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Updated: Aug 24, 2025
Reprograming Model of Human Monocyte-derived Macrophages for In-vitro Assays
Published on: April 18, 2025
The Construction of ITP Diagnostic Modeling Based on the Expressions of Hub Genes Associated with M1 Polarization of
Ming-Jing Wang1,2, Ying Song1,2, Xiao-Qing Guo1
1Department of Hematology, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, 100091, People's Republic of China.
Purpose:
Primary immune thrombocytopenia (ITP) is an immune disease with a diagnosis of exclusion, since no validated biomarkers have been identified. In this study, we explored biomarkers associated with the development of ITP from an immune perspective to inform the clinical diagnosis.
Patients And Methods:
Differentially expressed genes (DEGs) between normal and ITP samples were analyzed using limma package. Random forest algorithm and LASSO regression were further used to screen for DEGs associated with ITP. The expression of these hub genes was validated by PCR. The relationship between DEGs and immunity was explored by enrichment analysis. Immune cell infiltration in ITP was analyzed by CIBERSORT and ssGSEA, and the relationship between DEGs and infiltrating immune cells was analyzed by Spearman's rank correlation analysis. Finally, a diagnostic model related to DEGs was constructed by the neural network, and its efficiency was detected by the ROC curve.
Results:
After screening the GEO database and validation by PCR analysis, The expression of CTH and TAF8 were higher and while OSBP2 expression was lower in ITP patients compared to normal subjects (P<0.05). GO enrichment analysis showed that these DEGs were associated with inflammatory immune-related diseases, and KEGG analysis showed that they mainly regulated signaling pathways such as JAK-STAT. CIBERSORT and ssGSEA analyses showed that these DEGs were mainly associated with macrophage M1 polarization. The expression of CTH and TAF8 were positively correlated with M1 expression, while OSBP2 was negatively correlated with M1 expression. The ROC curve showed high accuracy of the neural network model [AUC= 0.939, 95% CI (0.8-1)].
Conclusion:
Our results suggest that CTH, TAF8, and OSBP2 can be used as effective diagnostic biomarkers of ITP.
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