Investigation of distinct gene expression profile patterns that can improve the classification of intermediate-risk
Nasr Eshibona1, Michelle Livesey1, Alan Christoffels1
1SAMRC Bioinformatics Unit, South African National Bioinformatics Institute, University of The Western Cape, Cape Town, South Africa.
Abstract:
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.
