Identification of immune infiltration and PANoptosis-related molecular clusters and predictive model in Alzheimer's disease based on transcriptome analysis

Ibrain Pub Date : 2024-09-23 DOI:10.1002/ibra.12179
Jin-Lin Mei, Shi-Feng Wang, Yang-Yang Zhao, Ting Xu, Yong Luo, Liu-Lin Xiong
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Abstract

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.

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基于转录组分析的阿尔茨海默病免疫浸润和细胞凋亡相关分子群鉴定及预测模型
本研究旨在探讨阿尔茨海默病(AD)中细胞凋亡相关基因(PANoptosis-related genes,PRGs)和免疫细胞浸润的表达谱。本研究基于基因表达总库(Gene Expression Omnibus)数据库,研究了AD中差异表达的PAN凋亡相关基因(PRGs)和免疫细胞浸润,并探索了相关的分子集群。基因集变异分析(GSVA)用于分析基因本体和京都基因与基因组百科全书在不同集群中的表达。利用加权基因共表达网络分析找出网络中的共表达基因模块和核心基因。通过随机森林、支持向量机、广义线性模型和极梯度提升(XGB)分析交叉基因,确定了 XGB 模型。最终,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、细胞内氯离子通道 1、TNF 受体超家族成员 10B)被选为预测基因。该研究探讨了泛凋亡与AD之间的关系,并建立了XGB学习模型来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。
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