Exploring of the molecular mechanism of rhinitis via bioinformatics methods

Genetic Loci in Phospholipase C-Like 1 (PLCL1) are Protective Factors for Allergic Rhinitis in Han Population of Northern Shaanxi, China

Background

Allergic rhinitis (AR) is a common allergic disease in otolaryngology. Its pathogenesis is still unclear. PLC1 plays a key role in calcium homeostasis and immune response, which is potentially related to AR. We aimed to explore the association between PLCL1 genetic loci and susceptibility to AR.

Methods

We recruited 1975 volunteers to perform an association analysis through SNPStats online software. False-positive report probability (FPRP) analysis was used to detect whether the positive findings were worth noting. Linkage disequilibrium and haplotype analysis were completed through Haploview and SNPStats. The influence of SNP-SNP interaction on AR susceptibility was evaluated through multifactor dimensionality reduction (MDR).

Results

The results showed that four genetic loci in PLCL1 (rs2139049, rs212164068, rs2228135, and rs6738825) are associated with AR susceptibility under multiple genetic models. Allele "A" of PLCL1-rs2139049 (OR = 0.85, p = 0.031) or of -rs212164068 (OR = 0.85, p = 0.030), and allele "G" of PLCL1-rs6738825 (OR = 0.84, p = 0.022) are significantly associated with reduced AR risk. PLCL1-rs2228135 is associated with an increased risk of AR in males or participants older than 43 years of age. FPRP analysis showed that most of positive results are noteworthy findings. Three loci model composed of rs2139049, rs2164068, and rs2228135 is the best model for predicting AR risk (p = 0.0022). In addition, the haplotype "G_rs2139049A_rs6738825A_rs2164068A_rs2228135" (OR = 0.50, p = 0.033) can reduce the AR risk.

Conclusion

Allele "A" of PLCL1-rs2139049, allele "A" of -rs212164068, and allele "G" of PLCL1-rs6738825 are protective factors of AR in Han population from northern Shaanxi, China.

Identification of gene biomarkers with expression profiles in patients with allergic rhinitis

Background

Allergic rhinitis (AR) is an upper respiratory tract inflammation disease caused by IgE-mediated reactions against inhaled allergens. The incidence of AR is significantly increasing throughout the world. Hence, more specific, and sensitive gene biomarkers and understanding the underlying pathways are necessary to further explore the AR pathogenesis.

Objective

To identify gene biomarkers in nasal mucosa and in blood from AR patients which could be used in AR diagnosis.

Methods

The gene expression profiles of GSE43523 from nasal epithelial cells and GSE75011 from Th2-enriched CD4+ T cells in blood were downloaded from the Gene Expression Omnibus database. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses and protein–protein interaction (PPI) network analysis were conducted to investigate the functional changes of genes. The receiver operating characteristic (ROC) curves were used to assess the diagnostic values of the hub genes. Real-time quantitative PCR (RT-qPCR) was performed to validate the hub genes.

Results

Significant differentially enriched gene signatures in AR patients were identified in nasal epithelial cells (n-DEGs) and in blood (t-DEGs). Signatures associated with axoneme, extracellular matrix, collagen fibril organization, cell motility, calcium ion binding, and so on were more enriched in n-DEGs, whereas signatures associated with TNF signaling pathway, detoxification of inorganic compound, and cellular response to corticotropin-releasing hormone stimulus were enriched in t-DEGs. In addition, we identified 8 hub genes and 14 hub genes from n-DEGs and t-DEGs, respectively. The combination of POSTN in nasal mucosa and PENK and CDC25A in blood was constructed with a good AR predicting performance. The area under the curve (AUC) of the ROC curve of 3 hub genes’ combination was 0.98 for AR diagnosis.

Conclusion

This study utilized gene expression profiles and RT-qPCR validation on nasal mucosa and blood from AR patients to investigate the potential biomarkers for AR diagnosis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13223-022-00656-4.

Identification of key genes in allergic rhinitis by bioinformatics analysis

Objective

This study aimed to explore the potential molecular mechanism of allergic rhinitis (AR) and identify gene signatures by analyzing microarray data using bioinformatics methods.

Methods

The dataset GSE19187 was used to screen differentially expressed genes (DEGs) between samples from patients with AR and healthy controls. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were applied for the DEGs. Subsequently, a protein–protein interaction (PPI) network was constructed to identify hub genes. GSE44037 and GSE43523 datasets were screened to validate critical genes.

Results

A total of 156 DEGs were identified. GO analysis verified that the DEGs were enriched in antigen processing and presentation, the immune response, and antigen binding. KEGG analysis demonstrated that the DEGs were enriched in Staphylococcus aureus infection, rheumatoid arthritis, and allograft rejection. PPI network and module analysis predicted seven hub genes, of which six (CD44, HLA-DPA1, HLA-DRB1, HLA-DRB5, MUC5B, and CD274) were identified in the validation dataset.

Conclusions

Our findings suggest that hub genes play important roles in the development of AR.