Identification of hub genes and its correlation with the prognosis of acute myeloid leukemia based on high‐throughput data analysis

Abstract

Abstract Objective Acute myeloid leukemia (AML) is one of the most common forms of leukemia in the world, but its molecular mechanism is still not well understood. The aim of this study was, by using multiple AML datasets, to obtain genes with differential expression, and to identify key genes in the development and progression of AML. Methods The AML microarray dataset (GSE24395, GSE30029, GSE38865, GSE90062) was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified and functionally enriched, and a protein–protein interaction network was constructed. Cytoscape was used for module analysis to obtain fundamental genes (hub genes), and combined with The Cancer Genome Atlas clinical data for survival and expression analysis. Results A total of 134 DEGs were identified (fold change >1, P < 0.01). Gene enrichment analysis included positive regulation of RNA polymerase II promoter transcriptions (c = 17.967, P = 0.011), neutrophil degranulation (c = 18.625, P = 0.017), integrin‐mediated cells (c = 17.862, P = 0.017), adhesion regulation, neutrophil‐mediated immunity (c = 17.624, P = 0.017), transcriptional disorders in cancer, (c = 14.786, P = 00.031). A total of 16 hub genes were identified. Based on The Cancer Genome Atlas clinical data expression analysis, CYBB (t = 0.368, P = 0.012) and CYFIP2 (t = 2.097, P = 0.038) were abnormally expressed in different living conditions of AML. Survival analysis showed that SERPINE1 (P = 0.031) and ITGAM (P = 0.049) might be involved in invasion or recurrence of AML. Conclusion The analysis of DEGs and hub analysis of genes can contribute to understanding the molecular mechanism of AML occurrence and progression, and provides a candidate for the treatment and prognosis of AML.

Keywords

• acute myeloid leukemia
• high‐throughput data
• hub genes
• prognosis