Weighted gene co-expression network analysis identifies key hub genes and pathways in acute myeloid leukemia

Abstract

Introduction: Acute myeloid leukemia (AML) is the most common type of leukemia in adults. However, there is a gap in understanding the molecular basis of the disease, partly because key genes associated with AML have not been extensively explored. In the current study, we aimed to identify genes that have strong association with AML based on a cross-species integrative approach.Methods: We used Weighted Gene Co-Expression Network Analysis (WGCNA) to identify co-expressed gene modules significantly correlated with human AML, and further selected the genes exhibiting a significant difference in expression between AML and healthy mouse. Protein-protein interactions, transcription factors, gene function, genetic regulation, and coding sequence variants were integrated to identify key hub genes in AML.Results: The cross-species approach identified a total of 412 genes associated with both human and mouse AML. Enrichment analysis confirmed an association of these genes with hematopoietic and immune-related functions, phenotypes, processes, and pathways. Further, the integrated analysis approach identified a set of important module genes including Nfe2, Trim27, Mef2c, Ets1, Tal1, Foxo1, and Gata1 in AML. Six of these genes (except ETS1) showed significant differential expression between human AML and healthy samples in an independent microarray dataset. All of these genes are known to be involved in immune/hematopoietic functions, and in transcriptional regulation. In addition, Nfe2, Trim27, Mef2c, and Ets1 harbor coding sequence variants, whereas Nfe2 and Trim27 are cis-regulated, making them attractive candidates for validation. Furthermore, subtype-specific analysis of the hub genes in human AML indicated high expression of NFE2 across all the subtypes (M0 through M7) and enriched expression of ETS1, LEF1, GATA1, and TAL1 in M6 and M7 subtypes. A significant correlation between methylation status and expression level was observed for most of these genes in AML patients.Conclusion: Findings from the current study highlight the importance of our cross-species approach in the identification of multiple key candidate genes in AML, which can be further studied to explore their detailed role in leukemia/AML.

5-methylcytosine RNA modification regulators-based patterns and features of immune microenvironment in acute myeloid leukemia

“…Network analysis of various molecular and regulatory factors [ 49 , 50 ] is a strong and widely used approach for exploring the underlying mechanisms of any disease. It has not only been used in AML research [ 51 – 53 ] but also for several other diseases and physiological conditions [ 54 – 56 ]. In the current study, we constructed protein-protein interaction network and revealed the close relationships among the m5C regulators.…”

Identification of two key biomarkers CD93 and FGL2 associated with survival of acute myeloid leukaemia by weighted gene co‐expression network analysis

“…found that the hub gene CEACAM5 is significantly associated with the prognosis of AML and can serve as a potential target for AML treatment. Wang et al 20 . identified a group of hub genes ( NFE2 , TRIM27 , MEF2C , ETS1 , TAL1 , FOXO1 and GATA1 ) and pathways in AML using WGCNA.…”

“…The weighted gene co‐expression network analysis (WGCNA) is a compelling method that utilizes big data to detect synergistically expressed genes related to clinical phenotypes rather than focusing on a single gene 17 . WGCNA has recently become widely used in various diseases, including AML, to identify target hub genes 18–21 . However, there is almost no overlap among these genes, and their mechanisms of action are largely unknown.…”

“…17 WGCNA has recently become widely used in various diseases, including AML, to identify target hub genes. [18][19][20][21] However, there is almost no overlap among these genes, and their mechanisms of action are largely unknown. Therefore, it is necessary to obtain more reliable targets related to AML by integrating multi-omics data.…”