Xuan Fan, Zhu Wenyuan, Zhang Baoxi, Zhao Hui, Li Chaonan, Wu Xiaoli
Department of Pediatrics, Hematology and Oncology, Second Hospital of Hebei Medical University, Shijiazhuang, China.
Graduate School, Hebei Medical University, Shijiazhuang, Hebei, China.
Front Immunol. 2025 Aug 12;16:1580597. doi: 10.3389/fimmu.2025.1580597. eCollection 2025.
Mitophagy has been implicated in the pathogenesis of acute myeloid leukemia (AML), yet its precise molecular mechanisms remain poorly understood. Understanding the roles of mitophagy-related genes (MRGs) may provide new insights into AML classification, prognosis, and therapeutic response.
We analyzed 72 MRGs using three independent AML datasets (TCGA-LAML, GSE24395, and GSE146173). Consensus clustering based on MRG expression was used to identify AML molecular subtypes. Differentially expressed genes (DEGs) common to AML subtypes and GSE24395 were identified. Prognostic genes were screened using univariate Cox regression and least absolute shrinkage and selection operator (Lasso) regression analyses. A prognostic risk model was constructed and validated. Functional enrichment, immune infiltration, and drug sensitivity analyses were conducted to explore the biological relevance of the model. In addition, regulatory elements including microRNAs, lncRNAs, and transcription factors targeting model genes were predicted.
Twenty-six overlapping DEGs were identified between AML subtypes and GSE24395. Five MRG-associated genes (, , , , ) were selected to construct a prognostic model that stratified patients into high- and low-risk groups with significantly different overall survival. Multivariate Cox analysis confirmed that risk score, age, and treatment status were independent prognostic indicators. Gene set enrichment analysis (GSEA) revealed 731 significantly enriched pathways, including mononuclear cell migration. Immune cell infiltration analysis showed a positive correlation between risk score and monocytes, and negative correlations with plasma B cells and activated mast cells. Drug sensitivity prediction identified 84 compounds with differential responses between risk groups. Regulatory network prediction highlighted hsa-miR-135b-5p, FTX, and SOX11 as potential upstream regulators of the prognostic genes.
This study identified five mitophagy-related genes as prognostic biomarkers in AML and developed a robust risk model that correlates with survival outcome, immune infiltration, and drug sensitivity. These findings offer new insights into mitophagy-related mechanisms in AML and may guide personalized therapeutic strategies.
线粒体自噬与急性髓系白血病（AML）的发病机制有关，但其确切的分子机制仍知之甚少。了解线粒体自噬相关基因（MRGs）的作用可能为AML的分类、预后和治疗反应提供新的见解。
我们使用三个独立的AML数据集（TCGA-LAML、GSE24395和GSE146173）分析了72个MRGs。基于MRG表达的一致性聚类用于识别AML分子亚型。确定AML亚型和GSE24395共有的差异表达基因（DEGs）。使用单变量Cox回归和最小绝对收缩和选择算子（Lasso）回归分析筛选预后基因。构建并验证了预后风险模型。进行功能富集、免疫浸润和药物敏感性分析以探索该模型的生物学相关性。此外，预测了包括靶向模型基因的微小RNA、长链非编码RNA和转录因子在内的调控元件。
在AML亚型和GSE24395之间鉴定出26个重叠的DEGs。选择五个与MRG相关的基因（， ， ， ， ）构建预后模型，该模型将患者分为总生存期有显著差异的高风险和低风险组。多变量Cox分析证实风险评分、年龄和治疗状态是独立的预后指标。基因集富集分析（GSEA）揭示了731条显著富集的通路，包括单核细胞迁移。免疫细胞浸润分析显示风险评分与单核细胞呈正相关，与血浆B细胞和活化肥大细胞呈负相关。药物敏感性预测确定了84种在风险组之间有不同反应的化合物。调控网络预测突出显示hsa-miR-135b-5p、FTX和SOX11作为预后基因的潜在上游调节因子。
本研究确定了五个线粒体自噬相关基因作为AML的预后生物标志物，并开发了一个与生存结果、免疫浸润和药物敏感性相关的强大风险模型。这些发现为AML中线粒体自噬相关机制提供了新的见解，并可能指导个性化治疗策略。
