Dataset Information

Identification of six hub genes and analysis of their correlation with drug sensitivity in acute myeloid leukemia through bioinformatics

ABSTRACT:

Background

Acute myeloid leukemia (AML) is a common hematopoietic malignancy and have unsatisfactory prognosis. Our study aimed to identify hub genes in AML and explore potential biomarkers through integrated bioinformatics.

Methods

Microarray datasets were analyzed to screen the differentially expressed genes (DEGs). Functional enrichment analysis was performed, and protein-protein interaction (PPI) network was generated by the STRING (11.0) database and Cytoscape (3.7.2) software. Hub genes were screened and verified through GEPIA2 and GEO microarray database. Sensitivity of AML cell lines with high expression of hub genes to the small-molecule drugs were identified using GSCA Lite.

Results

A total of 456 DEGs were identified and top 100 genes were screened out, of which six genes (FLT3, PF4, CD163, MRC1, CSF2RB, PPBP) were upregulated in AML and individually had a worse prognosis by the overall survival (OS) analysis. AML cell lines with FLT3-overexpression and CSF2RB-overexpression were sensitive to most small-molecule drugs, while, AML cells with CD163-overexpression were only sensitive to a few drugs. However, sensitivity to Erlotinib was correlated with high expression of PF4 and PPBP.

Conclusions

In summary, FLT3, PF4, CD163, MRC1, CSF2RB, PPBP may be potential biomarkers and potential sensitive small-molecule drugs were correlated with overexpression of the biomarkers in AML.

SUBMITTER: Cai D

PROVIDER: S-EPMC8798452 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background Chronic myeloid leukemia (CML) is a malignant hyperplastic tumor of the bone marrow originating from pluripotent hematopoietic stem cells. The advent of tyrosine kinase inhibitors (TKIs) has greatly improved the survival rate of patients with CML. However, TKI-resistance leads to the disease recurrence and progression. This study aimed to identify immune-related genes (IRGs) associated with CML progression. Methods We extracted the gene’s expression profiles from the Gene Expression Omnibus (GEO). Bioinformatics analysis was used to determine the differentially expressed IRGs of CML and normal peripheral blood mononuclear cells (PBMCs). Functional enrichment and gene set enrichment analysis (GSEA) were used to explore its potential mechanism. Hub genes were identified using Molecular Complex Detection (MCODE) and the CytoHubba plugin. The hub genes’ diagnostic value was evaluated using the receiver operating characteristic (ROC). The relative proportions of infiltrating immune cells in each CML sample were evaluated using CIBERSORT. Quantitative real-time PCR (RT-qPCR) was used to validate the hub gene expression in clinical samples. Results A total of 31 differentially expressed IRGs were identified. GO analyses revealed that the modules were typically enriched in the receptor ligand activity, cytokine activity, and endopeptidase activity. KEGG enrichment analysis of IRGs revealed that CML involved Th17 cell differentiation, the NF-kappa B signaling pathway, and cytokine-cytokine receptor interaction. A total of 10 hub genes were selected using the PPI network. GSEA showed that these hub genes were related to the gamma-interferon immune response, inflammatory response, and allograft rejection. ROC curve analysis suggested that six hub genes may be potential biomarkers for CML diagnosis. Further analysis indicated that immune cells were associated with the pathogenesis of CML. The RT-qPCR results showed that proteinase 3 (PRTN3), cathepsin G (CTSG), matrix metalloproteinase 9 (MMP9), resistin (RETN), eosinophil derived neurotoxin (RNase2), eosinophil cationic protein (ECP, RNase3) were significantly elevated in CML patients’ PBMCs compared with healthy controls. Conclusion These results improved our understanding of the functional characteristics and immune-related molecular mechanisms involved in CML progression and provided potential diagnostic biomarkers and therapeutic targets.