Abstract
Background
Allergic Rhinitis (AR), an inflammatory affliction impacting the upper respiratory tract, has been registering a substantial surge in incidence across the globe.
Methods
We embarked on examination of differentially expressed genes (DEGs) and the Weighted Gene Co-Expression Network Analysis (WGCNA). With this armory of genes identified, we engaged the tools of Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Our study continued with the establishment of a protein-protein interaction (PPI) network and the application of LASSO regression. Finally, we leveraged a docking model to elucidate potential drug-gene interactions involving these key genes.
Results
Through WGCNA and different express genes screening, PPI network was performed, identifying top 20 key genes, including CD44, CD69, CD274. LASSO regression identified three independent factors, STARD5, CST1, and CHAC1, that were significantly associated with AR. A predictive model was developed with an AUC value over 0.75. Also, 105 potential therapeutic agents were discovered, including Fluorouracil, Cyclophosphamide, Doxorubicin, and Hydrocortisone, offering promising therapeutic strategies for AR.
Conclusion
By fuzing DEGs with key genes derived from WGCNA, this study has illuminated a comprehensive network of gene interactions involved in the pathogenesis of AR, paving the way for future biomarker and therapeutic target discovery in AR.
Acknowledgements
Our profound gratitude goes to Liao Ruosha for her meticulous review of all drafts and for her initial guidance on this paper. We also wish to express our deep appreciation to Lin Xiaohong for her objective critique and significant insights, which have greatly enhanced the quality of this research. We commend Yuan Chiluo for his dedicated efforts in spearheading the research design and data analysis. Moreover, we would like to acknowledge the collective efforts of all the authors in refining and improving the article, ensuring its accuracy in presenting the complex research results. Last but not least, we are grateful to our peers and reviewers who provided invaluable feedback throughout the research process, making our findings more robust and comprehensive.
Authors’ contributions
This article was written by Yuan Chiluo. Yuan Chiluo independently undertook the research design and data analysis. Liao Ruosha reviewed all drafts and provided initial guidance for the paper, while Lin Xiaohong provided objective revisions. All authors contributed to the further refinement of the article. Given the heterogeneous knowledge in challenging clinical research, both Yuan Chiluo and Liao Ruosha participated in the design of the study, pivotal decisions during the research process, data collection, and the initial report. Lin Xiaohong offered crucial guidance in addressing intricate issues within the article. Furthermore, Yuan Chiluo and Liao Ruosha underwent the complete review process, ensuring the scientific integrity of the paper. Lastly, all authors were actively involved in the writing and revision of the article, providing constructive feedback and suggestions, ensuring the accurate representation of the intricate research outcomes.
Disclosure statement
The authors report no conflicts of interest. The authors alone are responsible for the content and writing of this article.
Data availability statement
All data analyzed in this study were sourced from the GEO (Gene Expression Omnibus) database. Specifically, the datasets we utilized are GSE19187, GSE43523, and GSE44037.