Alzheimer病（AD）是全球最常见的痴呆症，目前仍没有有效的干预措施来减缓或停止潜在病理机制。有强有力的证据表明神经氧化应激（OS）和随之而来的神经炎症在AD大脑中的进行性神经退行性破坏中起着重要作用，且在症状出现之前和之后均有表现。因此，与氧化应激相关的生物标志物可能对预后有价值，并在早期无症状阶段提供治疗靶点的线索。在本研究中，我们收集了AD患者和匹配对照的大脑RNA-seq数据来鉴定差异表达的OS相关基因（OSRGs）。这些OSRGs使用基因本体（GO）数据库进行细胞功能分析，并用于构建加权基因共表达网络（WGCN）和蛋白质相互作用（PPI）网络。然后构建接收器操作特征（ROC）曲线来鉴定网络中心基因。基于这些中心基因，使用最小绝对收缩和选择算子（LASSO）和ROC分析建立了诊断模型。通过评估中心基因表达和免疫细胞脑浸润评分之间的相关性来检查免疫相关功能。此外，使用Drug-Gene Interaction数据库预测目标药物，使用miRNet预测调节miRNA和转录因子。总共鉴定出156个候选基因，在11046个差异表达基因中有7098个基因在WGCN模块中，446个OSRG中有5个网络中心基因（MAPK9，FOXO1，BCL2，ETS1和SP1）通过ROC曲线分析鉴定出来。这些网络中心基因富集在GO注释“Alzheimer病途径”，“帕金森病”，“核糖体”和“慢性髓性白血病”中。此外，预测出78种药物可靶向FOXO1，SP1，MAPK9和BCL2，包括氟尿嘧啶、环磷酰胺和表柔比星。还生成了43个miRNA和36个TF的网络中心基因-miRNA调节网络和TF网络中心基因调节网络。这些中心基因可能为AD诊断提供生物标志物，并提供新的潜在治疗靶点的线索。©2023年作者。

Alzheimer's disease (AD) is the most prevalent dementia disorder globally, and there are still no effective interventions for slowing or stopping the underlying pathogenic mechanisms. There is strong evidence implicating neural oxidative stress (OS) and ensuing neuroinflammation in the progressive neurodegeneration observed in the AD brain both during and prior to symptom emergence. Thus, OS-related biomarkers may be valuable for prognosis and provide clues to therapeutic targets during the early presymptomatic phase. In the current study, we gathered brain RNA-seq data of AD patients and matched controls from the Gene Expression Omnibus (GEO) to identify differentially expressed OS-related genes (OSRGs). These OSRGs were analyzed for cellular functions using the Gene Ontology (GO) database and used to construct a weighted gene co-expression network (WGCN) and protein-protein interaction (PPI) network. Receiver operating characteristic (ROC) curves were then constructed to identify network hub genes. A diagnostic model was established based on these hub genes using Least Absolute Shrinkage and Selection Operator (LASSO) and ROC analyses. Immune-related functions were examined by assessing correlations between hub gene expression and immune cell brain infiltration scores. Further, target drugs were predicted using the Drug-Gene Interaction database, while regulatory miRNAs and transcription factors were predicted using miRNet. In total, 156 candidate genes were identified among 11046 differentially expressed genes, 7098 genes in WGCN modules, and 446 OSRGs, and 5 hub genes (MAPK9, FOXO1, BCL2, ETS1, and SP1) were identified by ROC curve analyses. These hub genes were enriched in GO annotations "Alzheimer's disease pathway," "Parkinson's Disease," "Ribosome," and "Chronic myeloid leukemia." In addition, 78 drugs were predicted to target FOXO1, SP1, MAPK9, and BCL2, including fluorouracil, cyclophosphamide, and epirubicin. A hub gene-miRNA regulatory network with 43 miRNAs and hub gene-transcription factor (TF) network with 36 TFs were also generated. These hub genes may serve as biomarkers for AD diagnosis and provide clues to novel potential treatment targets.© 2023. The Author(s).