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用于预测培养转化的TB27转录组模型

The TB27 Transcriptomic Model for Predicting Culture Conversion.

作者信息

Reimann Maja, Avsar Korkut, DiNardo Andrew R, Goldmann Torsten, Günther Gunar, Hoelscher Michael, Ibraim Elmira, Kalsdorf Barbara, Kaufmann Stefan H E, Köhler Niklas, Mandalakas Anna M, Maurer Florian P, Müller Marius, Nitschkowski Dörte, Olaru Ioana D, Popa Cristina, Rachow Andrea, Rolling Thierry, Salzer Helmut J F, Sanchez-Carballo Patricia, Schuhmann Maren, Schaub Dagmar, Spinu Victor, Terhalle Elena, Unnewehr Markus, Zielinski Nika J, Heyckendorf Jan, Lange Christoph

机构信息

Clinical Infectious Diseases, Research Center Borstel, Borstel, Germany.

German Center for Infection Research (DZIF), Partner Site Hamburg-Lübeck-Borstel-Riems, Germany.

出版信息

Pathog Immun. 2025 Jan 29;10(1):120-139. doi: 10.20411/pai.v10i1.770. eCollection 2024.

Abstract

RATIONALE

Treatment monitoring of tuberculosis patients is complicated by a slow growth rate of . Recently, host RNA signatures have been used to monitor the response to tuberculosis treatment.

OBJECTIVE

Identifying and validating a whole blood-based RNA signature model to predict microbiological treatment responses in patients on tuberculosis therapy.

METHODS

Using a multi-step machine learning algorithm to identify an RNA-based algorithm to predict the remaining time to culture conversion at flexible time points during anti-tuberculosis therapy.

RESULTS

The identification cohort included 149 patients split into a training and a test cohort, to develop a multistep algorithm consisting of 27 genes (TB27) for predicting the remaining time to culture conversion (TCC) at any given time. In the test dataset, predicted TCC and observed TCC achieved a correlation coefficient of =0.98. An external validation cohort of 34 patients shows a correlation between predicted and observed days to TCC also of =0.98.

CONCLUSION

We identified and validated a whole blood-based RNA signature (TB27) that demonstrates an excellent agreement between predicted and observed times to culture conversion during tuberculosis therapy. TB27 is a potential useful biomarker for anti-tuberculosis drug development and for prediction of treatment responses in clinical practice.

摘要

原理

结核分枝杆菌生长缓慢,这使得结核病患者的治疗监测变得复杂。最近,宿主RNA特征已被用于监测结核病治疗的反应。

目的

识别并验证一种基于全血的RNA特征模型,以预测接受结核病治疗患者的微生物学治疗反应。

方法

使用多步机器学习算法来识别一种基于RNA的算法,以预测抗结核治疗期间灵活时间点的培养转化剩余时间。

结果

识别队列包括149名患者,分为训练队列和测试队列,以开发一种由27个基因组成的多步算法(TB27),用于预测任何给定时间的培养转化剩余时间(TCC)。在测试数据集中,预测的TCC和观察到的TCC的相关系数为=0.98。34名患者的外部验证队列显示,预测的TCC天数与观察到的TCC天数之间的相关性也为=0.98。

结论

我们识别并验证了一种基于全血的RNA特征(TB27),该特征在结核病治疗期间预测的和观察到的培养转化时间之间显示出极好的一致性。TB27是抗结核药物开发和临床实践中治疗反应预测的潜在有用生物标志物。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bd20/11792529/40515325b46f/pai-10-120-g001.jpg

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