, achieved AUC values of 0.840, 0.876 and 0.892 at 1, 2 and 3 years within the GSE37642 dataset. In external validation dataset, the corresponding AUCs were 0.741, 0.754 and 0.783. The predictive performance of the model in the external dataset surpasses that of the model simply incorporates all predictors. Additionally, the final model exhibited good calibration accuracy. In conclusion, our findings indicate that the novel prediction model refines the prognostic prediction for AML patients, while the stacking strategy displays potential for model integration.

Identification of a Complex Karyotype Signature with Clinical Implications in AML and MDS-EB Using Gene Expression Profiling

Complex karyotype (CK) is associated with a poor prognosis in both acute myeloid leukemia (AML) and myelodysplastic syndrome with excess blasts (MDS-EB). Transcriptomic analyses have improved our understanding of the disease and risk stratification of myeloid neoplasms; however, CK-specific gene expression signatures have been rarely investigated. In this study, we developed and validated a CK-specific gene expression signature. Differential gene expression analysis between the CK and non-CK groups using data from 348 patients with AML and MDS-EB from four cohorts revealed enrichment of the downregulated genes localized on chromosome 5q or 7q, suggesting that haploinsufficiency due to the deletion of these chromosomes possibly underlies CK pathogenesis. We built a robust transcriptional model for CK prediction using LASSO regression for gene subset selection and validated it using the leave-one-out cross-validation method for fitting the logistic regression model. We established a 10-gene CK signature (CKS) predictive of CK with high predictive accuracy (accuracy 94.22%; AUC 0.977). CKS was significantly associated with shorter overall survival in three independent cohorts, and was comparable to that of previously established risk stratification models for AML. Furthermore, we explored of therapeutic targets among the genes comprising CKS and identified the dysregulated expression of superoxide dismutase 1 (SOD1) gene, which is potentially amenable to SOD1 inhibitors.