Jian Jimo, Yuan Chenglu, Hao Hongyuan
Qilu Hospital of Shandong University, Qingdao, 266035, Shandong, China.
Qilu Hospital of Shandong University, Jinan, 250012, Shandong, China.
J Appl Genet. 2025 May;66(2):347-362. doi: 10.1007/s13353-024-00881-0. Epub 2024 Jul 9.
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.
急性髓系白血病（AML）的特征是骨髓和其他造血组织中髓系白血病细胞的不受控制增殖，且具有高度异质性。虽然随着测序技术的进步，但对AML相关生物标志物的了解仍不完整。本研究的目的是识别AML预后的潜在生物标志物。基于基因共表达网络分析（WGCNA）对基因突变表达、甲基化水平分布、mRNA表达进行分析，并利用公共数据库中的AML相关基因来研究AML预后的潜在生物标志物。本研究通过分析103例急性髓系白血病（AML）样本中的各种变化，包括基因突变表达、甲基化水平分布、mRNA表达以及公共数据库中的AML相关基因，共筛选出6153个基因。此外，通过WGCNA分析挖掘出7个与AML相关的共表达模块，并从这7个共表达模块的前10个基因中分别鉴定出12个与AML预后相关的生物标志物。然后根据这12个基因的表达情况，将AML样本分为两个亚组，其预后有显著差异。两个亚组差异表达的7个基因（HOXB-AS3、HOXB3、SLC9C2、CPNE8、MEG8、S1PR5、MIR196B）主要参与葡萄糖代谢、谷胱甘肽生物合成、小G蛋白介导的信号转导以及Rap1信号通路。利用WGCNA挖掘技术，从TCGA数据库中鉴定出7个基因共表达模块，其中存在未报道的基因，这些基因可能是AML的潜在驱动基因，可能是识别预测AML患者生存的可能分子特征以及帮助指导潜在临床药物靶点实验的方向。
