Genome-wide expression in human whole blood for diagnosis of latent tuberculosis infection: a multicohort research.

IF 4 2区生物学 Q2 MICROBIOLOGY

Frontiers in Microbiology Pub Date : 2025-05-09 eCollection Date: 2025-01-01 DOI:10.3389/fmicb.2025.1584360

Fan Jiang, Yanhua Liu, Linsheng Li, Ruizi Ni, Yajing An, Yufeng Li, Lingxia Zhang, Wenping Gong

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引用次数: 0

Abstract

Background: Tuberculosis (TB) remains a significant global health challenge, necessitating reliable biomarkers for differentiation between latent tuberculosis infection (LTBI) and active tuberculosis (ATB). This study aimed to identify blood-based biomarkers differentiating LTBI from ATB through multicohort analysis of public datasets.

Methods: We systematically screened 18 datasets from the NIH Gene Expression Omnibus (GEO), ultimately including 11 cohorts comprising 2,758 patients across 8 countries/regions and 13 ethnicities. Cohorts were stratified into training (8 cohorts, n = 1,933) and validation sets (3 cohorts, n = 825) based on functional assignment.

Results: Through Upset analysis, LASSO (Least Absolute Shrinkage and Selection Operator), SVM-RFE (Support Vector Machine Recursive Feature Elimination), and MCL (Markov Cluster Algorithm) clustering of protein-protein interaction networks, we identified S100A12 and S100A8 as optimal biomarkers. A Naive Bayes (NB) model incorporating these two markers demonstrated robust diagnostic performance: training set AUC: median = 0.8572 (inter-quartile range 0.8002, 0.8708), validation AUC = 0.5719 (0.51645, 0.7078), and subgroup AUC = 0.8635 (0.8212, 0.8946).

Conclusion: Our multicohort analysis established an NB-based diagnostic model utilizing S100A12/S100A8, which maintains diagnostic accuracy across diverse geographic, ethnic, and clinical variables (including HIV co-infection), highlighting its potential for clinical translation in LTBI/ATB differentiation.

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人全血全基因组表达诊断潜伏结核感染：一项多队列研究。

背景：结核病（TB）仍然是一个重大的全球健康挑战，需要可靠的生物标志物来区分潜伏性结核病感染（LTBI）和活动性结核病（ATB）。本研究旨在通过对公共数据集的多队列分析，确定区分LTBI和ATB的血液生物标志物。方法：我们系统筛选了来自NIH基因表达综合数据库（GEO）的18个数据集，最终包括11个队列，包括8个国家/地区和13个种族的2758名患者。根据功能分配，将队列分为训练组（8个队列，n = 1933）和验证组（3个队列，n = 825）。结果：通过蛋白质-蛋白质相互作用网络的Upset分析、LASSO （Least Absolute Shrinkage and Selection Operator）、SVM-RFE （Support Vector Machine Recursive Feature Elimination）和MCL （Markov聚类算法）聚类，我们确定了S100A12和S100A8为最佳生物标志物。结合这两个标记的Naive Bayes （NB）模型显示出稳健的诊断性能：训练集AUC：中位数 = 0.8572（四分位数间范围0.8002,0.8708），验证AUC = 0.5719(0.51645,0.7078)，亚组AUC = 0.8635（0.8212,0.8946）。结论：我们的多队列分析利用S100A12/S100A8建立了一个基于nb的诊断模型，该模型在不同地理、种族和临床变量（包括HIV合并感染）中保持诊断准确性，突出了其在LTBI/ATB分化中的临床转化潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Frontiers in Microbiology MICROBIOLOGY-

CiteScore

7.70

自引率

9.60%

发文量

4837

审稿时长

14 weeks

期刊介绍： Frontiers in Microbiology is a leading journal in its field, publishing rigorously peer-reviewed research across the entire spectrum of microbiology. Field Chief Editor Martin G. Klotz at Washington State University is supported by an outstanding Editorial Board of international researchers. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics, clinicians and the public worldwide.