BackgroundTo date, the pathogenesis of Alzheimer’s disease is still not fully elucidated. Much evidence suggests that Ferroptosis plays a crucial role in the pathogenesis of AD, but little is known about its molecular immunological mechanisms. Therefore, this study aims to comprehensively analyse and explore the molecular mechanisms and immunological features of Ferroptosis-related genes in the pathogenesis of AD.Materials and methodsWe obtained the brain tissue dataset for AD from the GEO database and downloaded the Ferroptosis-related gene set from FerrDb for analysis. The most relevant Hub genes for AD were obtained using two machine learning algorithms (Least absolute shrinkage and selection operator (LASSO) and multiple support vector machine recursive feature elimination (mSVM-RFE)). The study of the Hub gene was divided into two parts. In the first part, AD patients were genotyped by unsupervised cluster analysis, and the different clusters’ immune characteristics were analysed. A PCA approach was used to quantify the FRGscore. In the second part: we elucidate the biological functions involved in the Hub genes and their role in the immune microenvironment by integrating algorithms (GSEA, GSVA and CIBERSORT). Analysis of Hub gene-based drug regulatory networks and mRNA-miRNA-lncRNA regulatory networks using Cytoscape. Hub genes were further analysed using logistic regression models.ResultsBased on two machine learning algorithms, we obtained a total of 10 Hub genes. Unsupervised clustering successfully identified two different clusters, and immune infiltration analysis showed a significantly higher degree of immune infiltration in type A than in type B, indicating that type A may be at the peak of AD neuroinflammation. Secondly, a Hub gene-based Gene-Drug regulatory network and a ceRNA regulatory network were successfully constructed. Finally, a logistic regression algorithm-based AD diagnosis model and Nomogram diagram were developed.ConclusionOur study provides new insights into the role of Ferroptosis-related molecular patterns and immune mechanisms in AD, as well as providing a theoretical basis for the addition of diagnostic markers for AD.

Background: Alzheimer’s disease (AD) is the most common form of dementia in old age and poses a severe threat to the health and life of the elderly. However, traditional diagnostic methods and the ATN diagnostic framework have limitations in clinical practice. Developing novel biomarkers and diagnostic models is necessary to complement existing diagnostic procedures.Methods: The AD expression profile dataset GSE63060 was downloaded from the NCBI GEO public database for preprocessing. AD-related differentially expressed genes were screened using a weighted co-expression network and differential expression analysis, and functional enrichment analysis was performed. Subsequently, we screened hub genes by random forest, analyzed the correlation between hub genes and immune cells using ssGSEA, and finally built an AD diagnostic model using an artificial neural network and validated it.Results: Based on the random forest algorithm, we screened a total of seven hub genes from AD-related DEGs, based on which we confirmed that hub genes play an essential role in the immune microenvironment and successfully established a novel diagnostic model for AD using artificial neural networks, and validated its effectiveness in the publicly available datasets GSE63060 and GSE97760.Conclusion: Our study establishes a reliable model for screening and diagnosing AD that provides a theoretical basis for adding diagnostic biomarkers for the AD gene.

BackgroundTemporal lobe epilepsy (TLE) is a common chronic episodic illness of the nervous system. However, the precise mechanisms of dysfunction and diagnostic biomarkers in the acute phase of TLE are uncertain and hard to diagnose. Thus, we intended to qualify potential biomarkers in the acute phase of TLE for clinical diagnostics and therapeutic purposes.MethodsAn intra-hippocampal injection of kainic acid was used to induce an epileptic model in mice. First, with a TMT/iTRAQ quantitative labeling proteomics approach, we screened for differentially expressed proteins (DEPs) in the acute phase of TLE. Then, differentially expressed genes (DEGs) in the acute phase of TLE were identified by linear modeling on microarray data (limma) and weighted gene co-expression network analysis (WGCNA) using the publicly available microarray dataset GSE88992. Co-expressed genes (proteins) in the acute phase of TLE were identified by overlap analysis of DEPs and DEGs. The least absolute shrinkage and selection operator (LASSO) regression and support vector machine recursive feature elimination (SVM-RFE) algorithms were used to screen Hub genes in the acute phase of TLE, and logistic regression algorithms were applied to develop a novel diagnostic model for the acute phase of TLE, and the sensitivity of the diagnostic model was validated using receiver operating characteristic (ROC) curves.ResultsWe screened a total of 10 co-expressed genes (proteins) from TLE-associated DEGs and DEPs utilizing proteomic and transcriptome analysis. LASSO and SVM-RFE algorithms for machine learning were applied to identify three Hub genes: Ctla2a, Hapln2, and Pecam1. A logistic regression algorithm was applied to establish and validate a novel diagnostic model for the acute phase of TLE based on three Hub genes in the publicly accessible datasets GSE88992, GSE49030, and GSE79129.ConclusionOur study establishes a reliable model for screening and diagnosing the acute phase of TLE that provides a theoretical basis for adding diagnostic biomarkers for TLE acute phase genes.