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A Component-resolved Diagnostic Approach for a Study on Grass Pollen Allergens in Chinese Southerners with Allergic Rhinitis and/or Asthma
Published on: June 4, 2017
Predicting diagnostic gene biomarkers in allergic asthma
Weihua Liu1,2, Shuanglan Xu3, Quan He4
1Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital, Nanjing Medical University, Nanjing, Jiangsu, China.
Background:
Allergic asthma (AA) is a heterogeneous chronic inflammatory airway disorder. In this study, we performed a retrospective bioinformatics analysis based on public transcriptome datasets to identify critical genes associated with immune cell infiltration in AA and to establish a novel predictive model.
Methods:
Two transcriptome datasets (GSE73482 and GSE40889) were analyzed to explore key genes implicated in AA. Functional enrichment analyses, including Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses, were performed using Metascape. Least absolute shrinkage and selection operator regression was applied to screen feature genes and construct a diagnostic prediction model. Weighted gene co-expression network analysis (WGCNA) was conducted to identify AA-related gene modules. The fractions of infiltrating immune cells were estimated using single-sample gene set enrichment analysis (ssGSEA). Gene set variation analysis and gene set enrichment analysis (GSEA) were performed to explore the biological functions and related signaling pathways of the key genes. The Cistrome Data Browser database was used to predict transcription factors that potentially regulate these key genes.
Results:
We identified 4 highly significant genes in the brown module: membrane associated O acetyltransferase 1 (MBOAT1), leucine rich repeats and immunoglobulin-like domains 1 (LRIG1), LOC401357, and G protein regulated inducer of neurite outgrowth 3 (GPRIN3). GSEA results revealed that these key genes were significantly enriched in multiple immune-related signaling pathways. To further explore the regulatory network of these genes, transcription factors were predicted using the Cistrome Data Browser database, and the regulatory network was visualized using Cytoscape software.
Conclusion:
MBOAT1, LRIG1, LOC401357, and GPRIN3 are candidate AA-associated genes identified through retrospective modeling. The identification of these genes offers potential opportunities to utilize them as biomarkers and targets for immunotherapy in AA.
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