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整合多种机器学习和孟德尔随机化方法揭示LTF基因作为非特异性眼眶炎症的预后生物标志物。

Integrated multiple machine learning and Mendelian randomization reveal LTF gene as a prognostic biomarker for nonspecific orbital inflammation.

作者信息

Wu Zixuan, Xu Jinfeng, Gao Yuan, Tan Kang, Yao Xiaolei, Peng Qinghua

机构信息

Hunan University of Chinese Medicine, Changsha, Hunan Province, 410208, China.

Dongying People's Hospital (Dongying Hospital of Shandong Provincial Hospital Group), Dongying, Shandong, 257091, PR China.

出版信息

BMC Pharmacol Toxicol. 2025 Aug 4;26(1):143. doi: 10.1186/s40360-025-00980-6.

DOI:10.1186/s40360-025-00980-6
PMID:40760708
Abstract

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.

摘要

背景

非特异性眼眶炎症(NSOI),也称为特发性眼眶炎症,是一组异质性的免疫介导疾病,影响眼眶组织,其共同特点是病因不明。乳铁蛋白(LTF)是一种铁结合糖蛋白,通过螯合微生物生长所必需的铁发挥强大的抗菌活性,并具有广谱抗菌、抗病毒和抗真菌特性。

方法

通过对来自GEO数据库的数据集GSE58331和GSE105149中的常见差异表达基因(DEG)与ImmPort数据库中的免疫相关基因列表进行交叉分析,使用多种机器学习和WGCNA分析来鉴定LTF。使用与LTF共表达的基因集进行基因集富集分析(GSEA)和基因集变异分析(GSVA)。为了进一步研究LTF与免疫相关生物学过程之间的相关性,采用CIBERSORT算法和ESTIMATE方法评估每个样本的免疫微环境特征。随后使用GSE105149验证LTF的表达水平。

结果

套索和支持向量机递归特征消除(SVM-RFE)算法确定了28个核心基因。富集分析表明,与LTF呈正相关的基因集在免疫相关途径中富集。对LTF进行生物学功能分析时,强调了视网膜稳态、苦味的感觉感知和组织稳态。免疫浸润分析表明,浆细胞和幼稚B细胞与LTF呈正相关(即,当LTF表达水平增加时,这些免疫细胞也相应增加),而记忆B细胞、M2巨噬细胞、静息肥大细胞、单核细胞、活化NK细胞、调节性T细胞(Tregs)与LTF呈负相关。LTF在鉴别NSOI方面显示出显著的诊断潜力。

结论

本研究确定LTF为与NSOI相关的潜在生物标志物,为其发病机制提供了见解,并为追踪疾病进展提供了新途径。

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Cancer Sci. 2025 Jun;116(6):1522-1536. doi: 10.1111/cas.70049. Epub 2025 Mar 17.
2
METTL16-SENP3-LTF axis confers ferroptosis resistance and facilitates tumorigenesis in hepatocellular carcinoma.METTL16-SENP3-LTF 轴赋予肝癌细胞对铁死亡的抗性并促进肿瘤发生。
J Hematol Oncol. 2024 Sep 2;17(1):78. doi: 10.1186/s13045-024-01599-6.
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LTF as a Potential Prognostic and Immunological Biomarker in Glioblastoma.
LTF作为胶质母细胞瘤潜在的预后和免疫生物标志物
Biochem Genet. 2024 May 19. doi: 10.1007/s10528-024-10716-6.
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Nonspecific Orbital Inflammation (NSOI): Unraveling the Molecular Pathogenesis, Diagnostic Modalities, and Therapeutic Interventions.非特异性眼眶炎症(NSOI):解析分子发病机制、诊断方式和治疗干预。
Int J Mol Sci. 2024 Jan 26;25(3):1553. doi: 10.3390/ijms25031553.
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Glutamine metabolism-related genes and immunotherapy in nonspecific orbital inflammation were validated using bioinformatics and machine learning.运用生物信息学和机器学习验证了与非特异性眼眶炎症的谷氨酰胺代谢相关的基因和免疫治疗。
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