Three genes, IRF1, GBP5, and PARP9, may play important roles in (IBD).
IRF1, GBP5, and PARP9 were identified as potential genes linked to IBD.
IRF1 showed a significant association with the risk of developing IBD.
The presence of specific biomarkers was connected to immune cell infiltration in IBD.
IRF1 passed statistical tests indicating it could promote IBD development.
Integrating multiple data sources may enhance understanding of IBD mechanisms.
AI simplified
BACKGROUND: The pathogenesis of (IBD) involves complex molecular mechanisms, and achieving clinical remission remains challenging. This study aims to identify IBD-potential biomarkers, analyze their correlation with immune cell infiltration, and identify genes that have a causal relationship with IBD.
METHODS: RNA-seq datasets for IBD were retrieved from GEO, stratified into discovery (GSE75214), validation (GSE36807), and independent testing cohorts (GSE179285, GSE47908). Through comparative expression profiling of the discovery cohort, IBD-associated (DEGs) were detected. Core candidate genes were subsequently prioritized using protein-protein interaction network analysis, further refined through machine learning approaches (Random Forest/Support Vector Machines). Immune cell abundance quantification and statistical correlation analyses with IBD-associated transcripts were conducted via the CIBERSORTx deconvolution algorithm. To complement these findings, blood expression quantitative trait loci (eQTL) data from GTExv8.ALL.Whole_Blood were integrated with IBD genome-wide association statistics from the FinnGen consortium. This multi-omics integration framework employed: (1) Bayesian colocalization to assess shared causal variants, (2) HEIDI heterogeneity testing, and (3) summary Mendelian randomization (SMR) for causal inference validation.
RESULTS: Three genes, IRF1, GBP5, and PARP9, demonstrated significant IBD-promoting effects. The characteristic biomarkers of IBD were associated with immune cell infiltration. SMR analysis based on eQTL data showed that IRF1 was significantly associated with the risk of IBD. Moreover, IRF1 passed the HEIDI test of > 0.05 on gene expression, and IRF1 demonstrated the ability to promote the development of IBD.
CONCLUSIONS: This integrative analysis identifies IRF1, GBP5, and PARP9 as potential genes associated with IBD pathogenesis. SMR analysis based on eQTL data revealed that IRF1 was significantly associated with the risk of IBD. The relationship between IRF1 and IBD risk highlights its potential as both a therapeutic target and diagnostic biomarker.
Key numbers
0.855
Score of Optimal Model
Average score from machine learning analysis across training and testing cohorts.
1816
Identified
Total identified with logFC > 0.585 and FDR < 0.05.
1.5029
Significant Association of IRF1 with Risk
b_SMR value from indicating IRF1's association with .
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