ArticleMedicine2026
Predicting diagnostic gene biomarkers in allergic asthma.
Article in Medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
backgroundAllergic 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.
methodsTwo 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.
resultsWe 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.
conclusionMBOAT1, 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.