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. 2024 Nov 11;14(1):27624.
doi: 10.1038/s41598-024-78375-6.

Bioinformatics analysis and identification of underlying biomarkers potentially linking allergic rhinitis and autophagy

Tao Zhou  1 , Hua Cai  1 , Lisha Wu  2 , Jianjun Chen  3 , Liuqing Zhou  4 , Jun Liu  5
Affiliations

Bioinformatics analysis and identification of underlying biomarkers potentially linking allergic rhinitis and autophagy

Tao Zhou et al. Sci Rep. .

Abstract

Allergic rhinitis (AR) resulted in impairing human health and quality of life seriously. There is currently no definitive remedy for AR. Recent studies have shown that autophagy may regulate airway inflammation. Our comprehension of autophagy and its molecular mechanism in the field of AR condition remains incomplete. Our research endeavors to bridge this knowledge deficit by investigating the correlation between AR and autophagy. The AR-related gene expression profile GSE50223 was screened and downloaded. The "limma" package of R software was utilized to identify differentially expressed genes associated with autophagy. GO, KEGG, and Gene set enrichment analyses were conducted. A PPI network of differentially expressed autophagy-related genes were established and further identified through the CytoHubba algorithm. A receiver operating characteristic curve analysis was employed to evaluate the diagnostic effectiveness of the hub genes and to examine the relationship between autophagy-related genes and AR. Finally, qRT-PCR was carried out to confirm the chosen autophagy-related genes using clinical samples. 21 autophagy-related genes in allergic rhinitis were identified. BECN1, PIK3C3, GABARAPL2, ULK2, and UVRAG were considered as significant differentially expressed autophagy-related genes. However, additional molecular biological experiments will be necessary to elucidate the underlying mechanism connecting autophagy and AR.

Keywords: Allergic rhinitis; Autophagy; Biomarker; Database; Gene expression.

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Conflict of interest statement

Declarations Statement All experimental protocols were approved by the Ethics Committee of Union Hospital, Tongji Medical College, Huazhong University of Science and Technology (UHCT-IEC-SOP-016-03-01). All methods were carried out in accordance with relevant guidelines and regulations. Competing interests The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Heatmaps of 21 significantly differentially expressed autophagy-related genes in allergic rhinitis.
Fig. 2
Fig. 2
(A,B) The bar plot (A) and dot plot (B) of GO analysis for the top ten enrichments. (C,D) The bar plot (C) and dot plot (D) of KEGG pathway enrichment analysis.
Fig. 3
Fig. 3
(A,B) GSEA results show significant enrichment signaling pathways in treat group (A) and in control group (B). (C) Visualization of PPI network of AR autophagy-related genes.
Fig. 4
Fig. 4
(A) The scatter diagram indicates the comparative expression of immune infiltration analysis between treat group and control group. (B) The bar plot shows the proportions of the 22 different types of immune cells. (C) The AUC value of ROC curve analysis.
Fig. 5
Fig. 5
The mRNA expression in individuals with AR and healthy controls was assessed by polymerase chain reaction (qRT-PCR) analysis.
See this image and copyright information in PMC

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