Peripheral Blood Immunogenomic Cytokine-receptor Signature (CXCR1, IL11RA, IL13RA2, CD19) Predicts HBV-related Cirrhosis: Public PBMC Transcriptome Mining and Nomogram Development
Abstract
Progression from chronic hepatitis B (CHB) to cirrhosis is closely associated with immune dysregulation and altered cytokine signaling, yet effective noninvasive immune biomarkers remain limited. This study aimed to develop a peripheral blood mononuclear cell (PBMC)-based cytokine gene signature for predicting HBV-related cirrhosis.
The GSE114783 microarray dataset was analyzed to identify differentially expressed genes between CHB and cirrhosis. Immune-related candidate genes were selected by integrating curated immune resources and the Kyoto Encyclopedia of Genes and Genomes cytokine-cytokine receptor interaction pathway. Least absolute shrinkage and selection operator regression and multivariable logistic regression were used to construct the predictive model. Receiver operating characteristic analysis, leave-one-out cross-validation, and nomogram development were performed to evaluate model performance and support clinical translation.
Differential expression analysis identified 3169 genes distinguishing cirrhosis from CHB, from which 86 immune/cytokine-related genes were prioritized. LASSO selected a parsimonious 4-gene signature-CXCR1, IL11RA, IL13RA2, and CD19-capturing key immune axes relevant to chronic inflammation and fibrogenesis (chemokine receptor signaling, IL-11/IL-13 receptor pathways, and B-cell–associated immunity). The resulting model achieved an area under the curve (AUC) of 0.935 (95% CI, 0.882-0.988); at an optimal cutoff of 0.832, sensitivity was 88.6% and specificity 84.3%. LOOCV supported robust performance, and the nomogram demonstrated good agreement between predicted and observed risk.
A PBMC-based immune cytokine-receptor gene signature (CXCR1/IL11RA/IL13RA2/CD19) provides a noninvasive tool for immunologically informed risk stratification of HBV-related cirrhosis and may support immune monitoring and early intervention strategies. Prospective, multicohort validation and mechanistic studies are warranted.
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| Keywords | ||
| B cells Chronic hepatitis B Chemokine signaling Cytokine receptors Immunogenomics LASSO Liver cirrhosis Machine learning Nomogram PBMC | ||
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