Gene Expression Insights into Cytarabine Resistance in Acute Myeloid Leukemia: The Role of Cytokines

Abstract

Background/aim: Cytarabine is the main chemotherapy agent used to treat acute myeloid leukemia (AML), but drug resistance remains a major challenge. Imbalances in cytokine secretion are known to play a role in the survival and proliferation of AML blast cells. While numerous studies have investigated cytokine secretion in AML, the precise role of cytokines in the pathogenesis of AML remains unclear. This study aimed to compare cytokine-related gene expression in parental and resistant HL60 cells and assess their prognostic relevance in AML.

Materials and methods: This study compared gene expression profiles of parental HL60 and R-HL60 cells by analyzing gene expression arrays. Further, the correlation between the differential expression genes (DEGs) and overall survival in patients with AML was obtained from the Cancer Genome Atlas (TCGA) database, and the expression of genes was validated by quantitative PCR (QPCR).

Results: Our results showed that twenty-six genes involved in cytokine regulation were significantly different between HL60 and R-HL60 cells. The DEGs associated with cytokine production between parental HL60 and R-HL60 was subjected to a functional enrichment analysis by ShinyGO. The expression of BCL3, OAS3, RELB, AIM2, NFKB2, CLEC9A, and OAS2 involved in the regulation of cytokine production was associated with survival probability of AML patient in the TCGA database. Among them, the expression of BCL3, OAS3, RELB, AIM2, and NFKB2 genes in R-HL60 cells was higher than that in HL-60 cells.

Conclusion: This study identified key genes involved in cytokine dysregulation in cytarabine-resistant HL60 cells, providing potential targets for overcoming drug resistance in AML. These findings offer new avenues for the development of more effective therapies for relapsed or refractory AML patients.

Keywords: Acute myeloid leukemia; HL60; cytarabine-resistance; cytokine production.

Figure 1

The flow chart for bioinformatics analysis of gene expression array. The DEGs related to cytokine production between parental HL60 and R-HL60 were analyzed for functional enrichment using ShinyGO 0.76. The correlation between the DEGs and overall survival in patients with AML was explored using data from the TCGA database and analyzed according to the Kaplan-Meier plot. Then, the expression of the candidate genes was confirmed through QPCR. DEGs: Differentially expressed genes; TCGA: the Cancer Genome Atlas; QPCR: quantitative PCR.

Figure 2

Functional enrichment of differentially expressed genes identified between parental HL60 cells and cytarabine-resistant HL60 cells. (A) Heatmap of the top 10 significantly enriched GO_BP terms of candidate differentially expressed genes. (B) Histogram of the top 10 significantly enriched GO_BP terms of candidate differentially expressed genes. (C) A hierarchical clustering tree summarizing the correlations among the top 10 significantly enriched GO_BP terms. GO_BP terms with a high percentage of shared gene members are clustered together. Three GO_BP terms, including cytokine production, regulation of cytokine production, negative regulation of cytokine production was clustered in the same cluster, termed as regulation of cytokine production pathways in this study. Larger dots indicate more significant p-values. (D) An interactive graph revealed the relationship among the top 10 significantly enriched GO_BP terms. Two pathways (nodes) were connected if they share greater than or equal to 20% of gene members. Darker nodes represented more significantly enriched pathways. Larger nodes represented bigger gene sets. Thicker edges represented more overlapped genes.

Figure 3

Twenty-six DEGs were found to be involved in the regulation of cytokine production. (A) Heatmap showing the hierarchical clustering of 26 DEGs associated with regulation of cytokine production between HL60 and R-HL60 cells. (B) Twenty-six DEGs participating regulation of cytokine production pathways were performed the network cluster analysis of protein–protein interaction using the STRING software. DEGs: Differentially expressed genes.

Figure 4

Correlation between gene expression and overall survival in patients with AML. Data was obtained from TCGA database and analyzed according to Kaplan-Meier plot. The TCGA database demonstrated that high expression levels of BCL3, OAS3, RELB, AIM2, NFKB2, CLEC9A, and OAS2 were significantly associated with poor survival curves in patients with AML. AML: Acute myeloid leukemia; TCGA: the Cancer Genome Atlas.

Figure 5

Validation of gene expression by QPCR. (A) Gene expression of BCL3, OAS3, RELB, AIM2, NFKB2, CLEC9A was validated by QPCR. Data are presented as mean±SEM (n=3). Statistical significance was determined using Student’s t-test. *p<0.05, **p<0.01. (B) Five DEGs involved in the regulation of cytokine production pathways and associated with poor survival outcomes in patients with AML were subjected to network cluster analysis of protein–protein interactions using the STRING software. QPCR: Quantitative PCR; DEGs: differentially expressed genes.