Background and objective: Omalizumab is a bio-targeted agent approved as add-on therapy for the treatment of severe asthma. Most patients with severe asthma show no response to omalizumab. American Thoracic Society (ATS) and European Respiratory Society (ERS) recommend blood eosinophil count and fractional exhaled nitric oxide (FeNO) as biomarkers with high value for increased response to omalizumab and periostin as a biomarker with a low value. In this study, we aimed to identify the biomarkers for predicting treatment response to omalizumab by performing whole blood transcriptional expression profiling using array and clinical data from GSE134544. Methods: We analyzed GSE134544 whole blood transcriptional and clinical data of omalizumab treatment using xCell, weighted gene co-expression network analysis (WGCNA), gene ontology enrichment analysis, KEGG pathway analysis, protein-protein interaction (PPI) network, and logistic regression analysis. Results: We calculated the immune enrichment score using xCell and found that CD4(+) T cells, CD4(+) Tem, CD4(+) memory T cells, CD8(+) Tcm, and dendritic cells (DC) were relatively higher in responders than in non-responders. Analysis of omalizumab response using WGCNA revealed that the above-mentioned significant immune cells in the red module was relevant to the sample traits; there were 547 genes in the red module. We identified 20 hub genes for the PPI network using cytoHubba, a Cytoscape plugin. Using logistic regression analysis, CD3E was found to be the only significant biomarker, and the area under the curve of ROC curves was 0.763. Conclusion: CD3E maybe a new predictive biomarker of response to omalizumab treatment in asthma patients and be used to select more suitable asthma patients for omalizumab treatment.