通过生物信息学分析和机器学习鉴定牙周炎和阿尔茨海默病中的串扰通路和PAN凋亡相关基因。
Identification of cross-talk pathways and PANoptosis-related genes in periodontitis and Alzheimer's disease by bioinformatics analysis and machine learning.
作者信息
Chen Xiantao, Dai Yifei, Li Yushen, Xin Jiajun, Zou Jiatong, Wang Rui, Zhang Hao, Liu Zhihui
机构信息
Hospital of Stomatology, Jilin University, Changchun, China.
Jilin Provincial Key Laboratory of Tooth Development and Bone Remodeling, Changchun, China.
出版信息
Front Aging Neurosci. 2024 Aug 27;16:1430290. doi: 10.3389/fnagi.2024.1430290. eCollection 2024.
BACKGROUND AND OBJECTIVES
Periodontitis (PD), a chronic inflammatory disease, is a serious threat to oral health and is one of the risk factors for Alzheimer's disease (AD). A growing body of evidence suggests that the two diseases are closely related. However, current studies have not provided a comprehensive understanding of the common genes and common mechanisms between PD and AD. This study aimed to screen the crosstalk genes of PD and AD and the potential relationship between cross-talk and PANoptosis-related genes. The relationship between core genes and immune cells will be analyzed to provide new targets for clinical treatment.
MATERIALS AND METHODS
The PD and AD datasets were downloaded from the GEO database and differential expression analysis was performed to obtain DEGs. Overlapping DEGs had cross-talk genes linking PD and OP, and PANoptosis-related genes were obtained from a literature review. Pearson coefficients were used to compute cross-talk and PANoptosis-related gene correlations in the PD and AD datasets. Cross-talk genes were obtained from the intersection of PD and AD-related genes, protein-protein interaction(PPI) networks were constructed and cross-talk genes were identified using the STRING database. The intersection of cross-talk and PANoptosis-related genes was defined as cross-talk-PANoptosis genes. Core genes were screened using ROC analysis and XGBoost. PPI subnetwork, gene-biological process, and gene-pathway networks were constructed based on the core genes. In addition, immune infiltration on the PD and AD datasets was analyzed using the CIBERSORT algorithm.
RESULTS
366 cross-talk genes were overlapping between PD DEGs and AD DEGs. The intersection of cross-talk genes with 109 PANoptosis-related genes was defined as cross-talk-PANoptosis genes. ROC and XGBoost showed that MLKL, DCN, IL1B, and IL18 were more accurate than the other cross-talk-PANoptosis genes in predicting the disease, as well as better in overall characterization. GO and KEGG analyses showed that the four core genes were involved in immunity and inflammation in the organism. Immune infiltration analysis showed that B cells naive, Plasma cells, and T cells gamma delta were significantly differentially expressed in patients with PD and AD compared with the normal group. Finally, 10 drugs associated with core genes were retrieved from the DGIDB database.
CONCLUSION
This study reveals the joint mechanism between PD and AD associated with PANoptosis. Analyzing the four core genes and immune cells may provide new therapeutic directions for the pathogenesis of PD combined with AD.
背景与目的
牙周炎(PD)是一种慢性炎症性疾病,对口腔健康构成严重威胁,是阿尔茨海默病(AD)的危险因素之一。越来越多的证据表明这两种疾病密切相关。然而,目前的研究尚未全面了解PD和AD之间的共同基因和共同机制。本研究旨在筛选PD和AD的串扰基因以及串扰与PANoptosis相关基因之间的潜在关系。将分析核心基因与免疫细胞之间的关系,为临床治疗提供新的靶点。
材料与方法
从GEO数据库下载PD和AD数据集,并进行差异表达分析以获得差异表达基因(DEG)。重叠的DEG具有连接PD和OP的串扰基因,通过文献综述获得PANoptosis相关基因。使用Pearson系数计算PD和AD数据集中串扰与PANoptosis相关基因的相关性。从PD和AD相关基因的交集中获得串扰基因,构建蛋白质-蛋白质相互作用(PPI)网络,并使用STRING数据库鉴定串扰基因。串扰与PANoptosis相关基因的交集定义为串扰-PANoptosis基因。使用ROC分析和XGBoost筛选核心基因。基于核心基因构建PPI子网、基因-生物学过程和基因-通路网络。此外,使用CIBERSORT算法分析PD和AD数据集上的免疫浸润情况。
结果
366个串扰基因在PD的DEG和AD的DEG之间重叠。串扰基因与109个PANoptosis相关基因的交集定义为串扰-PANoptosis基因。ROC和XGBoost分析表明,MLKL、DCN、IL1B和IL18在预测疾病方面比其他串扰-PANoptosis基因更准确,总体特征也更好。GO和KEGG分析表明,这四个核心基因参与机体的免疫和炎症反应。免疫浸润分析表明,与正常组相比,PD和AD患者的幼稚B细胞、浆细胞和γδT细胞存在显著差异表达。最后,从DGIDB数据库中检索到10种与核心基因相关的药物。
结论
本研究揭示了与PANoptosis相关的PD和AD之间的联合机制。分析这四个核心基因和免疫细胞可能为PD合并AD的发病机制提供新的治疗方向。