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鉴定出与自噬相关的分子簇,可作为鉴别儿童活动性和潜伏性结核感染的指标。

Autophagy-related molecular clusters identified as indicators for distinguishing active and latent TB infection in pediatric patients.

机构信息

Department of Pediatric, Nanjing Lishui People's Hospital, Zhongda Hospital Lishui Branch, Southeast University, Nanjing, China.

Department of Gastroenterology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.

出版信息

BMC Pediatr. 2024 Jun 19;24(1):398. doi: 10.1186/s12887-024-04881-1.

Abstract

BACKGROUND

Autophagy is crucial for controlling the manifestation of tuberculosis. This study intends to discover autophagy-related molecular clusters as biomarkers for discriminating between latent tuberculosis (LTBI) and active tuberculosis (ATB) in children through gene expression profile analysis.

METHODS

The expression of autophagy modulators was examined in pediatric patients with LTBI and ATB utilizing public datasets from the Gene Expression Omnibus (GEO) collection (GSE39939 and GSE39940).

RESULTS

In a training dataset (GSE39939), patients with LTBI and ATB exhibited the expression of autophagy-related genes connected with their active immune responses. Two molecular clusters associated with autophagy were identified. Compared to Cluster 1, Cluster 2 was distinguished through decreased adaptive cellular immune response and enhanced inflammatory activation, according to single-sample gene set enrichment analysis (ssGSEA). Per the study of gene set variation, Cluster 2's differentially expressed genes (DEGs) played a role in synthesizing transfer RNA, DNA repair and recombination, and primary immunodeficiency. The peak variation efficiency, root mean square error, and area under the curve (AUC) (AUC = 0.950) were all lowered in random forest models. Finally, a seven-gene-dependent random forest profile was created utilizing the CD247, MAN1C1, FAM84B, HSZFP36, SLC16A10, DTX3, and SIRT4 genes, which performed well against the validation dataset GSE139940 (AUC = 0.888). The nomogram calibration and decision curves performed well in identifying ATB from LTBI.

CONCLUSIONS

In summary, according to the present investigation, autophagy and the immunopathology of TB might be correlated. Furthermore, this investigation established a compelling prediction expression profile for measuring autophagy subtype development risks, which might be employed as possible biomarkers in children to differentiate ATB from LTBI.

摘要

背景

自噬对于控制结核病的表现至关重要。本研究旨在通过基因表达谱分析,发现自噬相关分子簇作为鉴别儿童潜伏性结核病(LTBI)和活动性结核病(ATB)的生物标志物。

方法

利用基因表达综合数据库(GEO)(GSE39939 和 GSE39940)中的公共数据集,检测小儿 LTBI 和 ATB 患者中自噬调节剂的表达。

结果

在训练数据集(GSE39939)中,LTBI 和 ATB 患者的自噬相关基因表达与他们的主动免疫反应有关。鉴定出与自噬相关的两个分子簇。根据单样本基因集富集分析(ssGSEA),与簇 1 相比,簇 2 的特点是适应性细胞免疫反应降低,炎症激活增强。根据基因集变异研究,簇 2 的差异表达基因(DEGs)在合成转移 RNA、DNA 修复和重组以及原发性免疫缺陷中发挥作用。随机森林模型的峰变效率、均方根误差和曲线下面积(AUC)(AUC=0.950)均降低。最后,利用 CD247、MAN1C1、FAM84B、HSZFP36、SLC16A10、DTX3 和 SIRT4 基因创建了一个基于七基因的随机森林模型,在验证数据集 GSE139940 上表现良好(AUC=0.888)。列线图校准和决策曲线在识别 LTBI 中的 ATB 方面表现良好。

结论

综上所述,根据本研究,自噬和结核病的免疫病理学可能相关。此外,本研究建立了一个强大的预测表达谱,用于衡量自噬亚型发展风险,可作为儿童区分 ATB 和 LTBI 的可能生物标志物。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ad9b/11186109/6be1c13a5622/12887_2024_4881_Fig1_HTML.jpg

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