Alharbi Homood, Ahmad Mohammad, Cui Zhong, Meng Dong, Xin Ying, Yan Xues
Department of Medical-Surgical Nursing, College of Nursing, King Saud University, Riyadh, Saudi Arabia.
Department of Hematology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China.
Int J Lab Hematol. 2025 Apr;47(2):288-296. doi: 10.1111/ijlh.14410. Epub 2024 Dec 5.
In this study, clinical bioinformatics analysis was used to identify potential biomarkers of acute myeloid leukemia (AML) occurrence and development, drug resistance, and poor prognosis to provide a theoretical basis for the treatment of AML.
On the basis of the TCGA, GEO, and GTEx databases, an AML secondary database was established, and differential expression analysis and WGCNA were carried out to identify genes related to the prognosis of AML patients. Survival analysis was carried out for internal verification of key genes, and GEO data were used for external verification to obtain core genes related to prognosis. For differentially expressed genes, the EpiMed platform independently developed by the team was used for drug prediction.
A total of 36 overlapping genes were obtained via difference analysis and WGCNA. Enrichment analysis revealed that the overlapping genes were associated with neutrophil activation, transcription dysregulation, AML, apoptosis, and other biological indicators. A protein interaction network was constructed for NCOA4, ACSL4, DPP4, ATL1, MT1G, ALOX15, and SLC7A11, which are key genes. Survival analysis revealed that NCOA4, ACSL4, DPP4, and ATL1 significantly affected the survival of patients with AML. The GSE142698 dataset verified that MPO, BCL2A1, and STMN1 had a statistically significant impact on the survival of AML patients.
NCOA4, ACSL4, DPP4, and ATL1 may be potential biomarkers related to the survival and prognosis of patients with AML, and the calcineurin signaling pathway is associated with the risk of vascular fragility in AML patients, which can provide a reference for further research and optimization of treatment regimens.
本研究采用临床生物信息学分析方法，旨在识别急性髓系白血病（AML）发生发展、耐药性及预后不良的潜在生物标志物，为AML的治疗提供理论依据。
基于TCGA、GEO和GTEx数据库建立AML二级数据库，进行差异表达分析和加权基因共表达网络分析（WGCNA）以识别与AML患者预后相关的基因。对关键基因进行生存分析以进行内部验证，并使用GEO数据进行外部验证以获得与预后相关的核心基因。对于差异表达基因，使用团队自主研发的EpiMed平台进行药物预测。
通过差异分析和WGCNA共获得36个重叠基因。富集分析表明，这些重叠基因与中性粒细胞活化、转录失调、AML、细胞凋亡等生物学指标相关。构建了关键基因NCOA4、ACSL4、DPP4、ATL1、MT1G、ALOX15和SLC7A11的蛋白质相互作用网络。生存分析显示，NCOA4、ACSL4、DPP4和ATL1显著影响AML患者的生存。GSE142698数据集验证了MPO、BCL2A1和STMN1对AML患者的生存有统计学显著影响。
NCOA4、ACSL4、DPP4和ATL1可能是与AML患者生存和预后相关的潜在生物标志物，且钙调神经磷酸酶信号通路与AML患者血管脆性风险相关，可为进一步研究和优化治疗方案提供参考。
