Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing, China.
Department of Research and Development, Beijing Yihua Biotechnology Co., Ltd, Beijing, China.
Front Immunol. 2024 Aug 12;15:1443464. doi: 10.3389/fimmu.2024.1443464. eCollection 2024.
Advancements in modern medicine have extended human lifespan, but they have also led to an increase in age-related diseases such as Alzheimer's disease (AD) and atherosclerosis (AS). Growing research evidence indicates a close connection between these two conditions.
We downloaded four gene expression datasets related to AD and AS from the Gene Expression Omnibus (GEO) database (GSE33000, GSE100927, GSE44770, and GSE43292) and performed differential gene expression (DEGs) analysis using the R package "limma". Through Weighted gene correlation network analysis (WGCNA), we selected the gene modules most relevant to the diseases and intersected them with the DEGs to identify crosstalk genes (CGs) between AD and AS. Subsequently, we conducted functional enrichment analysis of the CGs using DAVID. To screen for potential diagnostic genes, we applied the least absolute shrinkage and selection operator (LASSO) regression and constructed a logistic regression model for disease prediction. We established a protein-protein interaction (PPI) network using STRING (https://cn.string-db.org/) and Cytoscape and analyzed immune cell infiltration using the CIBERSORT algorithm. Additionally, NetworkAnalyst (http://www.networkanalyst.ca) was utilized for gene regulation and interaction analysis, and consensus clustering was employed to determine disease subtypes. All statistical analyses and visualizations were performed using various R packages, with a significance level set at p<0.05.
Through intersection analysis of disease-associated gene modules identified by DEGs and WGCNA, we identified a total of 31 CGs co-existing between AD and AS, with their biological functions primarily associated with immune pathways. LASSO analysis helped us identify three genes (C1QA, MT1M, and RAMP1) as optimal diagnostic CGs for AD and AS. Based on this, we constructed predictive models for both diseases, whose accuracy was validated by external databases. By establishing a PPI network and employing four topological algorithms, we identified four hub genes (C1QB, CSF1R, TYROBP, and FCER1G) within the CGs, closely related to immune cell infiltration. NetworkAnalyst further revealed the regulatory networks of these hub genes. Finally, defining C1 and C2 subtypes for AD and AS respectively based on the expression profiles of CGs, we found the C2 subtype exhibited immune overactivation.
This study utilized gene expression matrices and various algorithms to explore the potential links between AD and AS. The identification of CGs revealed interactions between these two diseases, with immune and inflammatory imbalances playing crucial roles in their onset and progression. We hope these findings will provide valuable insights for future research on AD and AS.
现代医学的进步延长了人类的寿命，但也导致了与年龄相关的疾病的增加，如阿尔茨海默病（AD）和动脉粥样硬化（AS）。越来越多的研究证据表明，这两种疾病之间存在密切联系。
我们从基因表达综合数据库（GEO）下载了四个与 AD 和 AS 相关的基因表达数据集（GSE33000、GSE100927、GSE44770 和 GSE43292），并使用 R 包“limma”进行差异基因表达（DEGs）分析。通过加权基因相关网络分析（WGCNA），我们选择了与疾病最相关的基因模块，并与 DEGs 进行交集，以识别 AD 和 AS 之间的共话基因（CGs）。随后，我们使用 DAVID 对 CGs 进行功能富集分析。为了筛选潜在的诊断基因，我们应用了最小绝对收缩和选择算子（LASSO）回归，并构建了疾病预测的逻辑回归模型。我们使用 STRING（https://cn.string-db.org/）和 Cytoscape 构建了蛋白质-蛋白质相互作用（PPI）网络，并使用 CIBERSORT 算法分析免疫细胞浸润。此外，我们还使用 NetworkAnalyst（http://www.networkanalyst.ca）进行基因调控和相互作用分析，并采用共识聚类确定疾病亚型。所有的统计分析和可视化都是使用各种 R 包进行的，显著性水平设为 p<0.05。
通过 DEGs 和 WGCNA 识别的疾病相关基因模块的交集分析，我们共鉴定出 31 个 CGs 存在于 AD 和 AS 之间，其生物学功能主要与免疫途径有关。LASSO 分析帮助我们识别出三个基因（C1QA、MT1M 和 RAMP1）作为 AD 和 AS 的最佳诊断 CGs。基于此，我们构建了两种疾病的预测模型，并通过外部数据库验证了其准确性。通过建立 PPI 网络并使用四种拓扑算法，我们从 CGs 中鉴定出四个核心基因（C1QB、CSF1R、TYROBP 和 FCER1G），它们与免疫细胞浸润密切相关。NetworkAnalyst 进一步揭示了这些核心基因的调控网络。最后，根据 CGs 的表达谱，分别为 AD 和 AS 定义 C1 和 C2 亚型，我们发现 C2 亚型表现出免疫过度激活。
本研究利用基因表达矩阵和各种算法探讨了 AD 和 AS 之间的潜在联系。CGs 的鉴定揭示了这两种疾病之间的相互作用，免疫和炎症失衡在其发病和进展中起着关键作用。我们希望这些发现将为 AD 和 AS 的未来研究提供有价值的见解。
