Journal Article

Mechanisms of cellular senescence combined with molecular docking strategies: A biomarker study of potential therapeutic targets for allergic rhinitis

Lin, Liqiang, Lv, Huaiqing, Wu, Yue, Chen, Qingyong, Sun, Dezhong, Zhang, Han, Liu, Jixin, Wang, Dongqing, Chen, Zhipeng, Shao, Qiang

2026

Overview

Bioinformatics and molecular docking methods were used to screen potential biomarkers of cellular senescence in allergic rhinitis (Allergic rhinitis AR), which provided a theoretical basis for revealing the mechanism of AR and exploring new therapeutic approaches. Four AR-related gene chips (GSE19187, GSE43523, GSE44037, and GSE51392) were downloaded from the gene expression database (GEO) for data pooling. Screening differential genes (DEGs) were taken to intersect with cellular senescence-related genes (SRGs) to obtain differential senescence genes (DESRGs). The differential senescence genes were subjected to Gene Ontology Database (GO) functional analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and GSEA enrichment analysis. Protein-protein interaction (PPI) networks were constructed through the STRING database, MCODE plugin weights were analyzed to identify important gene cluster modules, and Hub genes were screened using the CytoHubba plugin topological network algorithm. Hub gene protein interactions network (GeneMANIA) was constructed by the GeneMANIA database. Predict Hub gene construct mRNA-miNA-lncRNA interactions by miRanda, miRDB, miRWalk, TargetScan, and spongeScan databases; construct Hub gene transcription factor regulatory networks by TRRUST database; analyze Hub gene-drug interactions by DGIdb database and select commonly used drugs in the clinic for molecular docking validation. A total of 264 differential genes were screened in the training set with corrected P.adj < 0.05 and |log2FC| ≥ 1.2 as the filtering condition, and a total of 866 cellular senescence genes, and 20 differential senescence genes (DESRGs) were obtained by taking the intersection of the two. A total of 19 Hub genes were obtained after PPI analysis, which were CCL2, STAT1, TLR2, IGFBP3, TLR3, KLF4, IL1RN, IRF1, SERPINB2, DPP4, MME, NQO1, SAMHD1, XAF1, PHGDH, EIF4EBP1, CTH, HSPA2, AHR The gene-protein interaction network identified 19 Hub genes associated with 21 functional proteins. 5 of the Hub gene loci were associated with 29 miRNAs and 53 lncRNAs. The transcription factor regulatory network obtained 15 transcription factors capable of regulating Hub genes. The analysis of drug-gene interactions identified 489 drugs that target hub genes. For example, in the case of budesonide, the interacting genes STAT1, TLR2, TLR3, and AHR were selected for molecular docking. Similarly, for mometasone, the interacting genes TLR2 and CTH were chosen for molecular docking. Mining AR-related Hub senescence genes by bioinformatics analysis, constructing PPI network, ceRNA network, transcription factor regulatory network, gene-drug interaction network and molecular docking validation, we screened 19 CCL2, STAT1, TLR2, IGFBP3, TLR3, KLF4, IL1RN, IRF1, SERPINB2, DPP4, MME, NQO1, SAMHD1, XAF1, PHGDH, EIF4EBP1, CTH, HSPA2, and AHR are expected to be Hub genes for potential diagnostic and therapeutic biomarkers, which will provide targets and new insights for further in-depth explorations of AR cellular senescence-related mechanisms of action and therapy.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Aging / Algorithms / Allergic rhinitis / Analysis / Asthma / Bioinformatics / Biological markers / Biomarkers / Biomarkers - metabolism / Budesonide / Care and treatment / Cell cycle / Cells / Cellular Senescence - genetics / Chronic obstructive pulmonary disease / Computational Biology - methods / Computer programs / Databases, Genetic / Datasets / Diagnosis / Drug development / Drug interaction / Drug interactions / Drugs / Functional analysis / Gene expression / Gene Expression Profiling / Gene Ontology / Gene Regulatory Networks / Genes / Genomics / Hay fever / Health aspects / Humans / Insulin-like growth factor-binding protein 3 / Interferon regulatory factor 1 / KLF4 protein / Molecular docking / Molecular Docking Simulation / Monocyte chemoattractant protein 1 / Non-coding RNA / Pathogenesis / Phosphoglycerate dehydrogenase / Protein interaction / Protein Interaction Maps / Proteins / Rhinitis / Rhinitis, Allergic - drug therapy / Rhinitis, Allergic - genetics / Rhinitis, Allergic - metabolism / Rhinitis, Allergic - pathology / Risk factors / Senescence / Software / Stat1 protein / Statistical analysis / Therapeutic targets / TLR2 protein / TLR3 protein / Toll-like receptors / Transcription factors