Eshibona Nasr, Livesey Michelle, Christoffels Alan, Bendou Hocine
SAMRC Bioinformatics Unit, South African National Bioinformatics Institute, University of The Western Cape, Cape Town, South Africa.
Front Genet. 2023 Feb 14;14:1131159. doi: 10.3389/fgene.2023.1131159. eCollection 2023.
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. 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. 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. 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.
急性髓系白血病（AML）是一种异质性血液癌症，通常影响老年人。AML患者根据个体的基因组特征和染色体异常被分为低危、中危和高危类别。尽管进行了风险分层，但该疾病的进展和结果仍然高度可变。为了促进和改善AML患者的风险分层，该研究聚焦于不同风险类别的AML患者的基因表达谱分析。因此，该研究旨在建立能够预测AML患者预后的基因特征，并找出与风险组相关的基因表达谱模式中的相关性。微阵列数据取自基因表达综合数据库（GSE6891）。患者根据风险和总生存期被分为四个亚组。使用Limma筛选短生存期（SS）和长生存期（LS）之间的差异表达基因（DEG）。使用Cox回归和LASSO分析发现与总体生存期密切相关的DEG。为评估模型的准确性，使用了Kaplan-Meier（K-M）和受试者工作特征（ROC）分析。进行单因素方差分析以评估已识别的预后基因在风险亚类别和生存期之间的平均基因表达谱差异。对DEG进行基因本体（GO）和京都基因与基因组百科全书（KEGG）富集分析。在SS组和LS组之间共鉴定出87个DEG。Cox回归模型选择了9个与AML生存期相关的基因，即CD109、CPNE3、DDIT4、INPP4B、LSP1、CPNE8、PLXNC1、SLC40A1和SPINK2。K-M分析表明，这9个预后基因的高表达与AML的不良预后相关。ROC分析进一步证明了这些预后基因具有较高的诊断效能。方差分析也验证了这9个基因在生存期组之间的基因表达谱差异，并突出了4个预后基因，为低危和中低危以及良好和中高危亚组提供了新的见解，这些亚组表现出相似的表达模式。预后基因可以为AML提供更准确的风险分层。CD109、CPNE3、DDIT4和INPP4B为更好地进行中危分层提供了新的靶点。这可以加强针对这一占成年AML患者大多数群体的治疗策略。
