Department of Geriatric Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou, Fujian, China.
Fujian Provincial Center for Geriatrics, Fujian Provincial Hospital, Fuzhou, Fujian, China.
Front Immunol. 2023 Feb 21;14:1115202. doi: 10.3389/fimmu.2023.1115202. eCollection 2023.
We developed a novel system for quantifying DNA damage response (DDR) to help diagnose and predict the risk of Alzheimer's disease (AD).
We thoroughly estimated the DDR patterns in AD patients Using 179 DDR regulators. Single-cell techniques were conducted to validate the DDR levels and intercellular communications in cognitively impaired patients. The consensus clustering algorithm was utilized to group 167 AD patients into diverse subgroups after a WGCNA approach was employed to discover DDR-related lncRNAs. The distinctions between the categories in terms of clinical characteristics, DDR levels, biological behaviors, and immunological characteristics were evaluated. For the purpose of choosing distinctive lncRNAs associated with DDR, four machine learning algorithms, including LASSO, SVM-RFE, RF, and XGBoost, were utilized. A risk model was established based on the characteristic lncRNAs.
The progression of AD was highly correlated with DDR levels. Single-cell studies confirmed that DDR activity was lower in cognitively impaired patients and was mainly enriched in T cells and B cells. DDR-related lncRNAs were discovered based on gene expression, and two different heterogeneous subtypes (C1 and C2) were identified. DDR C1 belonged to the non-immune phenotype, while DDR C2 was regarded as the immune phenotype. Based on various machine learning techniques, four distinctive lncRNAs associated with DDR, including FBXO30-DT, TBX2-AS1, ADAMTS9-AS2, and MEG3 were discovered. The 4-lncRNA based riskScore demonstrated acceptable efficacy in the diagnosis of AD and offered significant clinical advantages to AD patients. The riskScore ultimately divided AD patients into low- and high-risk categories. In comparison to the low-risk group, high-risk patients showed lower DDR activity, accompanied by higher levels of immune infiltration and immunological score. The prospective medications for the treatment of AD patients with low and high risk also included arachidonyltrifluoromethane and TTNPB, respectively.
In conclusion, immunological microenvironment and disease progression in AD patients were significantly predicted by DDR-associated genes and lncRNAs. A theoretical underpinning for the individualized treatment of AD patients was provided by the suggested genetic subtypes and risk model based on DDR.
我们开发了一种新的系统来量化 DNA 损伤反应 (DDR)，以帮助诊断和预测阿尔茨海默病 (AD) 的风险。
我们使用 179 个 DDR 调节剂来全面评估 AD 患者的 DDR 模式。单细胞技术用于验证认知障碍患者的 DDR 水平和细胞间通讯。采用共识聚类算法将 167 名 AD 患者分为不同亚组，然后采用 WGCNA 方法发现与 DDR 相关的长非编码 RNA (lncRNA)。评估了不同类别在临床特征、DDR 水平、生物学行为和免疫特征方面的差异。为了选择与 DDR 相关的独特 lncRNA，我们使用了四种机器学习算法，包括 LASSO、SVM-RFE、RF 和 XGBoost。基于特征 lncRNA 建立了风险模型。
AD 的进展与 DDR 水平高度相关。单细胞研究证实，认知障碍患者的 DDR 活性较低，主要富集在 T 细胞和 B 细胞中。基于基因表达发现了与 DDR 相关的 lncRNA，并确定了两种不同的异质亚型 (C1 和 C2)。DDR C1 属于非免疫表型，而 DDR C2 被认为是免疫表型。基于各种机器学习技术，发现了四个与 DDR 相关的独特 lncRNA，包括 FBXO30-DT、TBX2-AS1、ADAMTS9-AS2 和 MEG3。基于 4 个 lncRNA 的风险评分在 AD 的诊断中具有良好的效果，并为 AD 患者提供了显著的临床优势。风险评分最终将 AD 患者分为低风险和高风险两类。与低风险组相比，高风险组 DDR 活性较低，免疫浸润和免疫评分较高。低风险和高风险 AD 患者的潜在治疗药物分别为花生四烯酸三氟甲氧基和 TTNPB。
总之，DDR 相关基因和 lncRNA 显著预测了 AD 患者的免疫微环境和疾病进展。基于 DDR 的建议遗传亚型和风险模型为 AD 患者的个体化治疗提供了理论依据。
