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. 2025 May;66(2):347-362.
doi: 10.1007/s13353-024-00881-0. Epub 2024 Jul 9.

Identifying key genes and functionally enriched pathways in acute myeloid leukemia by weighted gene co-expression network analysis

Jimo Jian  1   2 , Chenglu Yuan  1 , Hongyuan Hao  3
Affiliations

Identifying key genes and functionally enriched pathways in acute myeloid leukemia by weighted gene co-expression network analysis

Jimo Jian et al. J Appl Genet. 2025 May.

Abstract

Acute myeloid leukemia (AML) is characterized by the uncontrolled proliferation of myeloid leukemia cells in the bone marrow and other hematopoietic tissues and is highly heterogeneous. While with the progress of sequencing technology, understanding of the AML-related biomarkers is still incomplete. The purpose of this study is to identify potential biomarkers for prognosis of AML. Based on WGCNA analysis of gene mutation expression, methylation level distribution, mRNA expression, and AML-related genes in public databases were employed for investigating potential biomarkers for the prognosis of AML. This study screened a total of 6153 genes by analyzing various changes in 103 acute myeloid leukemia (AML) samples, including gene mutation expression, methylation level distribution, mRNA expression, and AML-related genes in public databases. Moreover, seven AML-related co-expression modules were mined by WGCNA analysis, and twelve biomarkers associated with the AML prognosis were identified from each top 10 genes of the seven co-expression modules. The AML samples were then classified into two subgroups, the prognosis of which is significantly different, based on the expression of these twelve genes. The differentially expressed 7 genes of two subgroups (HOXB-AS3, HOXB3, SLC9C2, CPNE8, MEG8, S1PR5, MIR196B) are mainly involved in glucose metabolism, glutathione biosynthesis, small G protein-mediated signal transduction, and the Rap1 signaling pathway. With the utilization of WGCNA mining, seven gene co-expression modules were identified from the TCGA database, and there are unreported genes that may be potential driver genes of AML and may be the direction to identify the possible molecular signatures to predict survival of AML patients and help guide experiments for potential clinical drug targets.

Keywords: Acute myeloid leukemia (AML); Biomarker; Prognosis; TCGA (The Cancer Genome Atlas); Weight gene co-expression network analysis (WGCNA).

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Conflict of interest statement

Declarations. Ethics approval: Since this was a planning study without any delivery to any patient, there was no ethical approval required for this study. Consent for publication: That the article is original, has already been published in a preprint copy https://doi.org/10.21203/rs.3.rs-29046/v1 ( https://doi.org/10.21203/rs.3.rs-29046/v1 ), and is not currently under consideration by another journal. Competing interests: The authors declare no competing interests.

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