Mei Jin-Lin, Wang Shi-Feng, Zhao Yang-Yang, Xu Ting, Luo Yong, Xiong Liu-Lin
School of Anesthesiology Zunyi Medical University Zunyi China.
Department of Neurology Third Affiliated Hospital of Zunyi Medical University Zunyi China.
Ibrain. 2024 Sep 23;10(3):323-344. doi: 10.1002/ibra.12179. eCollection 2024 Fall.
This study aims to explore the expression profile of PANoptosis-related genes (PRGs) and immune infiltration in Alzheimer's disease (AD). Based on the Gene Expression Omnibus database, this study investigated the differentially expressed PRGs and immune cell infiltration in AD and explored related molecular clusters. Gene set variation analysis (GSVA) was used to analyze the expression of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes in different clusters. Weighted gene co-expression network analysis was utilized to find co-expressed gene modules and core genes in the network. By analyzing the intersection genes in random forest, support vector machine, generalized linear model, and extreme gradient boosting (XGB), the XGB model was determined. Eventually, the first five genes (Signal Transducer and Activator of Transcription 3, Tumor Necrosis Factor (TNF) Receptor Superfamily Member 1B, Interleukin 4 Receptor, Chloride Intracellular Channel 1, TNF Receptor Superfamily Member 10B) in XGB model were selected as predictive genes. This research explored the relationship between PANoptosis and AD and established an XGB learning model to evaluate and screen key genes. At the same time, immune infiltration analysis showed that there were different immune infiltration expression profiles in AD.
本研究旨在探讨泛凋亡相关基因（PRGs）的表达谱及阿尔茨海默病（AD）中的免疫浸润情况。基于基因表达综合数据库，本研究调查了AD中差异表达的PRGs及免疫细胞浸润情况，并探索了相关分子簇。基因集变异分析（GSVA）用于分析不同簇中基因本体论和京都基因与基因组百科全书的表达。加权基因共表达网络分析用于在网络中寻找共表达基因模块和核心基因。通过分析随机森林、支持向量机、广义线性模型和极端梯度提升（XGB）中的交集基因，确定了XGB模型。最终，选择XGB模型中的前五个基因（信号转导和转录激活因子3、肿瘤坏死因子（TNF）受体超家族成员1B、白细胞介素4受体、氯离子细胞内通道1、TNF受体超家族成员10B）作为预测基因。本研究探索了泛凋亡与AD之间的关系，并建立了XGB学习模型以评估和筛选关键基因。同时，免疫浸润分析表明AD中存在不同的免疫浸润表达谱。
