Bioinformatics Analysis Identifies Key Genes and Pathways in Acute Myeloid Leukemia Associated with DNMT3A Mutation

Cuproptosis-Related Genes MTF1 and LIPT1 as Novel Prognostic Biomarker in Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is a life-threatening hematologic malignant disease with high morbidity and mortality in both adults and children. Cuproptosis, a novel mode of cell death, plays an important role in tumor development, but the functional mechanisms of cuproptosis-related genes (CRGs) in AML are unclear. The differential expression of CRGs between tumors such as AML and normal tissues in UCSC XENA, TCGA and GTEx was verified using R (version: 3.6.3). Lasso regression, Cox regression and Nomogram were used to screen for prognostic biomarkers of AML and to construct corresponding prognostic models. Kaplan-Meier analysis, ROC analysis, clinical correlation analysis, immune infiltration analysis and enrichment analysis were used to further investigate the correlation and functional mechanisms of CRGs with AML. The ceRNA regulatory network was used to identify the mRNA-miRNA-lncRNA regulatory axis. Cuproptosis-related genes LIPT1, MTF1, GLS and CDKN2A were highly expressed in AML, while FDX1, LIAS, DLD, DLAT, PDHA1, SLC31A1 and ATP7B were lowly expressed in AML. Lasso regression, Cox regression, Nomogram and calibration curve finally identified MTF1 and LIPT1 as two novel prognostic biomarkers of AML and constructed the corresponding prognostic models. In addition, all 12 CRGs had predictive power for AML, with MTF1, LIAS, SLC31A1 and CDKN2A showing more reliable results. Further analysis showed that ATP7B was closely associated with mutation types such as FLT3, NPM1, RAS and IDH1 R140 in AML, while the expression of MTF1, LIAS and ATP7B in AML was closely associated with immune infiltration. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis (GSEA) revealed that biological functions such as metal ion transmembrane transporter activity, haptoglobin binding and oxygen carrier activity, pathways such as interferon alpha response, coagulation, UV response DN, apoptosis, hypoxia and heme metabolism all play a role in the development of AML. The ceRNA regulatory network revealed that 6 lncRNAs such as MALAT1, interfere with MTF1 expression through 6 miRNAs such as hsa-miR-32-5p, which in turn affect the development and progression of AML. In addition, APTO-253 has the potential to become an AML-targeted drug. The cuproptosis-related genes MTF1 and LIPT1 can be used as prognostic biomarkers in AML. A total of six lncRNAs, including MALAT1, are involved in the expression and regulation of MTF1 in AML through six miRNAs such as hsa-miR-32-5p.

Epigenetic regulation in hematopoiesis and its implications in the targeted therapy of hematologic malignancies

Hematologic malignancies are one of the most common cancers, and the incidence has been rising in recent decades. The clinical and molecular features of hematologic malignancies are highly heterogenous, and some hematologic malignancies are incurable, challenging the treatment, and prognosis of the patients. However, hematopoiesis and oncogenesis of hematologic malignancies are profoundly affected by epigenetic regulation. Studies have found that methylation-related mutations, abnormal methylation profiles of DNA, and abnormal histone deacetylase expression are recurrent in leukemia and lymphoma. Furthermore, the hypomethylating agents and histone deacetylase inhibitors are effective to treat acute myeloid leukemia and T-cell lymphomas, indicating that epigenetic regulation is indispensable to hematologic oncogenesis. Epigenetic regulation mainly includes DNA modifications, histone modifications, and noncoding RNA-mediated targeting, and regulates various DNA-based processes. This review presents the role of writers, readers, and erasers of DNA methylation and histone methylation, and acetylation in hematologic malignancies. In addition, this review provides the influence of microRNAs and long noncoding RNAs on hematologic malignancies. Furthermore, the implication of epigenetic regulation in targeted treatment is discussed. This review comprehensively presents the change and function of each epigenetic regulator in normal and oncogenic hematopoiesis and provides innovative epigenetic-targeted treatment in clinical practice.

Investigation of distinct gene expression profile patterns that can improve the classification of intermediate-risk prognosis in AML patients

Background: Acute myeloid leukemia (AML) is a heterogeneous type of blood cancer that generally affects the elderly. AML patients are categorized with favorable-, intermediate-, and adverse-risks based on an individual's genomic features and chromosomal abnormalities. Despite the risk stratification, the progression and outcome of the disease remain highly variable. To facilitate and improve the risk stratification of AML patients, the study focused on gene expression profiling of AML patients within various risk categories. Therefore, the study aims to establish gene signatures that can predict the prognosis of AML patients and find correlations in gene expression profile patterns that are associated with risk groups. Methods: Microarray data were obtained from Gene Expression Omnibus (GSE6891). The patients were stratified into four subgroups based on risk and overall survival. Limma was applied to screen for differentially expressed genes (DEGs) between short survival (SS) and long survival (LS). DEGs strongly related to general survival were discovered using Cox regression and LASSO analysis. To assess the model's accuracy, Kaplan-Meier (K-M) and receiver operating characteristic (ROC) were used. A one-way ANOVA was performed to assess for differences in the mean gene expression profiles of the identified prognostic genes between the risk subcategories and survival. GO and KEGG enrichment analyses were performed on DEGs. Results: A total of 87 DEGs were identified between SS and LS groups. The Cox regression model selected nine genes CD109, CPNE3, DDIT4, INPP4B, LSP1, CPNE8, PLXNC1, SLC40A1, and SPINK2 that are associated with AML survival. K-M illustrated that the high expression of the nine-prognostic genes is associated with poor prognosis in AML. ROC further provided high diagnostic efficacy of the prognostic genes. ANOVA also validated the difference in gene expression profiles of the nine genes between the survival groups, and highlighted four prognostic genes to provide novel insight into risk subcategories poor and intermediate-poor, as well as good and intermediate-good that displayed similar expression patterns. Conclusion: Prognostic genes can provide more accurate risk stratification in AML. CD109, CPNE3, DDIT4, and INPP4B provided novel targets for better intermediate-risk stratification. This could enhance treatment strategies for this group, which constitutes the majority of adult AML patients.

Weighted gene co-expression network analysis identifies key hub genes and pathways in acute myeloid leukemia

Introduction: Acute myeloid leukemia (AML) is the most common type of leukemia in adults. However, there is a gap in understanding the molecular basis of the disease, partly because key genes associated with AML have not been extensively explored. In the current study, we aimed to identify genes that have strong association with AML based on a cross-species integrative approach. Methods: We used Weighted Gene Co-Expression Network Analysis (WGCNA) to identify co-expressed gene modules significantly correlated with human AML, and further selected the genes exhibiting a significant difference in expression between AML and healthy mouse. Protein-protein interactions, transcription factors, gene function, genetic regulation, and coding sequence variants were integrated to identify key hub genes in AML. Results: The cross-species approach identified a total of 412 genes associated with both human and mouse AML. Enrichment analysis confirmed an association of these genes with hematopoietic and immune-related functions, phenotypes, processes, and pathways. Further, the integrated analysis approach identified a set of important module genes including Nfe2, Trim27, Mef2c, Ets1, Tal1, Foxo1, and Gata1 in AML. Six of these genes (except ETS1) showed significant differential expression between human AML and healthy samples in an independent microarray dataset. All of these genes are known to be involved in immune/hematopoietic functions, and in transcriptional regulation. In addition, Nfe2, Trim27, Mef2c, and Ets1 harbor coding sequence variants, whereas Nfe2 and Trim27 are cis-regulated, making them attractive candidates for validation. Furthermore, subtype-specific analysis of the hub genes in human AML indicated high expression of NFE2 across all the subtypes (M0 through M7) and enriched expression of ETS1, LEF1, GATA1, and TAL1 in M6 and M7 subtypes. A significant correlation between methylation status and expression level was observed for most of these genes in AML patients. Conclusion: Findings from the current study highlight the importance of our cross-species approach in the identification of multiple key candidate genes in AML, which can be further studied to explore their detailed role in leukemia/AML.

A Novel Risk Signature with Seven Pyroptosis-Related Genes for Prognosis Prediction in Glioma

BACKGROUND

Increasing evidence indicates that pyroptosis is closely linked to the occurrence and progression of cancer. However, the expression and prognostic role of most pyroptosis-related genes in glioma have not been fully elucidated.

METHODS

Herein, we explored the expression profiles and prognostic value of 33 pyroptosis-related genes in glioma. LASSO regression analysis was then used to construct a risk signature to predict glioma outcomes in The Cancer Genome Atlas (TCGA) cohort. Furthermore, we constructed a nomogram based on independent prognostic factors and performed external validation. Finally, functional enrichment analysis was performed to explore the potential biological role of the pyroptosis-related signature in glioma.

RESULTS

The expression of most pyroptosis-related genes (31/33) was significantly different between normal brain and glioma tissue. By univariate Cox regression analysis, 24 genes were found to be significantly correlated with glioma overall survival (OS). Subsequently, we constructed a 7-gene risk signature in the TCGA training cohort, which demonstrated good performance in predicting glioma survival through multidatabase validation. Moreover, a nomogram was established based on independent prognostic factors (age, WHO grade, IDH status and signature) and confirmed to be more effective and accurate through internal evaluation and external validation. Finally, functional enrichment analyses suggested that the signature might be related to invasion ability and immune function.

CONCLUSIONS

The risk signature based on seven pyroptosis-related genes can effectively predict the clinical outcomes of glioma patients. Our study provides novel insights for further understanding the association between pyroptosis-related genes and glioma prognosis.