The First Clinical Medical College of Jinan University, First Affiliated Hospital of Jinan University, Guangzhou 510630, China.
School of Life Science, Northwestern Polytechnical University, Xian, 710072, China.
Oxid Med Cell Longev. 2022 Aug 22;2022:1507690. doi: 10.1155/2022/1507690. eCollection 2022.
Oxidative stress (OS) is associated with the development of acute myeloid leukemia (AML). However, there is lack of relevant research to confirm that OS-related genes can guide patients in risk stratification and predict their survival probability.
First, we Data from three public databases, respectively. Then, we use batch univariate Cox regression and machine learning to select important characteristic genes; next, we build the model and use receiver operating characteristic curve (ROC) to evaluate the accuracy. Moreover, GSEAs were performed to discover the molecular mechanism and conduct nomogram visualization. In addition, the relative importance value was used to identify the hub gene, and GSE9476 was to validate hub gene difference expression. Finally, we use symptom mapping to predict the candidate herbs, targeting the hub gene, and put these candidate herbs into Traditional Chinese Medicine Systems Pharmacology (TCMSP) to identify the main small molecular ingredients and then docking hub proteins with this small molecular.
A total of 313 candidate oxidative stress-related genes could affect patients' outcomes and machine learning to select six potential genes to construct a gene signature model to predict the overall survival (OS) of AML patients. Patients in a high group will obtain a short survival time when compared with the low-risk group (HR = 3.97, 95% CI: 2.48-6.36; < 0.001). ROC results demonstrate the model has better prediction efficiency with AUC 0.873. GSEA suggests that this gene is enriched in several important signaling pathways. Nomogram is constructed and is robust. PLA2G4A is a hub gene of signature and associated with prognosis, and Nobiletin could target PLA2G4A for therapy AML.
We use two different machine learning methods to build six oxidative stress-related gene signatures that could assist clinical decisions and identify PLA2G4A as a potential biomarker for AML. Nobiletin, targeting PLA2G4, may provide a third pathway for therapy AML.
氧化应激（OS）与急性髓系白血病（AML）的发生发展有关。然而，目前缺乏相关研究来证实 OS 相关基因可以指导患者进行风险分层，并预测其生存概率。
首先，我们从三个公共数据库中获取 OS 相关的基因表达数据。然后，我们使用批量单变量 Cox 回归和机器学习方法筛选出重要的特征基因；接下来，构建模型并使用接受者操作特征曲线（ROC）评估准确性。此外，进行 GSEA 以发现分子机制并进行列线图可视化。此外，使用相对重要值识别关键基因，并使用 GSE9476 验证关键基因的差异表达。最后，我们使用症状映射来预测针对关键基因的候选草药，并将这些候选草药输入中药系统药理学（TCMSP）以识别主要的小分子成分，然后将关键蛋白与这些小分子对接。
共筛选出 313 个候选氧化应激相关基因，这些基因可能影响患者的预后。利用机器学习方法选择 6 个潜在基因构建基因签名模型，用于预测 AML 患者的总生存期（OS）。高风险组患者的生存时间明显短于低风险组（HR=3.97，95%CI：2.48-6.36；<0.001）。ROC 结果表明，该模型的预测效率较好，AUC 为 0.873。GSEA 表明该基因与多个重要信号通路相关。构建了列线图，结果稳健。PLA2G4A 是签名的关键基因，与预后相关，并且诺必灵可以靶向 PLA2G4A 治疗 AML。
我们使用两种不同的机器学习方法构建了六个与氧化应激相关的基因签名，可以辅助临床决策，并确定 PLA2G4A 为 AML 的潜在生物标志物。针对 PLA2G4A 的诺必灵可能为 AML 治疗提供第三条途径。
