Sign in
BioinformaticsbioRxiv
Preprint

Exploring the mechanisms and potential therapeutic targets of Ferroptosis Related Genes in ankylosing spondylitis

Zhen-Gang LiuiD, Fan Yang, Bing CheniD, Peng-Fu Li, Boyin ZhangiD

Abstract

BackgroundFerroptosis is a novel type of regulated cell death, and there is growing evidence that it is directly associated with the disease. Therefore, this study aimed to investigate the relevance of iron Ferroptosis-related genes to ankylosing spondylitis (AS) to propose a novel targeted therapy for AS patients. MethodsAS samples were downloaded from the Gene Expression Omnibus (GEO) database (GSE41038). Ferroptosis-related genes were obtained from the FerrDb database, and differential expression analysis was performed using the GEO2R tool. Additionally, we constructed a protein-protein interaction (PPI) network and identified the central genes using Cytoscape. A neural network model was developed utilizing the data mining software Clementine. cMap technology was used to screen potential pharmacological compounds that target the central gene,and a predictive model was built by machine learning methods. ResultsWe discovered 786 differentially expressed genes in AS samples compared to normal tissue, including 359 up-regulated and 427 down-regulated genes. The intersection of ferroptosis-related genes and differential genes yielded 20 overlapping genes. We constructed a hub gene co-expression network with 20 nodes and 14 edges and obtained the ten most recommended drugs for AS. ConclusionBioinformatics analysis identified 20 potential genes associated with Ferroptosis in AS. Genes such as CAV1, NOX4, and NQO1 were revealed to influence the development of AS by regulating Ferroptosis. Our study provides new insights into the function of iron Ferroptosis-related genes in AS, suggesting that targeting Ferroptosis may be a potential therapeutic option for AS.

PDFDOI

02 /Specification & Classification

Click any tag to browse matching papers

Reproducibility ledger

Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.

Sign in to file a reproducibility report.

Similarity Map
SelectedSame fieldCross-field
THISZhang '23Ma '21Phadnis '22Hu '23Singh '26Liddell '24
53% semantic matchBioinformatics
Shuya Zhang, Ying Liu, Liping Chen+4 more·bioRxiv·Jun 22, 2023