This study is aimed at investigating the potential molecular features of allergic rhinitis (AR) and identifying gene signatures and related transcription factors using transcriptome analysis and in silico datasets. Transcriptome profiles were obtained using three independent cohorts (GSE101720, GSE19190, and GSE46171) comprising healthy controls (HC) and patients with AR. The pooled dataset (n= 82) was used to identify the critical signatures of AR compared with HC. Subsequently, key transcription factors were identified by a combined analysis using transcriptome and in silico datasets. Gene ontology: bioprocess (GO: BP) analysis using differentially expressed genes (DEGs) revealed that immune response-related genes were significantly enriched in AR compared with HC. Among them,IL1RL1,CD274, andCD44were significantly higher in AR patients. We also identified key transcription factors between HC and AR using the in silico dataset and found that AR samples frequently express KLF transcription factor 4 (KLF4), which regulates immune response-related genes includingIL1RL1,CD274, andCD44in human nasal epithelial cells. Our integrative analysis of transcriptomic regulation provides new insights into AR, which may help in developing precision management for patients with AR.