BMC pharmacology & toxicology · 2025
Integrated multiple machine learning and Mendelian randomization reveal LTF gene as a prognostic biomarker for nonspecific orbital inflammation.
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Wu Zixuan, Xu Jinfeng, Gao Yuan, Tan Kang, Yao Xiaolei, Peng Qinghua
Аннотация
BACKGROUND: Nonspecific orbital inflammation (NSOI), also known as idiopathic orbital inflammation, comprises a heterogeneous group of immune-mediated disorders affecting orbital tissues, unified by the absence of a defined etiology. Lactotransferrin (LTF), an iron-binding glycoprotein, exerts potent antimicrobial activity by sequestering iron essential for microbial growth, and demonstrates broad-spectrum antibacterial, antiviral, and antifungal properties.
METHODS: LTF was identified through the intersection analysis of common DEGs from datasets GSE58331 and GSE105149 from the GEO database, alongside immune-related gene lists from the ImmPort database, using Multiple Machine learning and WGCNA analysis. GSEA and GSVA were conducted with gene sets co-expressed with LTF. To further investigate the correlation between LTF and immune-related biological processes, the CIBERSORT algorithm and ESTIMATE method were employed to evaluate immune microenvironment characteristics of each sample. The expression levels of LTF were subsequently validated using GSE105149.
RESULTS: Lasso and SVM-RFE algorithms pinpointed 28 hub genes. Enrichment analysis revealed that gene sets positively correlated with LTF were enriched in immune-related pathways. For biological function analysis in LTF, retina homeostasis, sensory perception of bitter taste, and tissue homeostasis were emphasized. Immune infiltration analysis indicated that Plasma cells and B cells naive were positively associated (That is, when the expression level of LTF increases, these immune cells also increase accordingly) with LTF, whereas B cells memory, Macrophages M2, Mast cells resting, Monocytes, NK cells activated, T cells regulatory (Tregs) were negatively associated with LTF. LTF demonstrated significant diagnostic potential in differentiating NSOI.
CONCLUSIONS: This study identifies LTF as a potential biomarker linked to NSOI, providing insights into its pathogenesis and offering new avenues for tracking disease progression.
METHODS: LTF was identified through the intersection analysis of common DEGs from datasets GSE58331 and GSE105149 from the GEO database, alongside immune-related gene lists from the ImmPort database, using Multiple Machine learning and WGCNA analysis. GSEA and GSVA were conducted with gene sets co-expressed with LTF. To further investigate the correlation between LTF and immune-related biological processes, the CIBERSORT algorithm and ESTIMATE method were employed to evaluate immune microenvironment characteristics of each sample. The expression levels of LTF were subsequently validated using GSE105149.
RESULTS: Lasso and SVM-RFE algorithms pinpointed 28 hub genes. Enrichment analysis revealed that gene sets positively correlated with LTF were enriched in immune-related pathways. For biological function analysis in LTF, retina homeostasis, sensory perception of bitter taste, and tissue homeostasis were emphasized. Immune infiltration analysis indicated that Plasma cells and B cells naive were positively associated (That is, when the expression level of LTF increases, these immune cells also increase accordingly) with LTF, whereas B cells memory, Macrophages M2, Mast cells resting, Monocytes, NK cells activated, T cells regulatory (Tregs) were negatively associated with LTF. LTF demonstrated significant diagnostic potential in differentiating NSOI.
CONCLUSIONS: This study identifies LTF as a potential biomarker linked to NSOI, providing insights into its pathogenesis and offering new avenues for tracking disease progression.